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Reliability of blood pressure measurement and cardiovascular risk prediction

van der Hoeven, N.V.

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Citation for published version (APA):van der Hoeven, N. V. (2016). Reliability of blood pressure measurement and cardiovascular risk prediction.

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NIELS V. VAN DER HOEVEN

UITNODIGINGVoor het bijwonen van de openbare verdediging van

het proefschrift

POLDERWEG 132, 1093KP [email protected]

Paranimfen:Paul den BraveNoach de Haas

Op vrijdag 28 oktober 201612:00 uur in de Agnietenkapel

Oudezijds Voorburgwal 231Amsterdam

CARDIOVASCULAR RISK PREDICTION

BLOOD PRESSURE MEASUREMENT

RELIABILITY OF

AND

Receptie naafloop vande promotie

Cover_NielsV_VD_HOEVEN_Boekenlegger.indd 1 11-09-16 17:03

RELIABILITY OF BLOOD PRESSURE MEASUREM

ENT AND CARDIOVASCULAR RISK PREDICTIONNIELS V. VAN DER HOEVEN



RELIABILITY OF

AND


Cover_NielsV_VD_HOEVEN_DEF.indd 1 11-09-16 13:17

Reliability of Blood Pressure Measurement and

Cardiovascular Risk Prediction

Niels V. van der Hoeven

COLOFON

Reliability of Blood Pressure Measurement and Cardiovascular Risk Prediction

Dissertation, University of Amsterdam, The Netherlands

Omslagillustratie: www.stanvanlier.nl

Layout and printing: Gildeprint

ISBN: 978-94-6233-324-6

Copyright © 2016 Niels Vincent van der Hoeven, Amsterdam, The Netherlands

Financial support for printing this thesis was kindly provided by:

University of Amsterdam, Stichting tot Steun Promovendi Vasculaire Geneeskunde, Bayer

Healthcare Pharmaceuticals, Microlife, Pfi zer, Sanofi , Servier Nederland Farma B.V.

Reliability of Blood Pressure Measurement and

Cardiovascular Risk Prediction

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad van doctor

aan de Universiteit van Amsterdam

op gezag van de Rector Magnifi cus

prof. dr. ir. K.I.J. Maex

ten overstaan van een door het College voor Promoties ingestelde commissie,

in het openbaar te verdedigen in de Agnietenkapel

op vrijdag 28 oktober 2016 te 12:00 uur

door

Niels Vincent van der Hoeven

geboren te Zaanstad

PROMOTIECOMMISSIE:

Promotor: Prof. dr. E.S.G. Stroes Universiteit van Amsterdam

Copromotores: dr. B.J.H. van den Born Universiteit van Amsterdam

dr. R.A. Kraaijenhagen NIPED

Overige leden: Prof. dr. P.M.M Bossuyt Universiteit van Amsterdam

Prof. dr. Y.M. Smulders Vrije Universiteit

Prof. dr. J.B.L. Hoekstra Universiteit van Amsterdam

Prof. dr. J.J. van Lieshout University of Nottingham/UvA

Prof. dr. A.J. Smit Rijksuniversiteit Groningen

Prof. dr. R.J.G. Peters Universiteit van Amsterdam

Faculteit der Geneeskunde

Financial support by the Dutch Heart Foundation for the publication of this thesis is gratefully

acknowledged

Voor papa en mama

TABLE OF CONTENTS

Chapter 1 General Introduction and Outline of the Thesis 9

Part I Reliability of Blood Pressure MeasurementChapter 2 Reliability of Palpation of the Radial Artery Compared with Auscultation 23

of the Brachial artery in Measuring Systolic Blood Pressure

Chapter 3 Simultaneous Compared with Sequential Blood Pressure Measurement 35

Results in Smaller Inter-arm Blood Pressure Diff erences

Chapter 4 Poor Adherence to Home Blood Pressure Measurement Schedule 49

Chapter 5 ‘Diagnostic Mode’ Improves Adherence to the Home Blood Pressure 61

Measurement Schedule

Chapter 6 Severe Hypertension Related to Caff einated Coff ee and Tranylcypromine: 73

a Case Report

Part II Blood Pressure as Predictor of Cardiovascular Risk Chapter 7 Home Blood Pressure Measurement as a Screening Tool for Hypertension 81

in a Web-based Worksite Health Promotion Program

Chapter 8 A Six Question Screen to Facilitate Primary Cardiovascular Disease 97

Prevention

Chapter 9 The Ankle Central Aortic Index is More Closely Associated with 113

Cardiovascular Risk than the Ankle Brachial Index - the HELIUS Study

Chapter 10 Mortality and Cardiovascular Risk in Patients with a History of Malignant 127

Hypertension: a Case-Control Study

Chapter 11 Summary and Perspectives 139

Addenda Nederlandse samenvatting 155

Authors and affi liations 169

Dankwoord 171

Portfolio 175

Curriculum vitae 179

1General Introduction and Outline of the Thesis

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11

1Blood pressure measurement: a brief historical overview

The history of blood pressure (BP) measurement dates back over 4000 years, when it was noted

by the Chinese Yellow Emperor Huang Ti that “when the heart pulse beats vigorously and the

strokes are markedly prolonged, the corresponding illness […] makes the patient unable to

speak”(1). This was a remarkable observation at that time, considering that William Harvey’s

discovery of the blood circulation had yet to come more than four millennia later (2). Even after

publication of Harvey’s pioneering work in 1628 it took more than 100 years before the fi rst known

directly arterial measured BP was reported by Reverend Stephen Hales in 1733, who inserted a

brass pipe into the ligated left crural artery of a horse and found that the blood rose 8 feet and

3 inches (~2.44 meters) above heart level (3). Although that was an important breakthrough

in measuring BP, his invasive technique was, obviously, not safely applicable in humans. In the

19th century, John Leonard Marie Poiseuille introduced the unit millimeters of mercury (mmHg),

which allowed the use of smaller sized columns compared to Hales method (4). Still, the invasive

technique of BP measurement limited the use of BP as a parameter in a clinical setting. In the

second half of the 19th century, several physicians were working on the development of a non-

invasive method to establish BP.

The fi rst well-known of these devices, called sphygmographs (sphygmos is the Greek

word for pulse), was developed by Karl von Vierordt. His device transmitted the oscillations of

the pulse through a lever on a piece of paper. By adding weight to the lever till the pulse was

occluded, an estimation of BP – although cumbersome and unreliable – could be made. Several

improvements and adjustments were made, by once renowned physicians such as Étienne

Jules Marey and Samuel Siegfried Karl Ritter von Basch (5), but their eff orts were surpassed by

Scipione Riva-Rocci, who introduced his sphygmomanometer with infl atable cuff in 1896 (6;7).

The introduction of his device marks an important milestone in the history of BP measurement,

because it allowed for the fi rst time easy, reliable and reproducible BP assessment which could

be used in clinical practice. However, with his technique based on palpation of the radial artery,

only systolic BP could be estimated. In 1905 the Russian military surgeon Nicolai Korotkoff

combined the sphygmomanometer of Riva-Rocci with a stethoscope, introducing the concept

of auscultatory BP measurement. Korotkoff also measured BP during defl ation (after infl ating it

fi rst), contrary to Riva-Rocci who measured BP during infl ation. However, as already noted by Von

Recklinghausen in 1901, there seems to be no relevant diff erence in BP assessed during infl ation

or during defl ation (8). One of the most important assets of Korotkoff ’s technique was that it

made the assessment of diastolic BP possible. Originally, Korotkoff distinguished four phases of

the sounds he perceived when measuring BP. A fi fth sound was identifi ed few years later (9;10).

These sounds include, in chronological order, (I) the fi rst sound, (II) compression murmurs, (III) the

second sound, (IV) muffl ing of the second sound, and (V) the disappearance of sounds. It took

until 1939 when Korotokoff ’s auscultatory technique was given widespread recognition with its

acceptance by the Joint Committee of the American Heart Association and the Cardiac Society

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of Great Britain and Ireland (12;13), and the use of fi rst and fi fth sounds is still recommended in

current guidelines to determine systolic and diastolic BP for offi ce BP measurement s (11).

BP as predictor for cardiovascular disease

Insurance data from the 1930ies onward already revealed that arterial pressure is a strong

predictor of cardiovascular and renal mortality (14). A better understanding of the relation

between BP and cardiovascular disease and mortality was provided by the Framingham Study. In

this study, a general population sample of more than 5000 subjects free of coronary heart disease

residing in Framingham, a city in Massachusetts in the United States, were followed to unravel

risk factors contributing to the development of cardiovascular disease. The study started in 1948,

when little was known about the development of cardiovascular disease, and revealed that BP

was a strong predictor of the development of stroke and coronary heart disease (15). Nowadays

it is well-established that BP is a good predictor of cardiovascular diseases and mortality (16),

and many cardiovascular prediction models currently being used incorporate BP as an important

predict or (17-21). Although BP is a good predictor of cardiovascular diseases on a population

level, the usefulness of BP for individual risk prediction is limited due to BP variability. Riva-Rocci

already recognized that many factors, both physician and patient related, could infl uence BP

measurement (7). Therefore, several recommendations apply to standardized auscultatory BP

measurement, thereby optimizing the reliability and reproducibility of these measurements.

Such recommendations include that a patient should relax for fi ve minutes before commencing

the measurement, that the cuff should be placed at heart level, that patients should not talk

during the measurement and that patients should not keep their legs cros sed (11).

Still, there are many extraneous factors which can infl uence BP and cannot all be controlled

for, even in a clinical setting. Moreover, in 1983 it was fi rst described that the presence of a

doctor during BP measurement can substantially infl uence BP itself (22). This phenomenon,

often referred to as the white coat eff ect, has been largely overcome by the introduction of

ambulatory or 24h BP measurement (ABPM)(23). This technique allows 24h recording of BP

using an automatic portable BP device in a patients natural environment, which results in a more

reliable estimate of the true BP load exerted on target organs.

ABPM also provides registration of nocturnal BP. Normally, BP drops about 10% or more at

night (“dipping”), irrespective of hypertension status. However, in some patients BP does not

drop (“non-dipping”) or even rises during the night (“reverse dipping”). Both non-dipping and

reverse dipping are independent predictors of poor cardiovascular out come (24;25). Altogether,

BP assessed by ABPM is a better predictor of cardiovascular mortality than a conventional offi ce

BP measurement (26). However, ABPM also has its disadvantages. It is cumbersome, relatively

expensive, and therefore not widely available, and a large amount of patients experience

inconvenience or pain during the measurements, especially during the night (27). An alternative

for ABPM is self or home BP measurement (HBPM) (28;29). During HBPM, subjects can measure


13

1their BP at home according to a standardized regime (30). HBPM is also void of the white coat

eff ect, shows similar reproducibility as ABPM and has a similar correlation with target organ

damage as ABPM (31-41). In addition, HBPM has found to have a positive eff ect on BP control

(42) and enables BP monitoring for extended periods of time. The main disadvantages of HBPM

compared to ABPM is that no nocturnal readings can be recorded, and that it gives little insight

into diurnal BP variation. The fi rst problem might be overcome with newly developed HBPM

devices capable of performing nocturnal readings (43). A comparison of the advantages and

disadvantages of ABPM and HBPM is shown in Table 1.

Table 1. Comparison of ambulatory blood pressure measurement (ABPM) and home blood pressure measurement (HBPM)

ABPM HBPM

Detection of white coat hypertension + +

Detection of masked hypertension + +

Assessment of nocturnal BP and dipping pattern + -¶

Assessment of diurnal BP variation + -

Assessment of morning hypertension + +

Assessment of antihypertensive drug treatment + +

Assessment of duration of drug action + +

Long-term follow-up of hypertension - +

Improvement of patients’ compliance - +

Improvement of hypertension control rate - +

Reproducibility + +

Prognostic value + +

Availability - +

Costs - +

Patient preference - +

Adapted from Stergiou & Bliziotis, 2010 (28). ¶Except for novel HBPM devices with a nocturnal measurement mode.

Outline of the Thesis

Although we have gained a great deal of knowledge on the importance of BP in light of CVD risk

prediction, and on diff erent techniques to measure BP, there are still many factors that infl uence

the reliability of BP measurement. Therefore, the fi rst part of this thesis aims to improve reliability

of BP measurement. Blood pressure reliability is examined in the doctor’s offi ce and at patient’s

home, and a case report is shown to demonstrate how BP measurement was used to diagnose

a rare, but dangerous form of hypertension. In the second part, the focus shifts to the role of

BP in cardiovascular risk prediction. In this part of the thesis, we used hospital data, data from a

worksite health program, and data from a large, multi-ethnic population study with the aim to

improve CVD risk prediction, and to simplify the use of HBPM for screening purposes.

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Part I. Reliability of blood pressure measurement

The introduction of the sphygmomanometer of Riva-Rocci in 1896 marks the beginning of the era

of clinical BP measurement. Ever since the discovery of the auscultatory technique by Korotkoff

9 years later, not much has changed in the basic principles of clinical offi ce BP measurement.

Riva-Rocci’s original palpatory technique is nowadays largely abandoned as it only allows for

systolic BP measurement. However, current evidence suggests that, especially in older subjects,

systolic BP is most important in determining cardiovascular risk. Therefore, the elegantly

simple technique by Riva-Rocci – not requiring a stethoscope - could serve as an alternative to

Korotkoff ’s technique. Interestingly, in all the years that have passed since the introduction of

both techniques, a well-documented scientifi c comparison between both methods has been

lacking. In chapter 2 we compared the reliability of determining the systolic BP of Riva-Rocci’s

palpatory technique with Korotkoff ’s auscultatory technique, still considered the clinical gold

standard of BP measurement.

When measuring BP for the fi rst time in a patient it is recommended to perform the

measurement at both arms in order to detect vascular abnormalities and prevent underestimation

of BP when measured at the arm with the lowest BP values. However, BP diff erences between

arms are also related to fl uctuations in BP resulting from temporal and other variations in BP.

There are several ways in which inter-arm BP diff erences can be assessed. The most practical

approach is to measure BP fi rst at one arm, and then at the other one. Although this sequence

can be repeated as many times as desirable, BP can also be measured simultaneously at both

arms. The latter option discards diff erences in BP caused by temporal BP variations, but requires

at least two devices, or a single device with two cuff s that records BP at the same time. In chapter

3 we therefore compared systolic BP diff erences when assessed fi rst at one arm followed by the

other (sequential measurement) with inter-arm BP diff erences assessed at both arms at the same

time (simultaneous measurement).

It is currently well-established that out-of-offi ce BP measurements are more reliable in

diagnosing hypertension, and are more accurate in predicting cardiovascular risk than offi ce

BP measurements. A commonly used way to measure BP out of the offi ce, is HBPM where

subjects are instructed to measure BP according to a standardized measurement schedule and

standardized procedures. A disadvantage of HBPM is that it depends on the patients’ adherence

to the recommended instructions. In chapter 4 we assessed the adherence of subjects who

were given standardized instructions to follow the HBPM schedule as endorsed by the European

Society on Hypertension (ESH). Subsequently, we assessed in chapter 5 whether a HBPM device

with a build-in measurement scheme could improve adherence to the ESH protocol.

Despite the many advantages of HBPM, one of the major drawbacks of self-measurement

of BP at home is that, in contrast to ABPM, it provides little insight in circadian BP patterns.

Recognizing particular circadian BP patterns may help to diagnose certain conditions that have

a profound infl uence on the normal BP pattern such as autonomic dysfunction or obstructive


15

1sleep apnea. In Chapter 6, we demonstrate a case report where a rare, but potentially dangerous

form of hypertension was diagnosed with the help of ABPM.

Part II. Blood pressure as predictor of cardiovascular risk

One of the problems of screening for hypertension is the so-called white coat eff ect, defi ned

as having a higher BP in the offi ce than at home. It is estimated that the prevalence of subjects

in the community with offi ce hypertension but normal home BP (white coat hypertension or

isolated offi ce hypertension) i s ~20-25% (44). Because HBPM is considered not to be infl uenced

by the white coat eff ect, HBPM seems a more suitable tool for hypertension screening than

offi ce BP measurement. However, currently recommended cut-off values for HBPM to diagnose

hypertension apply to a whole series of HBPM, with a minimum of 12 BP measurements. There

are currently no recommendations regarding a single or a single pair of home measurements to

establish hypertension for screening purposes. In chapter 7 we examined the possibility to use

HBPM as a screening tool to confi rm or reject the diagnosis of hypertension using data of a large

worksite health risk assessment.

Although BP is an important cardiovascular risk factor, total cardiovascular risk is the result

of a combination of several risk factors. Current European guidelines recommend the use of the

Systematic COronary Risk Estimation (SCORE), a risk equation that uses age, gender, smoking

status, systolic BP and total cholesterol (or total cholesterol/HDL ratio) to estimate the 10-year

risk of dying from cardiovascular diseases. However, using the SCORE to identify subjects at high

CVD risk in a low-risk population, such as a working population, requires many persons to be

screened, including invasive blood sampling, to identify only few subjects with increased CVD

risk. In chapter 8, we developed and validated a simple questionnaire to estimate the SCORE risk

equation with the purpose of facilitating large-scale CVD screening.

Cardiovascular events often occur in individuals without known pre-existing CVD, and are

in many cases attributable to the presence of atherosclerosis. A simple and eff ective method to

non-invasively detect atherosclerotic burden is to measure the ratio of the systolic blood pressure

(SBP) at the ankle to that of the brachial artery, also known as the ankle-brachial index (ABI).

However, due to pressure amplifi cation, SBP at the brachial artery can be substantially increased

compared to the SBP at the level of the aortic root (central aortic SBP), leading to a relatively low

ABI. As this phenomenon is mostly present in younger subjects, who are generally little aff ected

by atherosclerosis, this may reduce the accuracy of ABI as predictor for CVD. In contrast, the ratio

of the systolic ankle brachial BP compared to the central aortic SBP (ACI) could serve as a better

alternative in predicting CVD. Therefore, in chapter 9 we tested whether ACI is a better predictor

of CVD than ABI in a large, multi-ethnic population.

Usually hypertension clusters with other CVD risk factors such as obesity, dyslipidemia and

diabetes. In chapter 10 we examined whether these CVD risk factors also cluster in patients with

an extreme phenotype of hypertension and hypertension-related complications. In order to

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determine to what extent the risk of CVD is attributable to other risk factors next to hypertension

we compared cardiovascular risk profi les of patients with a history of malignant hypertension

with age, gender and ethnicity matched hypertensive and normotensive controls.


17

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19

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analysis of randomised trials. Journal of Hypertension 2004 Jun;22:S287.

(43) Stergiou GS, Nasothimiou EG, Destounis A, Poulidakis E, Evagelou I, Tzamouranis D. Assessment of the diurnal blood pressure profi le and detection of non-dippers based on home or ambulatory monitoring. Am J Hypertens 2012 Sep;25(9):974-8.

(44) Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN, et al. Recommendations for blood pressure measurement in humans and experimental animals: Part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Hypertension 2005 Jan;45(1):142-61.

PART IReliability of Blood Pressure

Measurement

2Reliability of Palpation of the Radial Artery Compared

with Auscultation of the Brachial artery in Measuring

Systolic Blood Pressure

Niels V. van der Hoeven, Bert-Jan H. van den Born, Gert A. van Montfrans

From the Depts. of Internal and Vascular Medicine, Academic Medical Center,

Amsterdam, the Netherlands

Journal of Hypertension, 2011 Jan;29(1):51-5.

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ABSTRACT

Background Systolic blood pressure (SBP) contributes more to cardiovascular disease than

diastolic blood pressure, especially in elderly persons. Palpation of the radial artery to assess

SBP – Riva-Rocci’s technique – may be an attractive alternative for auscultatory SBP in these

patients. Therefore we investigated the difference between SBP determined by palpation of

the radial artery (pSBP) and SBP assessed by auscultation of the brachial artery (aSBP).

Methods Patients were included from the waiting room of a hypertension outpatient clinic.

In each patient eight simultaneous pSBP and aSBP measurements were assessed by two

observers in the same arm. After every two readings the observers switched between pSBP

and aSBP.

Results Forty patients were included, 25 males (62.5%), mean age 55.3 years (range 24-78).

From a total of 320 measurements, mean difference between pSBP and aSBP was -5.2 mmHg

(range -12 to 26 mmHg) (P<0.01). This difference correlated significantly with body mass index

(r=0.51, P <0.01), but not with age (r=0.15, P=0.35), pulse rate (r=0.29, P=0.09) or mean SBP

(r=0.03, P=0.85). After averaging the first three comparisons, reproducibility did not improve

when increasing the number of comparisons. When correcting for the underestimation

of 6 mmHg over the first three comparisons, Riva-Rocci’s technique estimates SBP with an

acceptable accuracy.

Conclusion In clinical practice, Riva-Rocci’s palpatory technique offers an acceptable alternative

for auscultatory SBP measurement. It is recommended to take three measurements, and then

correct for the average underestimation of 6 mmHg.


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2

INTRODUCTION

In 1896, the Italian internist, pathologist, and pediatrician Scipione Riva-Rocci made palpatory

systolic blood pressure (SBP) measurement available for medical practice. Contrary to

blood pressure (BP) devices earlier used in the nineteenth century, his sphygmomanometer

with inflatable cuff provided an easy way to perform sufficiently reliable, non-invasive, SBP

measurements [1-3]. Nine years later, in 1905, the Russian surgeon Nikolai Korotkoff observed

specific sounds when compressing arterial vessels, which eventually led to the auscultatory

method for blood pressure measurement [4, 5]. Although auscultatory BP measurement is the

present standard for clinical BP measurement, palpatory BP measurement is still embedded in

current medical practice, as it is used to estimate the SBP level before commencing auscultatory

BP measurement [6].

The main disadvantage of palpatory BP measurement is that no diastolic BP (DBP) can be

obtained. However, recent studies have shown that SBP contributes more to cardiovascular

morbidity and mortality than does DBP, especially in elderly persons [7-10]. This has led some

experts to propose that only SBP should be included to define hypertension in persons older

than 50 years [11]. The simplicity of palpating the radial artery, as originally proposed by Riva-

Rocci, may be an attractive alternative for auscultatory SBP measurement in these patients.

Interestingly, a formal comparison between Riva-Rocci’s and Korotkoff’s methods has never

been reported. Therefore we investigated the difference between SBP as determined by

simultaneous palpation of the radial artery (pSBP) and the conventional auscultatory technique

(aSBP) using an occluding upper arm cuff.

METHODS

We consecutively included 40 subjects from the waiting room of a hypertension outpatient

clinic. Patients were selected as young (<60 year) or old (≥60 year) and normotensive (SBP

<140 mmHg) or hypertensive (SBP ≥140 mmHg), with 10 subjects in each category. Patients

aged< 18 years, pregnant women and patients with atrial fibrillation were excluded.

Measurements were taken after five minutes rest while seated. The wrist was positioned

at heart level, with the arm with outstretched elbow resting on a table. In each patient, SBP

was assessed at the non-dominant arm by eight simultaneous measurements of palpatory

and auscultatory SBP by two trained observers. For both measurement techniques the same

mercury sphygmomanometer was used. The order of measurement type was randomized

between observers. The aSBP measurements were conducted as described by the ESH-guideline

for conventional BP measurement [6], based on Korotkoff’s technique using a stethoscope and

an appropriately sized cuff. The simultaneously conducted pSBP measurements resembled

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the technique as originally described by Riva-Rocci [3]. First, the radial artery was palpated

with the index and middle finger, until clear pulsations were felt. After inflating the cuff 30

mmHg above the pressure at which the pulse disappeared, it was slowly deflated with a rate of

approximately 2 mmHg per estimated pulse beat. The manometer reading coinciding with the

palpation of the first beat of a series of a least two beats was considered as the pSBP. DBP was

recorded by the observer performing aSBP measurements in the usual way in order not to bias

the observer conducting pSBP measurements. Observers switched between measurement

types after every two readings, until a total of eight readings was performed. Because deflation

of the cuff with one hand possibly interferes with palpation of the radial artery with the

contralateral hand, the cuff was always inflated by the observer performing the aSBP. To classify

patients into BP categories, one auscultatory measurement was taken before enrollment. This

measurement was omitted from further analysis. Both observers performed four pSBP, and

four aSBP measurements per patient, totaling 16 measurements per patient. Observers noted

their readings after each measurement, and did not communicate their readings.

Statistical analysis

All continuous values were expressed as mean, standard deviation (SD), and range. For the

mean difference between all pSBP and aSBP measurements SD, 95% limits of agreement and

range were calculated. Differences between pSBP and aSBP were classified into BP categories

of ≤5 mmHg, ≤10 mmHg, ≤15 mmHg and >15 mmHg respectively, according to the ESH

International Protocol for validation of blood pressure measuring devices in adults [12]. All

single pSBP and aSBP measurements were compared using a paired t-test. Difference in pSBP

and aSBP values between observers was compared using an independent samples t test.

Regression analysis was used to assess the association between the difference between pSBP

and aSBP and body mass index (BMI), age, pulse rate, and mean SBP. One-Way ANOVA was

used to compare the mean difference in pSBP and aSBP between all groups. To estimate the

variability of intra-individual differences of pSBP and aSBP measurements, we averaged the

first comparisons, then all first two comparisons, then all first three comparisons up to all eight

comparisons. The number of comparisons at which the standard deviation of the difference

(SDD) did not improve further when adding more comparisons was considered as a clinically

useful number of comparisons. For this number of measurements difference in pSBP and aSBP

was also calculated and classified into BP categories. A two-sided P value of less than 0.05 was

considered statistically significant. Statistical analysis was performed using SPSS 15.0 (SPSS

Inc., Chicago, Illinois, USA).


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RESULTS

Forty patients were included of which the characteristics are shown in Table 1. The mean

difference between pSBP (137.1 mmHg, range 98-226 mmHg) and aSBP (142.3 mmHg, range

106-234 mmHg) was -5.2 ± 5.9 mmHg (95% limits of agreement -16.7 to 6.4 mmHg) from all 320

comparisons (P<0.01). When considering all available comparisons as separate data points, the

number of comparisons did not materially alter the SDD (reproducibility). However, when the

mean differences of consecutive readings were calculated per patient, the SDD was reduced

from 6.6 to 4.0 mmHg for the first three comparisons; adding further comparisons up to all

eight did not seem to further increase reproducibility (Figure 1).

Table 1. Patient characteristics (n=40)

Age (range) 55.3 ± 16.0 (24-78)

SBP (range) 142.3 ± 25.4 (106-234)

Men (%) 25 (63%)

BMI (range) 26.5 ± 4.1 (20.0-35.5)

Pulse (range) 73.8 ± 14.2 (48-120)

Diabetes (%) 4 (10%)

Smoking (%) 3 (8%)

CVD (%) 6 (15%)

All continues values are expressed as mean with standard deviation. Values between brackets depict range. SBP is in mmHg. BMI is in kg/m2. Pulse is in beats/minute. CVD, coronary and/or vascular disease(s).

Figure 1. Number of comparisons between Riva-Rocci’s and Korotkoff’s methods of blood pressure measurement, their differences, and standard deviation of the differences in relation to the number of comparisons in 40 subjects.

pSBP = systolic blood pressure assessed by palpation, aSBP = systolic blood pressure assessed by auscultation.

When comparing the first three comparisons, the difference between pSBP (138.5 mmHg,

range 104-226 mmHg) and aSBP (144.2 mmHg, range 110-234 mmHg) was -5.7 ± 5.8 mmHg

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28

(95% limits of agreement -17.0 to 5.6 mmHg). The differences between pSBP and aSBP

classified into BP categories for all and the first three readings are shown in Table 2. These

numbers were also calculated when adjusted for the average underestimation, for clinical

utility rounded to the nearest whole digit, of 5 and 6 mmHg for all eight and the first three

comparisons respectively. Of all first three readings, 35.8% were <130 mmHg, 36.7% were

130-160 mmHg, and 27.5% were >160 mmHg. Individual SBP measurements of the first three

readings for all patients are presented in a Bland-Altman plot (see Figure 2). Of the 40 patients,

29 had a difference of 5 mmHg or less in at least two of the first three comparisons. One patient

had a differences of more than 5 mmHg in all first three comparisons.

Table 2. Classification of mean differences between pSBP and aSBP according to the international protocol for all and the first three comparisons including adjustment for the average underestimation.

≤5 mmHg ≤10 mmHg ≤15 mmHg >15 mmHg

pSBP-aSBP (all comparisons) 53.4% 84.4% 94.1% 5.9%

Adjusted for underestimation of fi ve mmHg

78.1% 91.9% 97.5% 2.5%

pSBP-aSBP (fi rst three comparisons) 54.2% 83.3% 90.8% 9.2%

Adjusted for underestimation of six mmHg

63.3% 95.0% 97.5% 2.5%

The upper row shows the percentage all readings (n=320) of which the difference between the pSBP and the aSBP is ≤5 mmHg, ≤10 mmHg, ≤15 mmHg, or >15 mmHg respectively. The second row shows figures when adjusting all measurements for the average underestimation of five mmHg. The third and last row show these figures for the first three measurements (n=120), and the first three measurements adjusted for the average underestimation of six mmHg respectively. pSBP, systolic blood pressure assessed by palpation, aSBP, systolic blood pressure assessed by auscultation.

Figure 2. Comparison of the first three SBP measurements assessed by palpation and the first three SBP measurements assessed by auscultation for all patients (n=120)

On the horizontal axis of the Bland-Altman plot the mean palpatory SBP (pSBP) and auscultatory SBP (aSBP) measurement is shown. The vertical axis shows the difference between pSBP and a SBP. The solid line represents the mean at -5.7 mmHg. The dashed lines represent 1.96 times the standard deviation above and below the mean at 5.6 mmHg and -17.0 mmHg respectively.


29

2

There was no significant difference between the pSBP values obtained by each of the observers

(P=0.60) and, similarly, there was no significant difference between aSBP values (P=0.63). The

difference between all the pSBP and aSBP correlated significantly with BMI (r=0.51, P <0.01),

but not with age (r=0.15, P=0.35), pulse rate (r=0.29, P=0.09) or mean SBP (r=0.03, P=0.85).

When dividing patients in high and low BMI by a median split at BMI 26.5 kg/m2, the patients

with a high BMI had a mean difference in pSBP and aSBP of -6.7 ± 2.9 mmHg, compared with a

difference of -3.8 ± 3.0 mmHg for patients with a low BMI (P=0.01). Table 3 shows the differences

in SBP comparing pSBP with aSBP for all four groups of patients. An ANOVA revealed no

significant difference in pSBP and aSBP between the four groups (P=0.76).

Table 3. Difference between pSBP and aSBP for all groups

Young/Low Young/High Old/Low Old/High

N 10 10 10 10

Age 33.8 ± 9.2 (24-49) 51.2 ± 6.8 (39-59) 67 ± 6.3 (61-78) 69 ± 6.3 (60-76)

pSBP 113 ± 4.7(98-130) 161 ± 24.8 (132-226) 121 ± 7.9 (104-142) 153 ± 14.4 (126-190)

aSBP 119 ± 7.0 (106-138) 166 ± 26.2 (138-234) 127 ± 7.3 (106-148) 158 ± 14.2 (136-200)

pSBP-aSBP -6.0 ± 3.9 (-26 to 12) -5.3 ± 3.1 (-22 to 10) -5.2 ± 2.8 (-20 to 8) -4.2 ± 2.9 (-16 to 10)

All values are expressed as mean with standard deviation. Values within brackets depict range. SBP values are in mmHg. Young = age <60 year, Old = age ≥60 year, Low = SBP <140 mmHg, High = SBP ≥140 mmHg, pSBP, systolic blood pressure assessed by palpation, aSBP, systolic blood pressure assessed by auscultation (see text for further description of pSBP and aSBP).

DISCUSSION

When measuring the SBP using the palpatory technique, the SBP is underestimated by 5 to 6

mmHg compared with the SBP measurements obtained by auscultation, irrespective of blood

pressure and age. By taking three Riva-Rocci readings and adding 6 mmHg, we found that SBP

can be measured with the palpatory method with an acceptable accuracy.

The introduction of palpatory BP measurement as described by Riva-Rocci 114 years

ago was followed within a decade by the discovery of the auscultatory method for BP

measurement. It did take however, more than three decades until Korotkoff’s BP measurement

was given world-wide recognition with its acceptation by the Joint Committee of the American

Heart Association and the Cardiac Society of Great Britain and Ireland in 1939 [13]. Albeit the

importance of SBP in predicting cardiovascular disease in middle-aged and older patient was

already demonstrated in the Framingham study in 1971 [14], hypertension diagnosis and

treatment in the last half of previous century was mainly based on DBP. Only in the last decade

the focus has gradually shifted towards SBP [11, 15, 16]. The longstanding focus on DBP is

perhaps one of the main reasons why there has never been much interest in reexamining the

palpatory BP technique, which does not allow assessment of DBP.

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Although Korotkoff already observed that with his auscultatory technique SBP was

approximately 10-12 mmHg higher compared to palpatory SBP measurements [4, 5], a formal

comparison on the difference between auscultatory and palpatory SBP was never reported.

A previous study showed that auscultatory SBP measurement correlated only slightly better

with intra-arterial SBP measurement than palpatory SBP measurement [17]. The same study

also showed that the palpatory SBP measurement mildly underestimated auscultatory SBP

measurement, which is comparable with the results of the current study. However, in that

study auscultatory and palpatory SBP measurements were performed sequentially and

not simultaneously as in the present study. In another more recent study, auscultatory SBP

measurement was compared with return to flow at the finger, as measured with the Finapres

system (TNO model 5, BMI-TNO, Amsterdam, the Netherlands) [18]. The authors reported that

the Finapres underestimated SBP with a mean of 1.85 mmHg compared with auscultatory

BP measurement. However, this difference was not significant and not considered clinically

relevant. Nonetheless, as the authors suggest, the human finger is presumably less sensitive

than the Finapres, which is supported by the fact that in the present study the mean difference

was larger and significant.

The mean difference in SBP between the two types of measurement over eight paired

readings was fairly consistent across all age groups and SBP levels. In general it takes two to

three heart beats before an auscultated SBP can be palpated. Possibly the cuff is still too tight

when hearing the first Korotkoff sounds for the arterial pulse wave to be propagated with an

amplitude strong enough to be felt by the observers. As the differences between both SBP

measurements show some outliers both in the positive and negative direction (see figure 1)

this cannot explain all the variance in the differences between pSBP and aSBP. In some cases

a radial pulse wave was felt before the first Korotkoff sound was heard. Interestingly, Verwij et

al. [18], also noted that in some patients a finger pulse was present before the first Korotkoff

sounds were heard. This suggests that sometimes a pulse wave can pass the cuff before the

first Korotkoff sound is audible.

Another interesting finding was the significant positive correlation between BMI and

the difference between pSBP and aSBP. Despite the finding that wrist diameter is in general

little affected by obesity [19], this does not exclude the possibility that accumulation of

subcutaneous fat around the radial artery may make it harder to feel a pulse and thereby

hamper palpatory SBP measurement. This is supported by the larger difference in pSBP and

aSBP found in patients with a BMI of more than 26.5 kg/m2.

Riva-Rocci originally proposed to measure the SBP during inflation of the cuff, taking the

point at which the pulsations disappear as the SBP [3]. For two reasons we decided to measure

the pSBP during deflation rather than inflation. First, as conventional SBP measurement is

performed during deflation, for simultaneous SBP measurements it was necessary to measure

the palpatory SBP during deflation as well. Second, current design of balloon and valve does


31

2

not allow smooth, gradual and slow inflation needed to take proper SBP readings. It seems

that there should not be a relevant difference in pSBP whether determined during inflation or

deflation of the cuff, as Von Recklinhausen already noted in 1901 [20].

In the current guidelines of the European Society on Hypertension (ESH) on BP measuring,

the palpatory technique is still used to estimate the SBP prior to the auscultatory BP

measurement [6]. The guidelines recommend palpating the brachial artery during inflation,

and to inflate the cuff to about 30 mmHg above the point at which the pulse disappears and

then slowly deflate the cuff. The present study supports this recommendation as the largest

difference we found between a single pSBP and aSBP measurement was 26 mmHg. It must

be noted however, that the guidelines recommend palpating the brachial artery, to locate its

position for auscultation, where in the present study we have palpated the radial artery.

Considering clinical practice we propose that SBP measurement offers an acceptable

alternative for auscultatory SBP measurement. When taking three readings and correcting for

the underestimation of 6 mmHg, over 90% of the established palpatory SBP values differed 10

mmHg or less compared with auscultatory SBP in our study group.

When applying the palpatory method to measure the SBP, by definition the DBP, and thus

the pulse pressure - a risk factor for coronary heart disease independent of SBP [21] - cannot

be obtained. Practice will dictate those situations when palpatory BP measurement is not the

logical approach, such as in young patients, at initial assessments, and in patients with coronary

involvement. Likewise, palpatory BP measurement will be appropriate when auscultatory BP is

hard to establish, such as pregnancy and patients in shock [22].

The current study has of course some limitations. First, the study population was relatively

small and homogeneous, as all patients were included from a hypertension clinic. It would

be interesting to examine the palpatory SBP technique in patients were auscultatory SBP is

hard to establish, and in particular to assess whether the proposed correction of 6 mmHg

remains valid in these patients. Finally, although the observers were blind to the readings that

were written down, they could have given each other unintended cues when hearing the first

Korotkoff sound or feeling the pulsation. However, as both observers were focused on the

mercury manometer, this seems unlikely.

In conclusion, the palpatory technique described by Riva-Rocci offers an acceptable

alternative for auscultatory SBP measurement. As a rule of thumb, we propose to take three

measurements and then add 6 mmHg to the mean palpated SBP. Thus, Riva-Rocci’s technique

should not be put at rest, but deserves to live on as a simple, cheap and always available tool.

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REFERENCES

1. Riva-Rocci S. Un nuovo sfigmomanometro. Gazzetta Medica Di Torino 1896; 47:981–1017.

2. Roguin A. Scipione Riva-Rocci and the men behind the mercury sphygmomanometer. Int J Clin Pract 2006; 60(1):73-79.

3. Zanchetti A, Mancia G. The centenary of blood pressure measurement: a tribute to Scipione Riva-Rocci. J Hypertens 1996; 14(1):1-12.

4. Korotkov NS. Concerning the problem of the methods of blood pressure measurement. J Hypertens 2005; 23(1):5.

5. Paskalev D, Kircheva A, Krivoshiev S. A centenary of auscultatory blood pressure measurement: a tribute to Nikolai Korotkoff. Kidney Blood Press Res 2005; 28(4):259-263.

6. O’Brien E, Asmar R, Beilin L, Imai Y, Mallion JM, Mancia G, et al. European Society of Hypertension recommendations for conventional, ambulatory and home blood pressure measurement. J Hypertens 2003; 21(5):821-848.

7. Lawes CM, Bennett DA, Parag V, Woodward M, Whitlock G, Lam TH, et al. Blood pressure indices and cardiovascular disease in the Asia Pacific region: a pooled analysis. Hypertension 2003; 42(1):69-75.

8. Lawes CM, Vander Hoorn S, Rodgers A. Global burden of blood-pressure-related disease, 2001. Lancet 2008; 371(9623):1513-1518.

9. Lewington S, Clarke R, Qizilbash N, Peto R, Collins R. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 2002; 360(9349):1903-1913.

10. Franklin SS, Jacobs MJ, Wong ND, L’Italien GJ, Lapuerta P. Predominance of isolated systolic hypertension among middle-aged and elderly US hypertensives: analysis based on National Health and Nutrition Examination Survey (NHANES) III. Hypertension 2001; 37(3):869-874.

11. Williams B, Lindholm LH, Sever P. Systolic pressure is all that matters. Lancet 2008; 371(9631):2219-2221.

12. O’Brien E, Pickering T, Asmar R, Myers M, Parati G, Staessen J, et al. Working Group on Blood Pressure Monitoring of the European Society of Hypertension International Protocol for validation of blood pressure measuring devices in adults. Blood Press Monit 2002; 7(1):3-17.

13. Nabokov AV, Nevorotin AJ. Dr N. S. Korotkov: the low-pitch sounds that stand high. Nephrol Dial Transplant 1998; 13(4):1041-1043.

14. Kannel WB, Gordon T, Schwartz MJ. Systolic versus diastolic blood pressure and risk of coronary heart disease. The Framingham study. Am J Cardiol 1971; 27(4):335-346.

15. Black HR. The paradigm has shifted to systolic blood pressure. J Hum Hypertens 2004; 18 Suppl 2:S3-S7.

16. Schillaci G, Pirro M, Mannarino E. Assessing cardiovascular risk: should we discard diastolic blood pressure? Circulation 2009; 119(2):210-212.

17. Van Bergen FH, Weatherhead DS, Treloar AE Dobkin AB, Buckley JJ. Comparison of indirect and direct methods of measuring arterial blood pressure. Circulation 1954; 10(4):481-490.

18. Verrij E, van Montfrans, G, Bos JW. Reintroduction of Riva-Rocci measurements to determine systolic blood pressure? Neth J Med 2008; 66(11):480-482.

19. Pickering TG. Measurement of blood pressure in and out of the office. J Clin Hypertens (Greenwich) 2005; 7(2):123-129.

20. Von Recklinghausen H. Ueber Blutdrukmessung beim Menschen. Arch Exper Path u Pharmakol 1901; 46:78-132.

21. Franklin SS, Khan SA, Wong ND, Larson MG, Levy D. Is pulse pressure useful in predicting risk for coronary heart Disease? The Framingham heart study. Circulation 1999; 100(4):354-360.


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22. Beevers G, Lip GY, O’Brien E. ABC of hypertension: Blood pressure measurement. Part II-conventional sphygmomanometry: technique of auscultatory blood pressure measurement. BMJ 2001; 322(7293):1043-1047.

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3Simultaneous Compared with Sequential Blood Pressure

Measurement Results in Smaller Inter-arm Blood Pressure

Diff erences

Niels V. van der Hoeven, MD1; Sophie Lodestijn1; Stephanie Nanninga1;

Gert A. van Montfrans, MD, PhD1; and Bert-Jan H. van den Born, MD, PhD1.

1Departments of Internal and Vascular Medicine, Academic Medical Center of the

University of Amsterdam, the Netherlands.

Journal of Clinical Hypertension 2013 Nov;15(11):839-44

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ABSTRACT

There are currently few recommendations on how to assess inter-arm blood pressure (BP)

differences. We compared simultaneous with sequential measurement on mean BP, inter-arm

BP differences, and within-visit reproducibility in 240 subjects stratified according to age (<50

or ≥60) and BP (<140/90 or ≥140/90 mmHg). Three simultaneous and three sequential BP

measurements were taken in each subject. Starting measurement type and starting arm for

sequential measurements were randomized. Mean BP and inter-arm BP differences of the first

pair and reproducibility of inter-arm BP differences of the first and second pair were compared

between both methods. Mean systolic BP was 1.3±7.5 mmHg lower during sequential compared

to simultaneous measurement (P<0.01). However, the first sequential measurement was on

average higher than the second one, suggesting an order effect. Absolute systolic inter-arm BP

differences were smaller on simultaneous (6.2±6.7/3.3± 3.5 mmHg) compared to sequential

BP measurement (7.8±7.3/4.6±5.6 mmHg, P<0.01 for both). Within-visit reproducibility was

identical (both r=0.60). Simultaneous measurement of BP at both arms reduces order effects

and results in smaller inter-arm BP differences thereby potentially reducing unnecessary

referral and diagnostic procedures.


37

3

INTRODUCTION

Inter-arm blood pressure (BP) differences have been established since the early 20s of the last

century.1 The importance of assessing inter-arm BP differences is to prevent underestimation

and undertreatment of hypertension because the arm with the highest BP should be taken as

reference. Therefore, guidelines on BP measurement recommend bilateral BP measurement at

a patient’s first visit.2;3 In addition, large inter-arm BP differences in systolic BP may indicate the

presence of atherosclerotic plaques and other vascular occlusive diseases and are associated

with increased cardiovascular risk.4;5 A recent meta-analysis of studies assessing the inter-arm

BP differences showed that a systolic BP difference larger than 15 mmHg was associated with

an increased risk of cardiovascular mortality.6 This is in line with another recent prospective

study in hypertensive primary care patients, which showed that a BP difference of ≥10 mmHg

was an independent predictor of cardiovascular events and all-cause mortality after ten years

of follow-up.7

Despite the clinical relevance of inter-arm BP differences, there are few recommendations

on how they should be assessed. The recently released guidelines on arterial hypertension of

the European Society on Cardiology (ESC) and Hypertension (ESH) recommend simultaneous

measurement, although this recommendation is not supported by evidence. Sequential

measurement of inter-arm BP differences is potentially influenced by order effects as the first BP

is on average higher than subsequent readings8. Indeed, more patients with a large inter-arm

BP difference were found in studies that used sequential assessment of inter-arm BP differences

compared to studies that assessed BP simultaneously.9 Simultaneous measurements, on the

other hand, may directly influence BP. Previous studies have shown that unilateral cuff inflation

may increase systolic BP up to 4 to 9 mmHg.10-12 Explanatory mechanisms include compression

of the muscles13, pain and discomfort during the measurement14, or increased arousal due to

the knowledge that BP is being measured.15 This reactive rise in BP might be stronger during

simultaneous bilateral BP measurement compared to unilateral sequential measurements.

Our primary objective was therefore to assess BP differences between sequential and

simultaneous measurements. Our second aim was to compare absolute inter-arm BP

differences and within-visit reproducibility of inter-arm BP differences between these methods

in normotensive and hypertensive subjects at low and high risk for cardiovascular disease.

Subjects

We performed a randomized cross-over study between April 2010 and May 2012 to compare

BP, inter-arm BP differences, and within-visit reproducibility between simultaneous and

sequential BP measurement. Prior to the study we defined four groups according to BP status

(BP <140/90 mmHg or ≥140/90 mmHg) and age (<50 or ≥60 year) to ensure that subjects at

low and high cardiovascular risk were included. An equal number of patients was included

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in each group. Subjects were hospital employees, students, or patients recruited from the

hypertension clinic of a large teaching hospital in Amsterdam, the Netherlands. Patients were

included if they were aged ≥18 years and able to provide written informed consent. Reasons

for exclusion were pregnancy, smoking or coffee consumption less than one hour before the

study visit, inability to acquire valid measurements (e.g. when the arm was too large to fit an

appropriate cuff or because of cardiac arrhythmias), and established unilateral or bilateral

abnormalities influencing blood flow or causing obstruction of the lymphatic system to the

left or right arm.

All eligible participants provided informed consent. The study was approved by the

Institutional Review Board.

METHODS

Cardiovascular risk was assessed by a standardized questionnaire that included age,

medication use, current smoking status, alcohol drinking behaviour, ethnicity (self-defined

black, white or other), and history of diabetes, hypertension and cardiovascular events. Self-

reported dyslipidemia, diabetes, and hypertension or use of statin therapy, glucose-lowering

therapy or BP-lowering medication was used to define the presence of dyslipidemia, diabetes

and hypertension, respectively. For alcohol intake, the number of patients with a daily alcohol

consumption of ≥2 units (females) or ≥3 units (males) was documented. Length and weight

were registered to calculate body mass index (BMI). BP was taken after at least five minutes rest

while seated, with appropriately sized cuffs positioned at heart level and both arms supported.

Sequential and simultaneous BP measurements were taken with the same, validated BP device

(Watch BP offic e ABI, Microlife, Widnau, Switzerland).16;17 This device is equipped with two

linked cuffs allowing both simultaneous and sequential measurement with the same device.

The first BP measurement was taken at the non-dominant arm and used to classify

subjects into BP categories. Subsequently the order of measurement type (sequential versus

simultaneous) and sequence (starting arm for sequential measurements) were randomized

using a computer generated randomization scheme. For sequential measurement, three

measurements alternating between arms (i.e. left-right-left or vice versa) were taken. To avoid

time-order effects, an equal number of readings was taken for simultaneous measurements,

meaning that three simultaneous pairs were recorded in every subject. For sequential

measurements we defined the first BP pair as the first and second measurement, and the

second pair as the second and third measurement. During all measurements both cuffs

remained attached to both arms.


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Mean BP of the first simultaneous and sequential pair was compared to calculate BP

differences between both methods. To correct for a potential order effect, we also compared the

mean BP of every individual sequential measurement with the mean BP of the corresponding

arm assessed by simultaneous measurement.

Inter-arm BP differences were expressed as absolute values (without sign). To assess

differences in inter-arm BP differences between sequential and simultaneous measurements

we compared the first pair of both methods. We classified inter-arm BP differences into three

commonly used categories (≥10, ≥15, and ≥20 mmHg) to assess the number of subjects with

large inter-arm BP differences. We compared the categorized inter-arm BP differences between

the first and second pair of each method and between the first pairs of both methods.

Cardiovascular events were defined as a history of ischemic stroke, transient ischemic attack

(TIA), myocardial infarction or peripheral vascular disease. Cardiovascular risk factors were

defined as a history of hypertension, diabetes, or dyslipidemia, male gender, age 60 year or

older, current smoker, alcohol use of ≥2 units/day for women and ≥3 units/day for men, and a

BMI of >25 kg/m2.

Sample Size Calculation and Statistical Analysis

To assess whether simultaneous BP measurement influences absolute BP levels, we assumed

that a systolic BP difference of ≥2 mmHg with sequential measurement would be clinically

relevant. A 2 mmHg increase in systolic BP is associated with a 10% increase in stroke risk

and a 7% increase in risk of ischemic heart disease.18 Assuming a standard deviation (SD) of

differences of 11 mmHg, we calculated that 239 persons would be needed to demonstrate a

2 mmHg difference with 80% power and alpha=0.05.19 We aimed to include 240 subjects with

60 subjects in each stratum.

Baseline variables were expressed as mean and their SD for variables with a parametric

distribution. Categorical variables were expressed as actual numbers and percentages.

Differences in parametric variables between subjects were compared with an independent

t-test and within subjects with a paired t-test. Differences in parametric variables among

subgroups were compared with analysis of variance with post-hoc analysis where appropriate.

Categorical variables were compared with Pearson’s χ2 between subjects and with the McNemar

test for within-subjects comparison. Correlations of parametric variables were assessed with

Pearson’s correlation coefficient. Bland-Altman analysis was used to express within-visit

reproducibility of inter-arm BP differences by simultaneous and sequential BP measurement.

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RESULTS

A flow-chart of study participants is shown in Figure 1. After exclusion, 60 subjects were

included in each group, totalling 240 subjects aged 52±21 year, of which 107 were male

(45%). Baseline characteristics stratified for age and BP are presented in Table 1. Older subjects

reported most cardiovascular events, including 28 (23%) subjects with ischemic stroke or TIA,

12 (10%) with coronary artery disease, and 12 (10%) with peripheral artery disease. Younger

subjects reported 3 (3%) ischemic strokes or TIAs, 2 (2%) coronary artery diseases and 1 (1%)

peripheral artery disease.

Figure 1. Flowcharts of study participants�

�

�

�

�

�

�

�

�

�

�

�

�

�

�

�

�

�

Assessed for eligibility (n=265)

Excluded (n=10) ♦ Not meeting inclusion criteria (n=10)

o Inability to measure BP (n=6) o Smoking/coffee <1 hour prior to

measurement (n=4)

Start with sequential measurement (n=129) Start with simultaneous measurement (n=126)

Randomized (n=255)

Three sequential measurements starting with left arm (i.e. left-right-left) (n=60)

Three sequential measurements starting with right arm (i.e. right-left-right) (n=69)

Analysed (n=60) ♦�Excluded from

analysis (Incomplete/Incorrect CRF) (n=2)�

Analysed (n=60)♦�Excluded from


Analysed (n=64) ♦�Excluded from


Analysed (n=56)♦�Excluded from


Three simultaneous measurements at both arms (n=64) �


Three sequential measurements starting with left arm (i.e. left-right-left) (n=64)

Three sequential measurements starting with right arm (i.e. right-left-right) (n=62)


Three simultaneous measurements at both arms (n=69)

CRF; case report form


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3

Table 1. Baseline characteristics (n=240)

Young and BP<140 and <90 mmHgn=60

Old and BP<140 and <90 mmHgn=60

Young and BP≥140 and/or ≥90 mmHgn=60

Old and BP≥140 and/or ≥90 mmHgn=60

Age (SD) 26.6 (6.7) 72.9 (8.8) 39.7 (6.8) 69.7 (8.6)

Male (%) 20 (33%) 26 (43%) 27 (45%) 34 (57%)

BMI (SD) 23.9 (4.1) 26.8 (6.8) 27.9 (6.3) 26.5 (4.0)

White (%) 46 (77%) 50 (83%) 28 (47%) 53 (88%)

SBP (SD) 120 (10.9) 126 (14.2) 154.2 (17.5) 160.1 (19.0)

DBP (SD) 72.8 (7.7) 73.6 (9.6) 98.7 (12.1) 88.5 (10.6)

CVE (%) 0 22 (37%) 3 (5%) 16 (27%)

Smoking (%) 18 (30%) 9 (15%) 15 (25%) 9 (15%)

DM (%) 4 (7%) 16 (27%) 9 (15%) 13 (22%)

Hypertension 6 (10%) 38 (63%) 44 (73%) 44 (73%)

Dyslipidemia (%) 3 (5%) 19 (32%) 6 (10%) 16 (27%)

Alcohol (%) 12 (20%) 10 (17%) 6 (10%) 9 (15%)

Number of CV risk factors (SD) 1.3 (1.0) 2.9 (1.3) 2.7 (1.2) 4.0 (1.2)

Continuous variables are expressed as mean and standard deviation (SD), frequencies are expressed as numbers and percentages. Young, aged <50; Old, aged ≥60; BP, blood pressure; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; DM, diabetes mellitus; CV, cardiovascular; CVE, cardiovascular events defined as a documented ischemic stroke, transient ischemic attack, myocardial infarction or documented peripheral vascular disease

Mean BP

The mean systolic BP of the first pair was lower with sequential measurement (134.3±20.2

mmHg) than by simultaneous measurement (135.7±21.1 mmHg, Δ-1.3±7.5 mmHg; P<0.01),

while diastolic BP values did not differ (81.0±14.9 mmHg versus 81.4±15.1 mmHg, Δ-0.4±5.3

mmHg; P=0.21). The difference in systolic BP disappeared when comparing the mean systolic

BP of the first pair of sequential measurement with the mean systolic BP of the first two

simultaneous measurements taken at the corresponding arms of sequential measurements (Δ

0.5±6.8 mmHg; P=0.24).

Mean systolic BP was higher on the arm that was measured first (135.1±20.9 mmHg)

compared to the arm that was measured second (133.5±21.0 mmHg; P=0.03) with sequential

measurement. When comparing these blood pressures with the mean BP of the corresponding

arm of the simultaneous measurements, the first measurement was also higher (135.8±23.8

mmHg) compared to the second measurement (133.8±21.7 mmHg; P<0.01). For hypertensive

subjects the difference in systolic BP between the first and second measurement was 2.3±11.7

mmHg on sequential (P=0.03) and 2.6±10.2 mmHg on simultaneous measurement (P<0.01),

while this difference was 0.7±9.4 mmHg (P=0.40) on sequential and 1.4±9.0 mmHg (P=0.10) on

simultaneous measurement for normotensive subjects.

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Inter-arm BP Differences

Inter-arm BP differences of the first pair of sequential measurements were larger (7.8±7.3/4.6±5.6

mmHg) compared to the first pair of simultaneous measurements (6.2±6.7/3.3±3.5 mmHg;

P<0.01 for both systolic and diastolic BP values). Inter-arm BP differences of the second pair

of sequential measurement (7.6±7.6/4.2±3.8 mmHg) were also larger compared to the first

pair of simultaneous measurements (P<0.01 both for systolic and diastolic values). Mean

systolic inter-arm BP difference on sequential measurement varied between 6.8±5.4 mmHg for

young normotensive and 9.2±8.8 mmHg for old hypertensive subjects, and for simultaneous

measurements between 5.8±4.3 mmHg for young normotensive and 6.9±7.4 mmHg for young

hypertensive subjects.

Large systolic inter-arm BP differences (≥10 mmHg) were less often observed by

simultaneous (18%) compared to sequential (26%) measurements (P<0.01). Inter-arm BP

differences of ≥15 mmHg and ≥20 mmHg were found in 20 (8%) and 10 (4%) subjects on

simultaneous and 30 (13%) and 17 (7%) subjects on sequential measurement (Table 2).

Systolic inter-arm BP differences assessed by sequential measurement correlated

significantly with the number of cardiovascular risk factors (r=0.22, P<0.01) and cardiovascular

events (r=0.16, P=0.01), but not with age and mean BP. Simultaneously assessed inter-arm

BP differences did not significantly correlate with the number of cardiovascular risk factors,

cardiovascular events, age, or mean BP.

Within-visit Reproducibility

The Bland-Altman plot in Figure 2 shows the absolute inter-arm systolic BP differences of the

first and second measurement pair for both methods. For sequential measurements, the mean

difference between the first and second measurement was 0.2 mmHg with an SD of 8.1 mmHg.

For simultaneous measurements the mean differences was 1.0 mmHg with an SD of 6.8 mmHg.

The correlation coefficients of the systolic inter-arm BP difference between the first and

second pairs for sequential and simultaneous measurements were identical (r=0.60, P<0.01 for

both). The reproducibility of commonly used cut-off values for inter-arm BP differences and the

number of subjects that could be identified by the alternative method is shown in Table 2. The

total number of subjects that fell into another inter-arm BP category when comparing the first

with the second inter-arm BP difference was larger for sequential (n=75 [31%] for systolic and

n=88 [37%] for diastolic) compared to simultaneous measurements (n=53 [22%] for systolic,

P=0.03, and n=68 [28%] for diastolic, P=0.06).


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Figure 2. Bland-Altman plot for inter-arm blood pressure differences of simultaneous (a) and sequential (b) blood pressure assessment

(A) Simultaneous assessment

(B) Sequential assessment

Comparison of systolic inter-arm BP difference between first and second pair of measurement for simultaneous (a) and sequential (b) assessment. The horizontal axis of the Bland–Altman plot shows the mean inter-arm BP difference from the first two pairs. On the vertical axis, the difference in inter-arm BP difference of the first two pairs is shown. The solid lines represents the mean at -1.0 mmHg for simultaneous and -0.2 mmHg for sequential assessment. The dashed lines represent 1.96 times the SD above and below the mean at 12.3 and -14.3 mmHg for simultaneous and 15.6 and -15.9 mmHg for sequential assessment.

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Table 2. Reproducibility and comparison of systolic inter-arm BP differences according to commonly used cut-off values for inter-arm BP differences.

Inter-arm BP diff erence ≥10 mmHg ≥15 mmHg ≥20 mmHg

First pair of simultaneous measurements 44 20 10

Identifi ed by second pair of simultaneous measurement 15 (34%) 6 (30%) 4 (40%)

Identifi ed by fi rst pair of sequential measurements 21 (48%) 12 (60%) 5 (50%)

First pair of sequential measurements 63 30 17

Identifi ed by second pair of sequential measurements 33 (52%) 16 (53%) 7 (41%)

Identifi ed by fi rst simultaneous measurement 21 (33%) 13 (43%) 5 (29%)

Number of subjects with a large inter-arm BP difference found on first simultaneous measurement and first pair of sequential measurement and the number of identical subjects with a large inter-arm BP difference that was identified by either the first pair measurement of the other method or by the second pair of measurement of the same method.

DISCUSSION

Simultaneous BP measurement results in a slightly but significantly higher blood pressure

compared to sequential BP measurement. This difference disappeared when taking two

separate BP measurements from the first two simultaneous measurements, pointing towards

an order effect. Simultaneous measurement of inter-arm BP differences led to smaller inter-

arm differences compared to sequential measurement, and markedly reduced the number of

subjects with clinically relevant inter-arm BP differences with similar reproducibility.

The marginally higher systolic BP values during simultaneous compared to sequential

measurement may suggest that the reactive rise in BP is more outspoken during bilateral

compared to unilateral BP measurement. However, this difference was principally attributable

to an order effect: when comparing BP of the first and second sequential measurement with

the BP at the first and the second simultaneous measurement at the corresponding arm, there

were no differences in BP. This suggests that for reliable identification of the arm with the

highest BP simultaneous measurement is preferred over sequential measurement.

When looking at inter-arm BP differences, we found fewer patients with a large inter-arm

BP difference on simultaneous measurement. This finding is in line with a recent meta-analysis

which showed that simultaneous measurement leads to fewer subjects with large inter-arm BP

differences compared to sequential measurements.9 This conclusion was drawn by pooling the

average inter-arm BP difference of studies in which the inter-arm BP difference was assessed

simultaneously, and comparing them with the pooled average of studies that measured

inter-arm BP differences sequentially. Only two studies included in the meta-analysis directly

compared simultaneous with sequential inter-arm BP differences in the same subjects.20;21

These studies reported smaller inter-arm BP differences on simultaneous measurements

compared to sequential measurement. However, in both studies auscultatory BP was used


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as a comparison, which has shown to be less accurate in assessing inter-arm BP difference.9

Another study comparing both methods that was not included in the meta-analysis reported

fewer subjects with a large inter-arm BP difference on simultaneous compared to sequential

measurements.22 However, in this study no true sequential measurements were performed

as they were derived from randomly taking one arm of each simultaneous measurement.

To avoid these shortcomings we compared simultaneous with sequential measurements in

the same population with the same device, and randomized between measurement types

as well as starting arm of the sequential measurements. In line with these previous studies

we demonstrate that fewer subjects had a large inter-arm BP difference on simultaneous

measurement, and also found significantly smaller inter-arm BP differences on simultaneous

compared to sequential measurements.

It has been proposed that inter-arm BP differences should initially be assessed sequentially

and subsequently confirmed by repeated simultaneous measurement when a large inter-

arm BP difference is found.23 When we apply that to the current study population, two out

of three subjects with an initial large systolic inter-arm BP difference (≥10 mmHg) on initial

sequential measurement would have a normal inter-arm BP difference (<10 mmHg) on a single

simultaneous measurement. Adding a second simultaneous measurement further reduced

this number, preventing unnecessary diagnostic procedures. Given the high prevalence of

large inter-arm BP differences, simultaneous measurement should therefore be preferred over

sequential measurement in assessing inter-arm BP differences at a patient’s first visit.

The clinical significance of inter-arm BP differences has recently been subject of debate

following the publications of Clark and co-workers on the relation between inter-arm BP

differences and cardiovascular morbidity and mortality. In a meta-analysis they demonstrated

that systolic inter-arm BP differences ≥15 mmHg with pre-existent cerebrovascular disease,

increased cardiovascular mortality and all-cause mortality.6 This was confirmed in two

prospective studies, which showed that inter-arm differences ≥10 mmHg were an independent

predictor for all-cause mortality.7;24 These data suggest that systolic inter-arm differences of

≥10 mmHg could be considered as an independent risk factor for cardiovascular disease on a

population level. However, because the with-in visit reproducibility is poor, it might be a less

suitable marker for the individual cardiovascular risk prediction.

Our study has some limitations. First, we did not determine associations between inter-

arm BP differences and vascular pathology in our subjects. Second, we do not know whether

the simultaneous measurements were truly taken simultaneously for example because of

small differences in the latency of cuff deflation. It is likely that, especially patients with large

inter-arm BP differences, some differences in measurement time still existed. Nonetheless,

simultaneous measurements taken according to the study protocol still showed a clear benefit

in reducing the number of subjects with large inter-arm BP differences compared to sequential

measurement.

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Conclusions

For a reliable estimation of the inter-arm BP difference, simultaneous measurement should

be preferred over sequential BP assessment at a patient’s initial visit as it is less influenced by

order effects compared with sequential BP measurement and results in smaller inter-arm BP

differences. Within-visit reproducibility of inter-arm BP differences assessed by both methods

was poor, thereby limiting their use for individual cardiovascular risk prediction.


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REFERENCES

(1) Cyriax E. Unilateral alterations in blood pressure: the differential blood-pressure sign (second communication). Q J Med 1921;14:309-313.

(2) National Institute for Health and Clinical Excellence. Hypertension: the clinical management of primary hypertension in adults, CG127. NICE, 2011. 2011.

(3) Mancia G, Fagard R, Narkiewicz K et al. 2013 ESH/ESC Guidelines for the management of arterial hypertension: The Task Force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Eur Heart J 2013.

(4) Aboyans V, Criqui MH, McDermott MM et al. The vital prognosis of subclavian stenosis. J Am Coll Cardiol 2007;49:1540-1545.

(5) Aboyans V, Kamineni A, Allison MA et al. The epidemiology of subclavian stenosis and its association with markers of subclinical atherosclerosis: the Multi-Ethnic Study of Atherosclerosis (MESA). Atherosclerosis 2010;211:266-270.

(6) Clark CE, Taylor RS, Shore AC, Ukoumunne OC, Campbell JL. Association of a difference in systolic blood pressure between arms with vascular disease and mortality: a systematic review and meta-analysis. Lancet 2012;379:905-914.

(7) Clark CE, Taylor RS, Shore AC, Campbell JL. The difference in blood pressure readings between arms and survival: primary care cohort study. BMJ 2012;344:e1327.

(8) Gould BA, Hornung RS, Kieso HA, Altman DG, Raftery EB. Is the blood pressure the same in both arms? Clin Cardiol 1985;8:423-426.

(9) Verberk WJ, Kessels AG, Thien T. Blood pressure measurement method and inter-arm differences: a meta-analysis. Am J Hypertens 2011;24:1201-1208.

(10) Charmoy A, Wurzner G, Ruffieux C et al. Reactive rise in blood pressure upon cuff inflation: cuff inflation at the arm causes a greater rise in pressure than at the wrist in hypertensive patients. Blood Press Monit 2007;12:275-280.

(11) Musso NR, Giacche M, Galbariggi G, Vergassola C. Blood pressure evaluation by noninvasive and traditional methods. Consistencies and discrepancies among photoplethysmomanometry, office sphygmomanometry, and ambulatory monitoring. Effects of blood pressure measurement. Am J Hypertens 1996;9:293-299.

(12) Veerman DP, van Montfrans GA, Wieling W. Effects of cuff inflation on self-recorded blood pressure. Lancet 1990;335:451-453.

(13) Julius S, Li Y, Brant D, Krause L, Buda AJ. Neurogenic pressor episodes fail to cause hypertension, but do induce cardiac hypertrophy. Hypertension 1989;13:422-429.

(14) Shapiro AP, outsos SE, rifcher E. Patterns of pressor response to noxious stimuli in normal, hypertensive, and diabetic subjects. J Clin Invest 1963;42:1890-1898.

(15) Redman S, Dutch J. Cardiovascular responses during cuff inflation in subjects who have been sensitised to the measurement of their blood pressure. N Z Med J 1984;97:180-182.

(16) Saladini F, Benetti E, Masiero S, Palatini P. Accuracy of Microlife WatchBP Office ABI monitor assessed according to the 2002 European Society of Hypertension protocol and the British Hypertension Society protocol. Blood Press Monit 2011;16:258-261.

(17) Stergiou GS, Tzamouranis D, Protogerou A, Nasothimiou E, Kapralos C. Validation of the Microlife Watch BP Office professional device for office blood pressure measurement according to the International protocol. Blood Press Monit 2008;13:299-303.

(18) Lewington S, Clarke R, Qizilbash N, Peto R, Collins R. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 2002;360:1903-1913.

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(19) Stergiou GS, Baibas NM, Gantzarou AP et al. Reproducibility of home, ambulatory, and clinic blood pressure: implications for the design of trials for the assessment of antihypertensive drug efficacy. Am J Hypertens 2002;15:101-104.

(20) Eguchi K, Yacoub M, Jhalani J, Gerin W, Schwartz JE, Pickering TG. Consistency of blood pressure differences between the left and right arms. Archives of Internal Medicine 2007;167:388-393.

(21) Lohmann FW, Eckert S, Verberk WJ. Interarm differences in blood pressure should be determined by measuring both arms simultaneously with an automatic oscillometric device. Blood Press Monit 2011;16:37-42.

(22) Kleefstra N, Houweling ST, Meyboom-de JB, Bilo HJ. [Measuring the blood pressure in both arms is of little use; longitudinal study into blood pressure differences between both arms and its reproducibility in patients with diabetes mellitus type 2]. Ned Tijdschr Geneeskd 2007;151:1509-1514.

(23) Clark CE. Inter-arm blood pressure measurement needs to be practical and accurate. Am J Hypertens 2011;24:1189-1190.

(24) Sheng CS, Liu M, Zeng WF, Huang QF, Li Y, Wang JG. Four-limb blood pressure as predictors of mortality in elderly chinese. Hypertension 2013;61:1155-1160.

4Poor Adherence to Home Blood Pressure

Measurement Schedule

Niels V. van der Hoeven, Bert-Jan H. van den Born, Marianne Cammenga,

Gert A. van Montfrans

Departments of Internal and Vascular Medicine, Academic Medical Center of the


Journal of Hypertension, 2009;27(2):275-279

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ABSTRACT

Background Consensus dictates that devices used for home blood pressure (BP) measurement

should be equipped with a memory to store readings, rather than trusting patients’ logbooks.

However, data entered in the memory rely on patients’ adherence to measurement schedules.

We investigated the number and relevance of deviations from the requested measurement

schedule.

Methods We instructed 106 patients to perform 28 BP readings in a 2-week period with a

memory-equipped electronic device. Patients were requested to note their scheduled BP

values in their logbook and were not informed on the presence of a memory function.

Results The concordance between all BP recordings in both memory and logbook was 90.1%

of possible total scheduled readings. The difference of mean BP of all readings from memory

compared to all readings from the logbook was -0.06 (95% CI –0.79 to 0.68) mmHg systolic

and -0.28 (95% CI -0.97 to 0.40) mmHg diastolic. Unscheduled measurements were performed

by 57.5% of patients. Missing scheduled readings in both logbook and memory were found

in 34.0% of patients. Fictional data were present in 16.0% of patients. When comparing all

individual BP readings from the memory and the logbook, 10.4% of patients were classified

in another hypertension stratum according to the ESH criteria. In 23.6% of patients we did not

find any bias.

Conclusion In spite of the use memory-equipped devices, to ensure patients’ adherence to

measurement schedules, patients still need proper instruction and a close watch.


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INTRODUCTION

Home blood pressure measurement (HBPM) is increasingly being used in medical practice as a

reliable tool to monitor the effect of blood pressure (BP) lowering therapy without interference

of the white coat effect [1]. Perhaps even more important is the capability of HBPM to improve

BP control [2]. There is evidence that HBPM is a better predictor of cardiovascular outcome

than conventional BP measurement [3-5]. Modern HBPM devices are usually equipped with a

memory chip allowing easy data handling. Compared to patients’ handwritten data, stored data

are processed more rapidly and free of errors. Several studies compared the handwritten BP

values acquired from patients with data stored in the memory of an electronic BP measurement

device [6-9]. Patients participating in these studies were unaware of the memory function of

the electronic device: values acquired from a person’s logbook frequently had reporting bias

against a memory-equipped device as gold standard [8].

However, the use of stored data from a memory-equipped device as gold standard can

be questioned. Data that are entered in the memory still rely on patients’ adherence to

measurement schedules and procedures. There are several ways by which data acquired from

the electronic device can be biased. Users can take unscheduled readings, skip readings, or

measure someone else’s BP. Therefore, we performed a single blind cohort study in which we

not only assessed the agreement between logbook and memory, but also investigated the

relevance of the deviations from the requested measurement schedule.

METHODS

We consecutively included 109 hypertensive patients from the outpatient department of

an academic hospital in a multiethnic community. A qualified research nurse instructed the

patients how to measure their BP at home with a validated electronic BP device (Stabil-o-graph,

IEM GmbH, Stolberg, Germany) [10]. The Stabil-o-graph is equipped with a memory function

capable of storing 50 measurements. Two supervised measurements were performed after

the instruction. The patients were asked to measure their BP on seven different days in two

weeks, totaling 28 measurements. On every scheduled day they were instructed to follow a

standardized routine: they had to perform two measurements in the morning after waking up,

and two in the evening before going to sleep. Patients were asked not to perform unscheduled

measurements and to measure only their own BP. All patients received a logbook in which

all dates, time of day (morning or evening) and number of measurement (first or second) for

every scheduled measurement were entered by the research nurse. The logbook contained

four blank boxes in which patients were requested to fill in the time, systolic BP, diastolic BP,

and pulse (not used for analysis of this study) respectively for every scheduled measurement.

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Patients were also asked to record all problems or difficulties they encountered during their

measurements as well as remarks about their medication intake and well-being. Patients were

given a leaflet on which all the information was summarized. To ensure an unbiased assessment

of the logbook data, patients were not informed about the memory function of the BP device.

The study was approved by the institutional ethical board.

Data-analysis

All data recorded before or on the first scheduled day were omitted from the logbook as well

from the memory of the electronic BP device. We used the 24 remaining home measurements

for analysis. To determine the accuracy of the patient-reported data we used the following

criteria. The home BP measurement was considered valid if there were less than 50 readings

recorded in the memory. The logbook data were all the BP values patients wrote down in their

logbook. The memory data were all the recordings that were retrieved from the memory of the

electronic BP device. Concordant measurements were defined as all entries that were identical in

logbook and memory as regards date, time, and value. The mean of concordant measurements

was considered as the concordant BP. Unscheduled measurements were defined as the

number of unscheduled readings found in the memory, in the logbook, or in both. Missing

readings were defined as the number of scheduled readings which were missing both in the

logbook and the memory. Fictional data were all data that were entered in the logbook but

could not be retrieved from the memory of the electronic BP device. Omitted readings were

defined as all scheduled readings which were found in the memory of the electronic BP device,

but were not entered in the logbook. Patients’ education level was classified in two groups

including primary school or vocational education in the lower education group, and secondary

school, higher professional education, or academic education in the higher education group.

Concomitant cardiovascular diseases (CVDs), defined as having a myocardial infarction or

stroke recorded in medical history, were assessed by reviewing patients’ medical charts. To

determine the clinical relevance of individual differences between the logbook entries and the

memory readings, we classified all patients according to BP category as endorsed in the ESH

hypertension guideline [11], based on either all logbook, or all memory retrieved individual

BP averages. The same classification was performed based only on scheduled logbook, or

scheduled memory retrieved BP values.


For sample size calculation a detectable difference of systolic BP of 2 mmHg between logbook

and memory was required [12]. Taking a standard deviation of differences (SDD) of 6.9 [13],

95 patients were needed for this study with an alpha of 5% and a power of 80%. To correct for

inter-assay variations and protocol deviations, the aim was to include 100 patients. Baseline

variables were expressed as mean±SD. Variables with a skewed distribution were described


53

4

as median with interquartile range (IQR). Concordance was the percentage of measurements,

out of the total number of measurements, which were identical in the memory as well as in

the logbook. BP differences between mean BP data retrieved from the memory and logbook

entries were calculated with their 95% confidence interval (CI). P values were calculated using

a two-sided paired t test for dependent continuous variables, an independent two-sided t-test

for independent continuous variables, and χ2 for categorical variables. A P value of less than

0.05 was considered statistically significant. Statistical analysis was performed using SPSS 14.0

(SPSS Inc., Chicago, Illinois, USA).

RESULTS

A total of 109 patients were initially recruited. Three patients were excluded form further

analysis: one patient did not submit his logbook and two patients performed 50 or more

measurements, leaving a total of 106 patients for further analysis. Patient characteristics are

given in Table 1. Seventy-five patients (70.8%) were on one or more antihypertensive drugs.

The mean BP retrieved from the memory was 146.5±17.1/88.8±10.5 mmHg, the mean BP

from the logbook entries was 146.5±17.6/89.1±10.7 mmHg. The mean difference between the

memory BP and the logbook BP was -0.06 (95% CI –0.79 to 0.68) mmHg systolic (NS), and -0.28

(95% CI -0.97 to 0.40) mmHg diastolic (NS). The correlation between memory and logbook was

r=0.98 (P<0.001) for systolic BP and r=0.94 (P<0.001) for diastolic BP.

Figure 1 shows a Bland-Altman plot, where the differences between the systolic BP retrieved

from the logbook and the memory are plotted against the mean systolic BP of the logbook and

memory values. Two outliers are visible. The outlier at the top of the figure was a patient who

had few readings stored in the memory, but filled the whole logbook. The fictional systolic

data in the logbook were on average 30 mmHg lower than the memory values. The outlier at

the bottom end of the figure was a patient who performed many unscheduled readings after

increasing the dose of her anti-hypertensive medication. The BP went down, and thus many of

the unscheduled BP measurements performed were lower than the logbook average.

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Table 1. Patient characteristics.

Unscheduled measurements

Missingreadings

Fictional data

Number of patients 106 61 35 17

Age±SD 55.9±12.7 57.0±12.9 54.29±11.1 53.0±16.0

Male (%) 56 (52.8%) 30 (49.2%) 15 (42.9%) 12 (70.6%)

BMI±SD 27.1±4.4 27.2±4.2 27.0±4.5 28.5±4.7

Higher education (%) 32 (30.2%) 16 (26.2%) 11 (33.4%) 4 (23.5%)

CVD 18 (17.0%) 10 (16.4%) 8 (22.9%) 3 (17.6%)

Born in the Netherlands (%) 62 (58.5%) 35 (57.4%) 18 (51.4%) 8 (47.1%)

Systolic logbook BP±SD 146.6±17.6 150.7±19.0 147.0±16.6 151.8±20.8

Diastolic logbook BP±SD 89.1±10.7 90.9±11.4 89.4±9.9 94.4±12.1

Systolic memory BP±SD 146.5±17.1 150.1±18.4 147.2±16.5 152.9±20.0

Diastolic memory BP±SD 88.8±10.5 90.6±10.7 88.56±10.0 94.7±13.2

All BP values are in mmHg. SD, standard deviation; CVD, established cardiovascular diseases defined as myocardial infarction or stroke in medical history.

Figure 1. Comparison of systolic BP between logbook entries and memory device.

The horizontal axis of the Bland-Altman plot shows the mean systolic BP from the memory

and the logbook. On the vertical axis, the difference between the systolic BP from the memory

and the logbook is shown. The solid line represents the mean at -0.06 mmHg. The dashed lines

represent 1.96 times the standard deviation above and below the mean at 7.41 mmHg and

-7.52 mmHg respectively. For comment on outliers, see text.


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Concordant measurements

A total of 2293 concordant measurements were obtained out of a possible total of 2544,

averaging 21.6 (90.1%) concordant measurements per patient. There were 53 (50.0%) patients

with 100% concordant measurements, and 91 (85.8%) patients with at least 18 (75%) concordant

measurements. Nine (8.5%) patients had 12 (50%) or less concordant measurements. The mean

concordant BP was 146.3±17.2/88.6±10.4 mmHg.


A total of 360 unscheduled readings were performed by 61 patients (57.5%) of which 159

(44.2%) were higher than the concordant BP average, and 201 (55.8%) were lower. The

median number of unscheduled readings of all patients who had at least one unscheduled

reading was 4.0 (IQR 2.0-9.0). Mean memory-recorded BP of patients with unscheduled

readings was 150.2±18.4/90.6±10.7 mmHg, and did not differ from their mean concordant BP

(150.3±18.5/90.4±11.0 mmHg, NS).

Missing readings

There were 36 patients (34.0%) who missed a total of 165 readings, with a median of 4.0 (IQR

2.0-6.0). Seventeen (47.2%) patients had 1-2 missing readings, 8 (22.2%) patients had 3-4

missing readings, 4 (11.1%) patients had 5-6 missing readings, and 7 (19.4%) patients had 7 or

more missing readings. In 18 patients we found unscheduled measurements in the memory as

well as missing data in the logbook. Mean concordant BP of patients with one or more missing

reading was 146.8±16.5/88.4±9.8 mmHg.

Fictional data

Seventeen patients (16.0%) entered a total of 81 BP readings in the logbook, which could not be

retrieved from the memory of the BP measurement device. The median number of fictional data

entries of all the patients who had entered at least one fictional reading was 3.0 (IQR 1.0-6.0).

Thirty-five (43.2%) fictional entries were higher than the concordant mean BP, and 46 (57.8%)

entries were lower. However, when comparing the mean fictional BP (153.2±21.2/95.2±12.5

mmHg) with the mean concordant BP (152.2±21.2/94.3±12.5 mmHg) there were no significant

differences. Compared to patients without fictional data (n=90) the concordant systolic BP of

patients with fictional data was 9.2 mmHg higher (P=0.047). The concordant diastolic BP was

8.0 mmHg higher (P=0.04). There were 15 patients who entered fictional data and performed

extra readings of which 4 had missing readings as well.

In summary, 17 patients entered on average 3.0 fictional readings. There were 14.6% more

fictional data lower than higher compared to the concordant BP, but the mean fictional BP was

not different from the concordant BP. Patients with fictional data had a significantly higher BP

than patients without fictional data.

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Omitted readings

Three patients (2.8%) had omitted 15 scheduled readings, which were recorded in the memory,

but where not written down in the logbook. One patient omitted 9 readings, one patient

omitted 5 readings, and one patient omitted 1 reading. When comparing the mean BP of the

omitted readings (127.7±8.1/82.2±3.0) to the mean concordant BP (133.3±7.5/83.7±6.7) there

were no significant differences. The mean concordant BP of patients with omitted readings did

not differ from the patients without omitted readings (n=103, 146.7±17.2/88.8±10.5). Of these

three patients, one had also missing data, one had performed unscheduled readings, and one

had performed both. None missed data or had entered fictional data.

There were 25 patients (23.6%) with full adherence to the measurement schedule and complete

agreement between memory and logbook. Their mean BP was 139.9±14.6/85.1±9.2 mmHg.

The BP was lower compared to patients with bias (148.3±17.6/89.6±10.6 mmHg, P=0.05

systolic and P=0.03 diastolic), but there were no differences regarding sex, age, education and

country of birth.

When we classified all patients according to BP category, there were 11 (10.4%) individuals

who had a difference of at least one stage of hypertension between all memory values and

all logbook entries (see Table 2). In nine of these patients, the average BP retrieved from the

memory was classified into a lower BP category compared to the average logbook BP. When

comparing only the scheduled readings from the memory to the scheduled readings from the

logbook, five (4.7%) patients fell into another BP category (data not shown). Three of them had

fictional readings in their logbook, and two of them had reported measurements as scheduled

in the logbook, while they were unscheduled according to the memory.

Table 2. Comparison of all individual BP by ESH criteria (n=106).

Memory

Logbook

Normo-tensive

Stage IHyperten-sion

Stage IIHyperten-sion

Stage IIIHyperten-sion

Patients shifted ≥1 stage up

Normotensive 32 0 0 0 0

Stage IHypertension

4 36 1 0 1

Stage IIHypertension

1 3 24 1 1

Stage IIIHypertension

0 0 1 4 0

Patients shifted ≥1 stage down

5 3 1 0 11

Rows and columns represent the number of patients who fall into a specific ESH BP category according to all logbook or memory BP values. Shaded in grey are the numbers of patients whose grading is identical. An upward shift indicates a higher BP classification in the memory compared to the logbook BP. A downward shift indicates a lower BP classification in the memory compared to the logbook. The bold numbers represent the total patients per BP category who shifted ≥1 BP stage down (bottom row) or up (right column), and the total amount of patients that shifted ≥1 BP stage (bottom right corner).


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DISCUSSION

In our study on patients’ adherence to home BP measurement procedures and the agreement

between logbook and memory, we found that many patient-reported data are biased.

Irrespective of using the logbook or the memory-equipped device, 76.4% of the patients

deviated from their requested measurement schedule. More than half of the patients

(57.5%) performed unscheduled measurements. Missing readings were found in 34.0% of

the population. Fictional data were present in 16.0% of the patients. Only 25 (23.6%) patients

measured their BP exactly according to the instructed protocol, with no missing or fictional

data, and without unscheduled measurements. On average, all these errors had almost no

influence on the mean BP. However, in individual patients, the deviations from the requested

schedule could influence treatment decisions. Classifying our patients according to the ESH

grades of severity of hypertension [11], 11 patients (10.4%) fell into a different hypertension

stratum when comparing all average individual logbook entries with all retrieved memory

values. Omitting nonscheduled readings reduced this figure, not surprisingly, to 5 patients

(4.7%).

We confirmed that patients with any form of bias had on average a statistically significantly

higher BP than patients who had no reporting bias [6]. Fourteen of the 15 omitted readings were

performed by two patients born outside the Netherlands. They had both performed duplicate

measurements, but mainly reported single measurements (data not shown). For other forms

of bias there was, however, no relation between reporting bias and other characteristics, such

as age, education, and country of birth. We did not find a tendency towards omitting higher

values, as previously described by patients with diabetes mellitus measuring their own glucose

levels [14].

Thus, despite the use of an electronic memory-equipped device, still a considerable amount

of patients has reporting bias. Innovations such as the recently introduced WatchBP (Microlife

AG, Widnau, Switzerland), an electronic BP device allowing patients to take a fixed number of

BP readings only at preset times in the ‘diagnostic’ mode, might help to improve adherence

[15]. Proper explanation by a physician or nurse emphasizing the importance of adherence

to measurement schedules and procedures should also contribute to a further decrease in

reporting bias. It has been shown that patients informed about the memory function reported

significantly less missing data than when they were not informed [16]. Although the use of

a memory-equipped device without a logbook eliminates fictional data, keeping a logbook

could serve as a reminder to adhere to the requested measuring schedule.

The present study has some limitations. First, the memory of the electronic BP device we

used had a capacity of 50 measurements. Patients were asked to perform 28 measurements,

leaving room for a maximum of 22 unscheduled readings, assuming no scheduled readings

were skipped. When patients performed more unscheduled readings, all readings after the

first 50 measurements were deleted from the memory. In the present study, two patients took

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at least 50 measurements. Since we do not know any possible deviations in those deleted

readings, we excluded them from further analysis. Second, other people than the intended

patient could have used the BP device. A few patients indeed reported to have occasionally

measured another persons BP. Although probably few, the number and influence of these

BP readings could not be assessed in the current study. Third, the population of our study

consisted of patients referred to a specialist, mainly because their BP was difficult to control,

which may limit the generalizability of our results. Fourth, the HBPM was performed in

a research setting. The research nurse asked the patients explicitly to follow the instructed

schedule, and the logbook had a structured design, leaving little room for patients to write

down unscheduled measurements. In day-to-day practice, such intensive instruction might

not be available, and the prevalence of reporting bias might even be higher than found in our

study. Finally, we graded the patients according to the ESH criteria to determine the clinical

relevance of BP differences between the logbook entries and the values acquired from the

electronic BP device. However, the ESH criteria are based on office BP, which has slightly higher

reference values than HBPM [17].

In conclusion, we found that patients, intentionally or not, often deviate from the requested

measurement schedule during HBPM. The BP changes resulting from reporting bias did not

affect the average BP on a population level. However, when comparing the individual BP values

from the memory with the logbook, one out of every 10 patients changed one or more BP

categories. For studies, memory-equipped BP devices are adequate to provide reliable group

averages. Future research should focus on increasing patients’ adherence to measurement

schedules and procedures. Frequent telemonitoring or the use of BP devices with preset

diagnostic modes may both be useful tools to achieve this goal.


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REFERENCES

1. Verberk WJ, Kroon AA, Kessels AGH, De Leeuw PW. Home blood pressure measurement - A systematic review. Journal of the American College of Cardiology 2005; 46(5):743-751.

2. Cappuccio FP, Kerry SM, Forbes L, Donald A. Blood pressure control by home monitoring: A meta-analysis of randomised trials. Journal of Hypertension 2004; 22:S287.

3. Bobrie G, Chatellier G, Genes N, Clerson P, Vaur L, Vaisse L, et al. Cardiovascular prognosis of “masked hypertension” detected by blood pressure self-measurement in elderly treated hypertensive patients. Jama-Journal of the American Medical Association 2004; 291(11):1342-1349.

4. Ohkubo T, Imai Y, Tsuji I, Nagai K, Kato J, Kikuchi N, et al. Home blood pressure measurement has a stronger predictive power for mortality than does screening blood pressure measurement: a population-based observation in Ohasama, Japan. Journal of Hypertension 1998; 16(7):971-975.

5. Sega R, Facchetti R, Bombelli M, Cesana G, Corrao G, Grassi G, et al. Prognostic value of ambulatory and home blood pressures compared with office blood pressure in the general population - Follow-up results from the Pressioni Arteriose Monitorate e Loro Associazioni (PAMELA) study. Circulation 2005; 111(14):1777-1783.

6. Johnson KA, Partsch DJ, Rippole LL, Mcvey DM. Reliability of self-reported blood pressure measurements. Archives of Internal Medicine 1999; 159(22):2689-2693.

7. Mengden T, Alvarez E, Beltran B, Kraft K, Vetter H. Reliability of reporting self-measured blood pressure values of hypertensive patients. Journal of Hypertension 1998; 16:S271.

8. Myers MG. Reporting bias in self-measurement of blood pressure. Blood Pressure Monitoring 2001; 6(4):181-183.

9. Nordmann A, Frach B, Walker T, Martina B, Battegay E. Reliability of patients measuring blood pressure at home: prospective observational study. British Medical Journal 1999; 319(7218):1172.

10. Westhoff TH, Schmidt S, Zidek W, van der GM. Validation of the Stabil-O-Graph blood pressure self-measurement device. J Hum Hypertens (in press).

11. Mancia G, De Backer G, Dominiczak A, Cifkova R, Fagard R, Germano G, et al. 2007 Guidelines for the Management of Arterial Hypertension: The Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Journal of Hypertension 2007; 25(6):1105-1187.

12. Lewington S, Clarke R, Qizilbash N, Peto R, Collins R. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 2002; 360(9349):1903-1913.

13. Stergiou GS, Baibas NM, Gantzarou AP, Skeva II, Kalkana CB, Roussias LG, et al. Reproducibility of home, ambulatory, and clinic blood pressure: Implications for the design of trials for the assessment of antihypertensive drug efficacy. American Journal of Hypertension 2002; 15(2):101-104.

14. Mazze RS, Shamoon H, Pasmantier R, Lucido D, Murphy J, Hartmann K, et al. Reliability of blood glucose monitoring by patients with diabetes mellitus. The American journal of Medicine 1984; 77(2):211-217.

15. Stergiou G, Giovas PP, Jaenecke B, Chang A, Yen CY, Tan TM. Microlife watch BP monitor: A tool for reliable home BP monitoring designed strictly according to the European society of hypertension working group on BP monitoring (ESH-WG) recommendations. Journal of Hypertension 2006; 24:266.

16. Bachmann LM, Steurer J, Holm D, Vetter W. To what extent can we trust home blood pressure measurement? A randomized, controlled trial. Journal of clinical hypertension 2002; 4(6):405-407.

17. Thijs L, Staessen JA, Celis H, de Gaudemaris R, Imai Y, Julius S, et al. Reference values for self-recorded blood pressure - A meta-analysis of summary data. Archives of Internal Medicine 1998; 158(5):481-488.

5‘Diagnostic Mode’ Improves Adherence to the

Home Blood Pressure Measurement Schedule

Stephanie E Wessel, Niels V van der Hoeven, Marianne Cammenga,

Gert A van Montfrans, Bert-Jan H van den Born

Departments of Internal and Vascular Medicine, Academic Medical Center of the


Blood Pressure Monitoring. 2012 Oct;17(5):214-9

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ABSTRACT

Background The accuracy of home blood pressure measurement (HBPM) depends on adherence

to the measurement schedule. We investigated the number of deviations from the requested

schedule using a HBPM device equipped with a diagnostic mode that only allows patients to take

a fixed number of BP readings at preset times.

Methods We randomised patients to measure their BP as recommended by the European

Society of Hypertension guideline in either the usual mode or the diagnostic mode.

Results A total of 135 patients were included, mean age 54.4 ± 13.6 year, 57 (42.2%) men, with

a mean systolic BP of 147.0 ±18.4 mmHg, and a mean diastolic BP of 88.0 ± 10.3 mmHg. In 66

patients BP was measured in the diagnostic mode, whereas in 69 patients BP was measured in the

usual mode. In the diagnostic mode, 40% of patients showed full adherence to the measurement

schedule, compared with 23% of patients in usual mode (P=0.02). Unscheduled measurements

were performed by 55% of patients measuring in the usual mode and none in the diagnostic

mode. The number of patients that omitted readings was similar in diagnostic and usual mode

(P= 0.9). Compared with scheduled readings only, 12% of patients measuring in the usual mode

fell into a different BP category, whereas reclassification did not occur in patients using the

diagnostic mode (P=0.03).

Conclusion HBPM in the diagnostic mode almost doubled the number of patients with full

adherence to the measurement schedule and eliminated the number of patients that were

reclassified in a different BP category.


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INTRODUCTION

Home blood pressure measurement (HBPM) is a reliable tool to monitor the effect of blood

pressure (BP)-lowering therapy without interference of the white-coat effect [1, 2]. HBPM is

more closely associated with daytime ambulatory BP than office BP [3, 4]. There is now ample

evidence that HBPM is a better predictor of cardiovascular outcome compared with office B P

[5-7]. HBPM is capable of improving BP control [8], and reduces the need for anti-hypertensive

dru gs [9]. Modern HBPM devices are usually equipped with a memory chip allowing easy data

handling. Compared with patients’ handwritten data, stored data are processed more rapidly

and free of errors. Therefore, memory readings are preferred over logbook entries. However, data

that are entered in the memory still rely on patients’ adherence to measurement schedules and

procedures.

There are several ways data from an electronic device can be biased. Patients can perform

unscheduled measurements, miss a measurement or measure someone else’s BP. It has been

shown that adherence to measurement schedules is important in obtaining a reliable BP [10].

We have shown previously that 58% of patients performed unscheduled measurements despite

careful instructions from a trained nurse [11], showing that there is room for improvement

in patient adherence to HBPM. Because unscheduled measurements are taken outside the

standardized measurement occasions, they are susceptible to variations in BP as a result of

medication effects and circadian physiological mechanisms. A potential tool to eliminate

unscheduled readings is the recently introduced WatchBP Home device (Microlife AG, Widnau,

Switzerland). The WatchBP Home is an oscillometric device for home BP monitoring that is

equipped with a switch enabling patients to measure their BP in either the diagnostic or the

usual mode. The diagnostic mode allows patients to take a fixed number of BP readings only

at preset times following European Society of Hypertension (ESH) recommendations on self-

measurement o f BP [6]. In the usual mode, patients are at liberty to take any number of readings

at any time. Although patients cannot deviate in the diagnostic mode from the scheduled times,

they can certainly omit measurements by oversight or by simply deciding not to take a BP

reading.

We hypothesized that measuring BP at home in the diagnostic mode should result in an

overall better adherence to the BP measurement schedule than measuring BP in the usual

mode. To assess the added value of the diagnostic mode facility, we determined in a group of

hypertensive outpatients the number of deviations from the requested measurement schedule

in the diagnostic and usual mode, and compared the numbers. To determine whether elimination

of unscheduled readings results in a clinically significant difference in BP, we classified all readings

with and without unscheduled readings according to BP categories as endorsed by the ESH

guideline and compared them.

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PATIENTS AND METHODS

We used a prospective randomized open-label design to compare HBPM performed in the usual

mode with HBPM performed in the diagnostic mode. We recruited consecutive patients referred

for HBPM at the outpatient clinic at the department of Internal Medicine of the Academic Medical

Center in Amsterdam, the Netherlands. Patients were randomly assigned using a computer

generated allocation scheme to measure their BP at home in the usual or the diagnostic mode

according to the ESH recommendation on HBPM.

The Watch BP Home is equipped with a memory capable of storing 250 measurements

in the usual mode. The WatchBP Home has been validated and given a pass according to the

International Protocol of the European Society of Hypertension Working Group on Blood Pressure

Monit oring [12, 13]. In the diagnostic mode, the device allows and stores two measurements

between 6:00 and 12:00 h and two measurements between 18:00 and 24:00 h for 7 consecutive

days. Outside these periods the device does not allow BP measurements, while in the usual mode

the device allows BP measurements at all times. Because the diagnostic mode measures BP twice

automatically at specific pre-set time points, we used an open label design in order to be able to

instruct patients properly on their measurement scheme. Before the study, nurses were trained

to deliver a standardized instruction of ~15 minutes. Patients were asked to measure their BP

on seven consecutive days, twice in the morning immediately after waking up and twice in the

evening before going to bed, totalling 28 measurements. In the diagnostic mode, they were told

that the device would measure their BP twice, with a 1-minute interval between measurements

and, that BP recordings outside these time frames would not be possible. In the usual mode,

patients were instructed to measure their BP twice manually, immediately after waking up

between 06:00 and 12:00 h and before going to bed between 18:00 and 24:00 h. Patients who

measured in the usual mode were not informed that they could measure outside these time

periods. If patients were not able to perform BP measurements within these time periods, they

were excluded from participation. The number of patients who had to be excluded for this reason

was recorded. All patients were urged to measure only their own BP.

All patients received a logbook in which they were to record the date, time, systolic blood

pressure (SBP), diastolic blood pressure (DBP), and heart rate, for every scheduled measurement.

In the diagnostic mode, the device only displays the mean of the two measurements, which

patients were asked to record. In the usual mode, patients were asked to record the value of

both measurements. All patients were asked to report remarks about their medication intake

and maintain a diary of their activities. Patients were given a leaflet on which all the information

was summarized. Two supervised measurements were performed after the instructions. To avoid

possible changes in adherence to the measurement schedule, patients were not informed about

their participation in this study and about the memory function of the device, as it has been

demonstrated that patients who are aware of the memory function show less bias [14]. The


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study procedures were considered standard medical care; approval of our institutional ethics

committee was therefore not required.

Data analysis

Patients were included if they performed at least one scheduled measurement. In agreement

with ESH recommendations all measurements of the first day were discarded, leaving 24

measurements per patient for further calculations. For this analysis, only the memory recorded

measurements were used. As primary outcome measure we chose the number of patients with

full adherence to the HBPM schedule as recommended by the ESH. The mean of all measurements

that were on schedule, defined as the mean of measurements that were stored in the memory at

the correct day and time was considered the concordant BP.

To determine the number and the nature of the deviations from the measurement schedule,

we used the following predefined criteria. Unscheduled measurements were defined as readings

that were stored in the memory outside the predefined date and time frames or were additional

to the number of scheduled measurements. Readings that were scheduled but could not be

retrieved from the memory were defined as omitted readings. The result were analysed on an

intention to treat basis, because this best simulates real life situations.

In addition to the registration of patient characteristics, language, highest education level,

and history of cardiovascular disease were recorded. Patient’ education level was classified in

two groups, primary school or vocational education in the lower group and secondary school,

higher professional education or academic education in the higher group. Patient’ language was

defined as Dutch when their primary language was Dutch. Cardiovascular disease was defined as

having a history of myocardial infarction or stroke.

To determine the clinical relevance of unscheduled measurements patients were classified

into the ESH-recommended BP categories [15]. For this comparison the mean Bps of all recorded

readings and just the scheduled readings were calculated.

Sample size and statistical analysis

We calculated that a total of 120 patients was needed to demonstrate a 25% difference in the

number of patients that adhered to the measurement schedule with an alpha of 0.05 and 80%

power. To account for protocol deviations, the aim was to include 140 patients.

Baseline variables were expressed as mean ± SD. An independent two-sided t-test was used

to calculate BP differences between the usual mode and diagnostic mode. A two sided paired

t-test was used to calculate BP differences between scheduled and unscheduled readings in each

group. Paired BP differences were calculated with their 95% confidence interval (CI). Categorical

variables were calculated using χ². A P value of less than 0.05 was considered statistically

significant. Statistical analysis was performed using SPSS 18.0 (SPSS inc., Chicago, Illinois, USA).

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RESULTS

A total number of 139 patients was recruited. Three patients could not participate because they

could not adhere to the HBPM schedule, leaving 136 patients for randomization. One patient

was excluded because of a failure to perform valid measurements, leaving a total of 135 patients

for further analysis. Twelve patients switched from the diagnostic to the usual mode at any time

during the HBPM recording. Baseline patient characteristics are presented in Table 1. There were

no significant differences in baseline characteristics between patients measuring in the usual

mode or patients measuring in the diagnostic mode. In the diagnostic mode 27 (40%) patients

had full adherence to the measurement schedule, whereas in the usual mode 16 (23%) patients

had full adherence to the measurement schedule (P=0.03).

When excluding patients who switched between both modes, 27 (50%) of patients in the

diagnostic mode had full adherence to the measurement schedule whereas 16 (23%) patients in

the usual mode had full adherence to the measurement schedule (P<0.01).

Table 1. Baseline characteristics

All patients Usual Mode Diagnostic Mode

Number of patients 135 69 66

Age ± SD 54.4 ± 13.6 54.2 ± 14.4 54.7 ± 12.8

Men (%) 57 (42.2%) 33 (47.8%) 24 (36.4%)

BMI ± SD 28.3 ± 5.8 28.1 ± 5.3 28.7 ± 6.2

Systolic BP ± SD 147.0 ±18.4 145.6 ± 16.4 148.4 ± 20.4

Diastolic BP ± SD 88.0 ± 10.3 87.8 ±10.4 88.3 ±10.4

CVD (%) 53 (39.3%) 24 (36.2%) 29 (43.9%)

Higher education (%) 73 (54.1%) 39 (56.5%) 34 (51.2%)

Dutch language (%) 112 (83.0%) 55 (79.7%) 57 (86.4%)

BP, blood pressure; CVD, cardiovascular disease

Scheduled measurements

A total number of 3170 measurements were obtained, 2767 of these measurements were

scheduled, averaging 20.5 (85%) scheduled measurements per patient. The number of scheduled

measurements in the diagnostic mode and the usual mode was identical between groups with

a mean of 20.5 (IQR 20-24) readings in the diagnostic mode and 20.5 (IQR 20-24) readings in

the usual mode (P=0.95). The characteristics of patients who performed only scheduled readings

are presented in the left panel of Table 2. The average BP of the scheduled measurements did

not significantly differ between groups and was 148.7±20.4/88.3±10.4 mmHg in the diagnostic

mode and 146.6±16.6/88.4±10.9 mmHg in the usual mode (P=0.5 SBP and P=1.0 DBP).


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A total of 42 (30.7%) patients performed 425 unscheduled measurements, 35 (83.3%) patients

performed 220 (51.8%) unscheduled measurements outside the predefined timeframe and

seven (16.7%) patients performed 205 (48.2%) unscheduled additional measurements to the

scheduled measurements. The characteristics of the patients with unscheduled readings are

presented in the middle panel of Table 2. Because the device did not allow patients to perform

unscheduled measurements in the diagnostic mode, all of the unscheduled measurements

were performed by patients measuring in the usual mode or by patients who switched modes.

Out of the patients measuring BP in the usual mode 38 (55.1%) performed 370 unscheduled

measurements, with a median of 5 (IQR 2-12) measurements per patient. Out of the 12 patients

who switched modes, four performed 33 unscheduled measurements, with a median of 9 (IQR

4-12) measurements per patient. In the usual mode, 151 unscheduled measurements were

higher than the concordant BP average and 241 were lower. In the group that switched modes,

11 unscheduled measurements were higher than the concordant BP and 22 were lower. The

mean difference between unscheduled measurements and scheduled measurements was -4.2

mmHg (95% CI -7.8 to -0.6) for SBP and -2.5 mmHg (95% CI -4.6 to -0.3) for DBP in the usual mode

and -6.5 mmHg (95% CI -14.5 to 1.5) for SBP and -0.5 mmHg (95% CI -1.4 to 0.4) for DBP in the

group that switched modes.

Table 2. Characteristics of patients with unscheduled or omitted measurements

Scheduled Unscheduled Omitted

Usual Diagnostic Usual Diagnostic Usual Diagnostic

Number of patients 16 27 38 4 39 38

Age ± SD 58.4 ± 15.8 55.7 ± 11.9 52.8 ± 14.2 57.0±16.1 51.2 ± 13.0 54.3 ± 13.8

Men (%) 6 (37.5%) 8 (29.6%) 21 (55.3%) 2 (50%) 19 (48.7%) 15 (39.5%)

BMI ± SD 26.1 ± 3.8 29.3 ± 7.6 28.0 ± 5.7 27.4 ±5.2 28.8 ± 6.2 28.8 ± 6.8

Systolic BP ± SD 151.3 ± 14.2 148.3 ± 21.5 140.9 ± 15.4 143.0 ± 8.5 144.8 ± 18.2 148.9 ± 20.0

Diastolic BP± SD 86.5 ± 12.2 87.3 ± 12.5 86.7 ± 9.4 88.5 ± 3.4 89.3 ± 10.9 88.9 ± 8.8

CVD (%) 6 (37.5%) 10 (37%) 16 (42.1%) 0 (0%) 17 (43.6%) 14 (36.8%)

Higher education (%) 8 (50%) 14 (51.9%) 23 (60.5%) 0 (0%) 19 (48.7%) 20 (52.6%)

Dutch language (%) 12 (75%) 25 (92.6%) 29 (76.3%) 2 (50%) 32 (82.1%) 32 (84.2%)

The left panel represents the characteristics of patients that performed only scheduled readings. In the middle panel, the same characteristics are shown for patients who performed unscheduled readings. The right panel shows patients who omitted readings. The diagnostic mode included both patients that measured in the diagnostic mode and patients that switched from diagnostic to usual mode. BP, blood pressure; CVD, cardiovascular disease.

Omitted readings

A total of 77 (57%) patients omitted 473 readings. The characteristics of the patients with

omitted readings are presented in the right panel of Table 2. The number of patients that omitted

readings was 38 (57.6%) in the diagnostic mode and 39 (56.5%) in the usual mode (P= 0.9). Also

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the number of readings that were omitted was similar between groups with 233 readings and

a median of 4 (IQR 2-12) omitted readings per patient in the diagnostic mode and 240 readings

(P=0.2) with a median of 4 (IQR 2-11) readings per patient in the usual mode. In the diagnostic

mode the mean BP of patients with omitted readings was 148.9±20.0/88.9±8.8 mmHg. This did

not significantly differ from the mean BP of just scheduled readings 149.2±20.0/88.9±8.8 mmHg

(P=0.1 for SBP and P=1.0 for DBP) In the usual mode, the mean BP of patients with omitted

readings was 144.8±18.2/89.3±10.9 mmHg. Compared with the BP of all scheduled readings

146.2±18.3/90.3±11.4 mmHg) this was significantly lower for DPB (P=0.04), but not for SBP

(P=0.06).

When comparing the total of memory readings with the all scheduled memory readings,

eight (12%) patients measuring in the usual mode fell into a different BP category, whereas

reclassification into a different BP category did not occur in patients using the diagnostic

mode (P=0.03). One patient was classified two categories higher only based on scheduled

measurements, six patients were classified one category higher and one patient was classified

one category lower. Patient classification according to BP category with and without unscheduled

measurements is listed in Table 3.

Table 3. Comparison of BP readings with and without unscheduled readings according to HBPM schedule by BP category of patients measuring in the usual mode.

Total memory → Normotensive Stage 1 hypertension

Stage 2 hypertension

Stage 3 hypertension

Patients shifted ≥ 1

stage upScheduled

↓

Normotensive 43 0 0 00

Stage 1 hypertension 2 52 1 0 1



Patients shifted ≥ 1 stage down

2 4 1 0 7/1

Rows and columns represent the number of patients who fall into a specific ESH BP category according to all scheduled memory BP readings (without unscheduled readings; rows), or all memory BP values (including the unscheduled readings; columns), or both. Taking the scheduled readings as the standard, an upward shift indicates a higher BP classification, and a downward shift a lower BP classification when including the unscheduled readings. The bold numbers represent the total patients per BP category who shifted ≥ 1 BP down (right column) or up (bottom row) and the total number of patients that shifted ≥ 1 BP stage down and up respectively (bottom right corner). BP, blood pressure; ESH, European Society of Hypertension.


69

5

DISCUSSION

In the present study measuring in the diagnostic mode significantly improved patient adherence

to the HBPM schedule as endorsed by the ESH by almost doubling the number of patients that

showed full adherence, and by eliminating the number of patients that were reclassified in a

different BP category.

Because patients in the diagnostic mode could measure their BP only at pre-set times we

expected them, being daily prompted to follow a rigid schedule, to have fewer omitted readings.

However, the number of patients that missed readings in the usual and diagnostic mode was

similar, and they also omitted the same number of measurements. Therefore the time frame does

not seem to influence patients to omit readings.

In contrast to omitted readings, the number of patients who performed unscheduled readings

reduced from 58% in the usual mode to 6% in the diagnostic mode. The remaining six percent

can be explained by patients who switched from diagnostic to usual mode. The mean of the

unscheduled measurements was lower than the concordant measurements. This explains why

patients measuring in the usual mode had on average a lower BP, for example due to medication

or circadian effects on BP.

In this study, 85% of scheduled measurements were recorded. This percentage is similar to

previous studies showing a compliance of 90% with recording of scheduled measurements [16,

17]. However, in spite of the excellent compliance with scheduled measurements, we previously

demonstrated that 58% of the patients deviate from the requested schedule by performing

unscheduled readings [11] which we confirmed in the present study. The number of patients

that did not have full compliance with the measurement schedule was 77% in the usual group

and similar to that of our previous report (76%). This shows that the percentage of patients that

deviate in any way from their HBPM schedule is consistently high, but can be reduced by applying

a restriction to the time-frame were measurements can be recorded.

Although the number of patients that fully adhered to the measurement schedule was almost

doubled in the diagnostic mode, still 60% failed to fully comply with measurements schedule and

procedures. However, when comparing all scheduled readings to scheduled and unscheduled

readings combined, none of the patients measuring in the diagnostic mode reclassified into a

different BP category. In contrast, of the patients measuring in the usual mode were 1 out of every

10 patients classified into another BP category (Table 3). Most of these patients were classified

into a higher BP category based on scheduled compared with all readings. This implicates that

patients may receive undertreatment when decisions are based on readings taken in the usual

mode. It therefore seems that measuring in the diagnostic mode improves accuracy of diagnoses

and follow-up of BP lowering treatment.

There are several limitations to our study. First, all patients received a logbook in which they

were to record the date, time, systolic blood pressure, diastolic blood pressure and heart rate for

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every scheduled measurement. This logbook was not used in the present analysis as logbook

recorded data are frequently biased [11, 16, 18, 19]. Furthermore our aim was to compare the

influence of a pre-set time-frame on adherence to the ESH measurement schedule and not the

reliability of logbook entries. As in our previous study, patients were asked to report the data

in a logbook because reporting the data in a logbook could serve as a reminder to keep to the

requested measurement schedul e [20]. However, we are not aware of any studies that support

this practice.

Second, we had to exclude three patients because they were unable to measure during the

scheduled hours, either because they got out of bed before 06:00 or after 12:00 or went to bed

after 24:00. Because the scheduled hours in the device cannot be altered, these patients were

unable to measure in diagnostic mode. This can be solved by designing a function to alter the

pre-set times. Third, although we demonstrated that measuring in the diagnostic mode resulted

in a better adherence to the HBPM schedule as endorsed by the ESH, the relevance of this

improvement with regard to BP control or cardiovascular outcomes remains to be determined.

Finally, the research nurse who instructed the patient on how to measure his or her BP was not

blinded and even though the oral and written instructions were standardized, there could still

have been some instruction bias.

Conclusions

We have shown that a BP measuring device with a pre-set time frame and fixed number of readings

almost doubled the number of patients with complete adherence to the measurement schedule

and eliminated the number of patients that were reclassified in a different BP category. Future

studies could elucidate whether improved adherence leads to improvement of cardiovascular

outcomes.


71

5

REFERENCES

1. Verberk WJ, Kroon AA, Kessels AGH, De Leeuw PW. Home blood pressure measurement - A systematic review. Journal of the American College of Cardiology 2005; 46(5):743-751.

2. Zhu N, Bu M, Chen D, Li T, Qian J, Yu Q, et al. A study of the white-coat phenomenon in patients with primary hypertension. Hypertens Res 2008; 31(1):37-41.

3. Little P, Barnett J, Barnsley L, Marjoram J, Fitzgerald-Barron A, Mant D. Comparison of agreement between different measures of blood pressure in primary care and daytime ambulatory blood pressure. BMJ 2002; 325(7358):254.

4. Stergiou GS, Bliziotis IA. Home Blood Pressure Monitoring in the Diagnosis and Treatment of Hypertension: A Systematic Review. Am J Hypertens 2010.

5. Niiranen TJ, Jula AM, Kantola IM, Kahonen M, Reunanen A. Home blood pressure has a stronger association with arterial stiffness than clinic blood pressure: the Finn-Home Study. Blood Press Monit 2009; 14(5):196-201.

6. Parati G, Stergiou GS, Asmar R, Bilo G, de LP, Imai Y, et al. European Society of Hypertension guidelines for blood pressure monitoring at home: a summary report of the Second International Consensus Conference on Home Blood Pressure Monitoring. J Hypertens 2008; 26(8):1505-1526.

7. Ward AM, Takahashi O, Stevens R, Heneghan C. Home measurement of blood pressure and cardiovascular disease: systematic review and meta-analysis of prospective studies. J Hypertens 2012.

8. Cappuccio FP, Kerry SM, Forbes L, Donald A. Blood pressure control by home monitoring: A meta-analysis of randomised trials. Journal of Hypertension 2004; 22:S287.

9. Verberk WJ, Kroon AA, Lenders JW, Kessels AG, van Montfrans GA, Smit AJ, et al. Self-measurement of blood pressure at home reduces the need for antihypertensive drugs: a randomized, controlled trial. Hypertension 2007; 50(6):1019-1025.

10. Imai Y, Nishiyama A, Sekino M, Aihara A, Kikuya M, Ohkubo T, et al. Characteristics of blood pressure measured at home in the morning and in the evening: the Ohasama study. J Hypertens 1999; 17(7):889-898.

11. van der Hoeven NV, van den Born BJ, Cammenga M, van Montfrans GA. Poor adherence to home blood pressure measurement schedule. J Hypertens 2009; 27(2):275-279.

12. Stergiou GS, Jaenecke B, Giovas PP, Chang A, Chung-Yueh Y, Tan TM. A tool for reliable self-home blood pressure monitoring designed according to the European Society of Hypertension recommendations: the Microlife WatchBP Home monitor. Blood Press Monit 2007; 12(2):127-131.

13. Stergiou GS, Tzamouranis D, Protogerou A, Nasothimiou E, Kapralos C. Validation of the Microlife Watch BP Office professional device for office blood pressure measurement according to the International protocol. Blood Press Monit 2008; 13(5):299-303.

14. Bachmann LM, Steurer J, Holm D, Vetter W. To what extent can we trust home blood pressure measurement? A randomized, controlled trial. J Clin Hypertens (Greenwich) 2002; 4(6):405-7, 412.

15. Mancia G, De Backer G, Dominiczak A, Cifkova R, Fagard R, Germano G, et al. 2007 Guidelines for the Management of Arterial Hypertension: The Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Journal of Hypertension 2007; 25(6):1105-1187.

16. Nordmann A, Frach B, Walker T, Martina B, Battegay E. Reliability of patients measuring blood pressure at home: prospective observational study. BMJ 1999; 319(7218):1172.

17. Port K, Palm K, Viigimaa M. Daily usage and efficiency of remote home monitoring in hypertensive patients over a one-year period. J Telemed Telecare 2005; 11 Suppl 1:34-36.

18. Johnson KA, Partsch DJ, Rippole LL, McVey DM. Reliability of self-reported blood pressure measurements. Arch Intern Med 1999; 159(22):2689-2693.

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19. Mengden T, Hernandez Medina RM, Beltran B, Alvarez E, Kraft K, Vetter H. Reliability of reporting self-measured blood pressure values by hypertensive patients. Am J Hypertens 1998; 11(12):1413-1417.

20. Parati G, Stergiou GS, Asmar R, Bilo G, de LP, Imai Y, et al. European Society of Hypertension practice guidelines for home blood pressure monitoring. J Hum Hypertens 2010; 24(12):779-785.

6Severe Hypertension Related to Caff einated Coff ee

and Tranylcypromine: a Case Report

Niels van der Hoeven, MDa, Ieke Visser, MDb, Aart Schene, MDb,c,d, PhD,

Bert-Jan van den Born, MD, PhDa

aDepartment of Vascular Medicine, Academic Medical Center, University of Amsterdam;

Amsterdam, the NetherlandsbDepartment of Psychiatry, Academic Medical Center, University of Amsterdam;

Amsterdam, the NetherlandscDepartment of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands

dDonders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen,

Nijmegen, the Netherlands

Adapted from Ann Intern Med. 2014 May 6;160(9):657-8. doi: 10.7326/L14-5009-8

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75

6

Background: Hypertension can develop in patients receiving monoamine oxidase (MAO)

inhibitors who consume tyramine-rich food, such as aged cheeses and some alcoholic beverages.

Tyramine leads to increased norepinephrine release at adrenergic nerve terminals when MAO is

inhibited (1, 2). In vitro studies show that caffeine inhibits MAO, suggesting that caffeine might

supplement the effects of MAO inhibitors that are used to treat atypical depression and other

disorders (3–5).

Objective: To report a case of severe hypertension related to an interaction between caffeine

and MAO inhibitor therapy.

Case Report: In February 2013, a psychiatrist referred a 56-year-old man to us for evaluation

of severe hypertension. In November 2012, the patient began tranylcypromine therapy

(Parnate, GlaxoSmithKline, Mississauga, Ontario, Canada), an irreversible MAO inhibitor, for

major depressive disorder in increasing doses to 50 mg twice daily. In the days after his last

dose increase, he began to have progressively severe headaches and difficulty concentrating,

with blood pressure (BP) exceeding 200/110 mmHg. His hypertension had been controlled with

hydrochlorothiazide, 25 mg once daily. His BP was 126/79 mmHg at the last visit to his primary

care physician, which was before tranylcypromine therapy was started.

When we examined him, he was carefully avoiding tyramine-rich foods except for 1 glass of red

wine daily during dinner. He had consumed 10 to 12 cups of caffeinated coffee every day for many

years and smoked 6 to 8 cigarettes per day. He did not use 3,4-methylenedioxymethamphetamine

(ecstasy), amphetamines, or other sympathomimetics.

A physical examination was unremarkable except for a BP of 220/119 mmHg. An

electrocardiogram and kidney function were normal. Analysis of a morning urine sample showed

no microalbuminuria. Ambulatory BP monitoring revealed 2 peaks of elevated BP and an increase

in heart rate after tranylcypromine intake, followed by a progressive decrease in BP during the

evening and at night resulting in a “morning dip” (Figure 1).

We advised the patient to stop drinking caffeinated coffee but to continue tranylcypromine

therapy and consuming wine as usual. Repeated ambulatory BP monitoring 2 weeks later

showed a normal BP pattern with an average daytime BP of 129/85 mmHg and nighttime BP

of 104/65 mmHg (Figure 2). After the patient began drinking decaffeinated coffee in the same

quantity as he had been drinking caffeinated coffee, his BP remained normal (office BP at 2 hours

after tranylcypromine intake, 132/66 mmHg).

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Figure 1. Ambulatory BP monitoring during consumption of caffeinated coffee combined with monoamine oxidase inhibitor therapy.

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This patient’s BP was elevated during the day followed by a steady decrease that started at 17 h and continued during the night, producing a “morning dip.” On this day, the patient received 1 dose of tranylcypromine, which has a half-life of 2.5 h, 1 h before monitoring started and a second dose early in the afternoon. The 2 solid horizontal lines indicate normal average BP during the day (unshaded) and during the night (shaded). His average daytime BP was 148/95 mmHg (normal, <135/85 mmHg), and his average nighttime BP was 107/71 mmHg (normal, <120/70 mmHg). Blood pressure measurements between 13:30 and 15:30 could not be recorded (possibly as a result of extreme vasoconstriction or tachycardia), and he was fasting until the next morning when we removed the monitoring device. BP, blood pressure.

Figure 2. Ambulatory BP monitoring two weeks later without consumption of caffeinated coffee but with continuation of monoamine oxidase inhibitor therapy.

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The BP was recorded two weeks later after refraining from coffee consumption, but with continuation of the monoamine oxidase inhibitor therapy. The 2 solid horizontal lines indicate normal average BP during the day (unshaded) and during the night (shaded). His average daytime BP was now 129/85 mmHg (normal <135/85 mmHg), and his average nighttime BP was 107/71 mmHg (normal <120/70 mmHg), a significant decrease in daytime BP compared to his first ambulatory BP measurement.

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77

6

Discussion: Coffee is not a tyramine-rich product but has been shown to inhibit MAO and

increase the turnover of several monoamines, including 5-hydroxytryptamine, dopamine, and

norepinephrine, in vitro. These mechanisms may have potentiated the vasoconstrictive effects of

tranylcypromine in this patient. It seems unlikely that caffeine alone caused this patient’s sudden

hypertension because previous BP measurements were normal and his coffee consumption

remained unchanged as the BP increased.

We believe that this case shows that habitual consumption of large amounts of caffeinated coffee

can lead to severe hypertension when combined with MAO inhibitor therapy. As a result, we

think that clinicians should consider limiting caffeine consumption in patients with high caffeine

intake when they are receiving MAO inhibitor therapy.

REFERENCES

1. Flockhart DA. Dietary restrictions and drug interactions with monoamine oxidase inhibitors: an update. J Clin Psychiatry. 2012;73 Suppl 1:17-24. [PMID: 22951238]

2. Horwitz D, Lovenberg W, Engelman K, Sjoerdsma A. Monoamine oxidase inhibitors, tyramine, and cheese. JAMA. 1964;188:1108-10. [PMID: 14163106]

3. Fredholm BB, Bättig K, Holmén J, Nehlig A, Zvartau EE. Actions of caffeine in the brain with special reference to factors that contribute to its widespread use. Pharmacol Rev. 1999;51:83-133. [PMID: 10049999]

4. Herraiz T, Chaparro C. Human monoamine oxidase enzyme inhibition by coffee and beta-carbolines norharman and harman isolated from coffee. Life Sci. 2006;78:795-802. [PMID: 16139309]

PART IIBlood Pressure as Predictor of

Cardiovascular Risk

7Home Blood Pressure Measurement as a Screening Tool

for Hypertension in a Web-based Worksite Health

Promotion Program

Maurice A.J. Niessena, Niels V. van der Hoevena,b, Bert-Jan H. van den Bornb,

Coen K. van Kalkena, and Roderik A. Kraaijenhagena

aNetherlands Institute for Prevention and E-Health Development (NIPED) Research Foundation,

Amsterdam, The NetherlandsbDepartments of Internal and Vascular Medicine, Academic Medical Center, Amsterdam,

The Netherlands

Eur J Public Health. 2014 Oct;24(5):776-81

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ABSTRACT

Background Guidelines on home blood pressure measurement (HBPM) recommend taking at

least 12 measurements. For screening purposes, however, it is preferred to reduce this number.

We therefore derived and validated cut-off values to determine hypertension status after the

first duplicate reading of a HBPM series in a web-based worksite health promotion program.

Method 945 employees were included in the derivation and 528 in the validation cohort which

was divided into a normal (n=297) and increased cardiometabolic risk subgroup (n=231), and

a subgroup with a history of hypertension (n=98). Six duplicate home measurements were

collected during three consecutive days. Systolic and diastolic readings at the first duplicate

measurement were used as predictors for hypertension in a multivariate logistic model. Cut-off

values were determined using receiver operating characteristics analysis.

Results Upper (≥150 or ≥95 mmHg) and lower limit (<135 and <80 mmHg) cut-off values were

derived to confirm or reject presence of hypertension after one duplicate reading. The area

under the curve was 0.94 (SE 0.007, 95% confidence interval 0.93-0.95). In 62.5% of participants

hypertension status was determined, with 1.1% false positive and 4.7% false negatives.

Performance was similar in participants with high and low cardiometabolic risk, but worse in

participants with a history of hypertension (10.4% false negatives).

Conclusion One duplicate home reading is sufficient to accurately assess hypertension status

in 62.5% of participants, leaving 37.5% in which the whole HBPM series needs to be completed.

HBPM can thus be reliably used as screening tool for hypertension in a working population.


83

7

INTRODUCTION

Hypertension is a major risk factor for cardiovascular (CV) events,1 and is estimated to affect

up to one billion people worldwide.2 Despite the importance of blood pressure (BP) lowering

therapy in hypertensive patients, adequate BP control (<140/90 mmHg) is achieved in merely

half of hypertensive cases. In addition, 20% to 50% of hypertensive individuals are unaware of

their condition.3-7 These numbers indicate that there is still need to improve both awareness

and control of hypertension. Potential tools to improve BP control and awareness are worksite

health promotion programs. Current health promotion programs are often based on multiple

risk factor interventions, in which BP is assessed as one of several CV risk factors. Although

in general the benefit of these health promotion programs in improving overall CV risk is

limited,8;9 previous uncontrolled studies have shown a positive effect on BP control.10

BP is variable and influenced by many stressors, which include, amongst others, the white-

coat effect.11 Therefore even for standardized office BP measurements the current European

and Canadian guidelines recommend to take BP at least at two to three different visits before

establishing the diagnosis of hypertension.12;13 The British guideline of the National Institute

for Health and Clinical Excellence (NICE) recommends ambulatory BP measurement (ABPM) in

every patient with an elevated office BP to confirm or rule out hypertension.14 For the purpose of

mass screening of BP in health promotion programs, however, ABPM has several disadvantages.

It is expensive, not widely available, and patients experience more discomfort during

measurement compared with home BP measurement (HBPM).15;16 HBPM therefore seems more

suitable for application in screening programs to detect hypertension. HBPM measurements

have similar reproducibility as ABPM measurements,17 are void of the white coat effect,18 and

show better correlation with target organ damage and CV events than conventional office BP

measurements.19-24 Despite these advantages no health promotion programs in which BP is

assessed by HBPM have thus far been reported. Current recommendations on HBPM advocate

to take at least 12 BP measurements.25 For screening purposes, however, one or two duplicate

BP measurements are preferred over a whole series to increase feasibility. Therefore, the aim of

this study was to define and subsequently validate BP cut-off values to either confirm or reject

the diagnosis of hypertension after one or two duplicate home BP measurements in persons at

low and high CV risk. In addition, we examined whether these cut-off values could be applied

to establish hypertension control in patients already known with hypertension.

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METHODS

Participants

The web-based HBPM study was performed as part of a worksite health promotion program

(The Prevention Compass) as implemented at 16 Dutch companies during the period

December 2010 – September 2011.

Initial assessment with a web-based electronic health questionnaire included questions

about medical and family history, health complaints, psychological functioning and health

behavior. Participants aged ≥60, (aged ≥50 for male and ≥55 for female tobacco users), with

a BMI ≥30, with a medical history of cardiovascular diseases (CVD), symptoms suggestive of

CVD, or with a first degree relative diagnosed with CVD before age 60 were considered to be

at high cardiometabolic risk (CMR). Subjects with an estimated SCORE (Systemic Coronary Risk

Evaluation)26 risk of ≥5% based on age, gender, BMI, tobacco use and medical history were also

classified as high CMR.

A subset of the participants with increased CMR was offered HBPM as part of additional

biometric measurements. All other participants were offered HBPM irrespective of their

CMR. Pregnant women were excluded. Informed consent was obtained prior to the study

in accordance with the requirements for identifiable data collection in the Dutch Code of

Conduct for Observational Research (www.federa.org).

Home blood pressure measurements

A validated HBPM device (Sensacare SAA-102, Sensacare Company, Hong Kong, China)27 was

sent to participants who accepted additional biometric measurements. They were instructed

through an enclosed leaflet to take duplicate BP measurements every morning and evening for

three consecutive days. Participants were advised to relax for five minutes before commencing

each duplicate measurement. They were urged not to talk during the measurements and to

breathe normally. They were instructed to place the cuff at heart level while resting their arm

on a table. Participants noted down all readings on a chart enclosed with the measurement

device. After all measurements were completed, participants entered the readings into a

protected, personal webpage. Based on the average BP a tailored advice was reported back to

the participants online.

Derivation cohort and validation cohorts

Participants who completed the HBPM before 13 April 2011 were assigned to the derivation

cohort. The validation cohort consisted of all participants who completed the HBPM between

13 April 2011 and 23 September 2011.


85

7

From the total validation cohort, three predefined subgroups were selected. Those

subgroups included participants with a normal CMR, participants with an increased CMR and

participants with a history of diagnosed hypertension.

Outcome measure

The main outcome measure was the presence of hypertension defined as an average BP over

six duplicate HBPM readings equal to or exceeding 135 mmHg systolic or 85 mmHg diastolic.

For participants with a history of hypertension the same BP limits were used to determine

whether their BP was adequately controlled.


Independent t-tests and χ² were used to determine differences in baseline variables. Repeated

measures ANOVA with Bonferroni post hoc correction for multiple testing was used to compare

the average BP measurements of the first, second and third day. To determine the relevance

of data derived from each increase of the number of duplicate BP measurements intraclass

correlation coefficients (ICCs) were calculated. Using the ICCs, the average BP of six duplicate

HBPM readings was compared with the first duplicate BP reading, the (average of ) the first and

second duplicate BP reading, and so on.

To determine cut-off values for normotension and hypertension two multivariate logistic

models were built. In the first model, the average systolic and diastolic BP readings at the

first duplicate HBPM were used as predictors. In the second model, the average systolic and

diastolic BP readings of the first and second duplicate HBPM were used as predictors.

For each participant a logit score was calculated based on the unstandardized βs of systolic

and diastolic BP (and the constant). The logit scores were subsequently entered into a Receiver

Operating Characteristic (ROC) curve analysis.

Based on predefined limits for the maximum allowed percentages of participants incorrectly

diagnosed as respectively normotensive (false negative) and hypertensive (false positive),

cut-off points on the ROC curve were chosen. Corresponding BP readings were rounded to

the nearest five mmHg (i.e. 122.5/84 was rounded to 125/85) to ensure that clinically useful

cut-off values would be validated. An accuracy measures matrix with incremental five mmHg

BP steps was computed to determine the accuracy of the first duplicate HBPM for predicting

hypertension at various other cut-off values. The performance of the models was assessed by

the ROC curve and the Area Under the Curve (AUC). The AUC of the model in the validation

cohort(s) was tested for significant (one-tailed) differences with the AUC in the derivation

cohort using Hanley and McNeil’s formula.28 The sensitivity, specificity, positive and negative

predictive value, and the positive and negative likelihood ratio of the cut-off values were also

calculated. All analyses were performed using SPSS 19.0 (SPSS inc., Chicago, Illinois, USA).

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Tab

le 1

. Bas

elin

e ch

arac

teris

tics

of H

ealt

h Ri

sk A

sses

smen

t par

ticip

ants

.

D

eriv

atio

n Va

lidat

ion

coho

rt

Valid

atio

n su

bco

hort

Va

lidat

ion

sub

coho

rtVa

lidat

ion

sub

coho

rt

coho

rt

I (n=

528)

II (n

=29

7)III

(n =

231

)IV

(n =

98)

(n

=94

5)To

tal

Nor

mal

CM

R H

igh

CM

RH

yper

tens

ion

Mal

e (%

)49

3(5

2.2%

)29

9(5

6.6%

)15

0(5

0.5%

)14

9(6

4.5%

)56

(57.

1%)

Age

(SD

)53

.1(5

.2)

53.1

(6.2

)52

.0(5

.3)

54.4

(7.0

)53

.9(4

.9)

Educ

atio

n le

vel*

Low

177

(19.

3%)

84(1

5.9%

)45

(15.

2%)

39(1

6.9%

)21

(21.

4%)

Mid

leve

l25

3(2

6.8%

)17

1(3

2.4%

)82

(27.

6%)

89(3

8.5%

)35

(35.

7%)

Hig

h48

6(5

1.4%

)25

4(4

8.1%

)15

6(5

2.5%

)98

(42.

4%)

40(4

0.8%

)

Unk

now

n29

(3.1

%)

19(3

.6%

)14

(4.7

%)

5(2

.2%

)2

(2.0

%)

Hyp

erte

nsio

n (%

)98

(18.

6%)

49(1

6.5%

)49

(21.

2%)

98(1

00.0

%)

SBP

(SD

)12

4.5

(13.

3)12

6(1

5.1)

123.

9(1

5.4)

128.

6(1

4.3)

130.

1(1

6.2)

DBP

(SD

)78

.3(9

.6)

79.3

(10.

9)78

.4(1

1.5)

80.4

(10.

1)83

.0(1

0.4)

Hyp

erte

nsio

n w

as d

efin

ed a

s a

hist

ory

of d

iagn

osed

hyp

erte

nsio

n. F

or d

escr

iptio

n of

diff

eren

t coh

orts

see

text

. Blo

od p

ress

ure

valu

es a

re e

xpre

ssed

in m

mH

g. C

MR,

ca

rdio

met

abol

ic ri

sk; S

BP, s

ysto

lic b

lood

pre

ssur

e; D

BP, d

iast

olic

blo

od p

ress

ure.

*Ed

ucat

ion

leve

l: Lo

w, l

ower

gen

eral

sec

onda

ry/ l

ower

voc

atio

nal;

Mid

leve

l, hi

gher

ge

nera

l sec

onda

ry/p

re-u

nive

rsit

y/in

term

edia

te v

ocat

iona

l; H

igh,

hig

her v

ocat

iona

l / u

nive

rsit

y.


87

7

RESULTS

A total of 1,852 persons participated in the study. Of these participants, 378 (20.5%) did not

complete or report their HBPM readings, leaving 1,473 (79.5%) persons for analysis, including

52% with increased CMR. Persons who did not complete or report their HBPM readings were

younger (48.0±10.0 versus 53±5.6 years, P< 0.01) and less highly educated (42.0% versus 51.8%

higher education; P<0.01) than those who did. No sex differences were observed. A total of

945 participants (64.2%) completed the HBPM before 13 April 2011 and were assigned to the

derivation cohort. The remaining 528 (35.8%) participants were assigned to the validation

cohort. Table 1 summarizes the baseline characteristics of the study cohorts. There were

no differences between the derivation and the total validation cohort. Compared to the

participants with a normal CMR, individuals with a high CMR were older (P<0.01) and more

often male (P=0.01). Also, their mean systolic (P<0.01) and diastolic (P=0.04) BP was higher.

Derivation Cohort

Two-hundred sixty-one (27.6%) subjects were diagnosed with (uncontrolled) hypertension

based on their HBPM series. The average morning BP (123±14/78±10 mmHg) was lower

than the average evening BP (126±14/78±10 mmHg, P<0.01 for systolic, P=0.01 for diastolic

BP). Also, the average BP of the first, second and third measurement day were significantly

different (P<0.01 for systolic, P<0.01 for diastolic BP). Systolic BP of the first day (125±14

mmHg) was higher than the systolic BP of the second (124±14 mmHg (P<0.01), but not of the

third day (124±14 mmHg, P=0.11). The average diastolic BP of the first day (79±10 mmHg) was

higher compared to the diastolic BP of the second (78±10 mmHg, P=0.01), and the third day

(78±10mmHg, P<0.01).

The average of each consecutively included duplicate HBPM was compared to all six

duplicate measurements using ICC. As shown in Figure 1, all ICCs were ≥0.9. The largest

increase in ICC was observed between the average of the first (morning), and the average of

the first and second (evening) duplicate HBPM. Addition of other duplicate measurements did

not further increase ICC.

Two separate cut-off values were selected and subsequently rounded to their nearest

five mmHg. The first cut-off BP value was set to discriminate normotensive from possible

hypertensive persons. For the purpose of this study the false negative rate for hypertension

was not allowed to exceed 5%. A reading of ≥135/80 mmHg at the first duplicate HBPM was

chosen as the ‘lower limit’ cut-off value, indicating that participants with a first duplicate

reading of ≥135 or ≥80 mmHg (sensitivity: 0.96, specificity: 0.71) were classified as having

possible hypertension. Vice versa, those with a first duplicate HBPM reading of <135 mmHg

and <80 mmHg were labelled as normotensive (sensitivity: 0.71, specificity: 0.96). Sensitivity

and specificity of other cut-off values are shown in Table 2.

R1R2R3R4R5R6R7R8R9


Chapter 7

88

The second cut-off BP value was set to positively diagnose hypertension. For the purpose

of this study the false positive rate for hypertension were minimized at 1%. A value of ≥150/95

mmHg (sensitivity: 0.33, specificity: 1.00) was selected as the ‘upper limit’ cut-off value. Thus,

participants with a first duplicate HBPM reading of ≥150 or ≥95 mmHg were classified as

hypertensive. For those with a first duplicate HBPM between the lower and upper cut-off

limits, no accurate diagnosis was possible based on the first duplicate HBPM. The AUC of the

second model, using the average readings of the first and second duplicate HBPM to predict

hypertension, was 0.97 (standard error [SE] 0.01, 95% confidence interval [CI] 0.96-0.98),

representing a marginal improvement on the first model [AUC 0.94, SE 0.01%, 95% CI 0.93-

0.95]. We therefore proceeded to validate the cut-off scores based only on the first duplicate

HBPM.

Figure 1. Intraclass Correlation Coefficients from each increase in the number of duplicate home blood pressure measurements as compared with all six duplicate measurements.

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89

7

Table 2. Accuracy measures at different blood pressure cut-off values of the first duplicate measurement for predicting hypertension

Systolic →120 125 130 135 140 145 150 155 160 165 170

Diastolic ↓

70se 1.000 1.000 1.000 1.000 1.000 0.996 0.996 0.996 0.996 0.996 0.996

sp 0.251 0.263 0.278 0.282 0.284 0.284 0.284 0.284 0.284 0.284 0.284

75se 1.000 1.000 1.000 1.000 0.992 0.981 0.977 0.977 0.977 0.977 0.977

sp 0.415 0.463 0.496 0.504 0.506 0.507 0.507 0.507 0.507 0.507 0.507

80se 0.985 0.973 0.962 0.962 0.935 0.916 0.904 0.904 0.900 0.900 0.900

sp 0.513 0.624 0.684 0.709 0.724 0.728 0.732 0.732 0.732 0.732 0.732

85se 0.969 0.935 0.874 0.824 0.762 0.709 0.690 0.686 0.682 0.682 0.682

sp 0.541 0.715 0.819 0.883 0.906 0.911 0.917 0.917 0.917 0.917 0.917

90se 0.958 0.912 0.820 0.693 0.602 0.513 0.471 0.460 0.448 0.444 0.441

sp 0.544 0.732 0.855 0.942 0.977 0.984 0.990 0.990 0.990 0.990 0.990

95se 0.954 0.908 0.805 0.648 0.521 0.395 0.326 0.295 0.257 0.249 0.241

sp 0.545 0.734 0.858 0.944 0.981 0.991 0.999 0.999 0.999 0.999 0.999

100se 0.954 0.908 0.793 0.625 0.475 0.310 0.222 0.180 0.138 0.123 0.111

sp 0.545 0.734 0.858 0.944 0.981 0.991 0.999 0.999 0.999 0.999 0.999

105se 0.954 0.908 0.789 0.621 0.460 0.287 0.188 0.138 0.084 0.065 0.054

sp 0.545 0.734 0.858 0.944 0.981 0.991 0.999 0.999 0.999 0.999 0.999

110se 0.954 0.908 0.789 0.621 0.460 0.287 0.188 0.138 0.077 0.057 0.042

sp 0.547 0.735 0.860 0.946 0.982 0.993 1.000 1.000 1.000 1.000 1.000

115se 0.954 0.908 0.789 0.621 0.456 0.284 0.180 0.130 0.069 0.050 0.034

sp 0.547 0.735 0.860 0.946 0.982 0.993 1.000 1.000 1.000 1.000 1.000

120se 0.954 0.908 0.789 0.621 0.452 0.276 0.169 0.119 0.057 0.038 0.023

sp 0.547 0.735 0.860 0.946 0.982 0.993 1.000 1.000 1.000 1.000 1.000

Cut-off values above the black line indicates a BP level with a sensitivity of ≥95% in detecting hypertension defined as an average blood pressure of ≥135/90 mmHg on 12 home blood pressure measurements. Se, sensitivity; sp, specificity.

Validation cohorts

Figure 2 depicts the ROC curves of both the total validation and the derivation cohort. The

AUC of the validation cohort (AUC 0.94, SE 0.01, 95% CI [0.92- 0.96]) was not different (P=0.45)

from the AUC in the derivation cohort (AUC 0.94, SE 0.01, 95% CI [0.93- 0.95]). Table 3 shows the

accuracy measures for the validation cohorts, using the cut-off values chosen in the derivation

cohort.

There were 169 (32.0%) subjects diagnosed with (uncontrolled) hypertension in the total

validation cohort. After the first duplicate measurement, 62.5% of the total validation cohort

could be classified. The classified group included 71.8% of the normotensive and 37.3% of

the hypertensive participants, while four individuals (1.1%) with a normal BP were incorrectly

labelled as hypertensive, and eight persons with hypertension (4.7%) were incorrectly labelled

R1R2R3R4R5R6R7R8R9


Chapter 7

90

as normotensive. The average BP of these eight participants was 138/83 mmHg. The average

BP of the uncategorized participants (37.5%) was 131/83 mmHg with 50% being hypertensive.

The AUC in the normal and high CMR subgroups were, respectively, 0.95 (SE 0.01, 95% CI

[0.92- 0.97]) and 0.92 (SE 0.02, 95% CI [0.89- 0.96]). The AUCs of the derivation cohort did not

differ from the AUCs of both the normal (P=0.35) and high (P=0.23) CMR validation cohorts.

After the first HBPM 67.0% of the normal CMR and 56.7% of the high CMR subgroups were

classified.

In almost half (49.0%) of individuals in the subgroup with a history of hypertension BP

was not optimally controlled. The AUC in this subgroup was 0.91 (SE 0.03, 95% CI [0.85- 0.96]),

which was not different from the AUC in the derivation cohort (P=0.16). After the first duplicate

HBPM, 44.9% of this subgroup was classified. The average BP of the uncontrolled hypertensive

subjects who were mislabelled as having normal BP was 139/84 mmHg.

Figure 2. Receiver Operating Characteristic curves for the prediction model of hypertension for the derivation and validation cohort.


91

7

Tab

le 3

. Dia

gnos

tic C

lass

ifica

tion

Acc

urac

y by

firs

t dup

licat

e H

BPM

Rea

ding

TP

FNFP

TNSe

nsiti

vity

Spec

ifi ci

ty

PPV

NPV

LR +

LR-

Tota

l val

idat

ion

co

ho

rt (n

=52

8)

Cut

-off

for h

yper

tens

ive

(≥15

0 or

≥95

mm

Hg)

63

106

435

537

.3%

98.9

%94

.0%

77.0

%33

.50.

6

Cut

-off

for n

orm

oten

sive

(<13

5 an

d <

80 m

mH

g)

255

104

816

171

.0%

95.3

%97

.0%

60.8

%15

.00.

3

No

rmal

CM

R s

ub

gro

up

(n=

297)

Cut

-off

for h

yper

tens

ive

(≥15

0 or

≥95

mm

Hg)

33

482

214

40.7

%99

.1%

94.3

%81

.7%

44.0

0.6

Cut

-off

for n

orm

oten

sive

(<13

5 an

d <

80 m

mH

g)

161

553

7874

.5%

96.3

%98

.2%

58.6

%20

.10.

3

Hig

h C

MR

su

bg

rou

p (n

= 2

31)

Cut

-off

for h

yper

tens

ive

(≥15

0 or

≥95

mm

Hg)

30

582

141

34.1

%98

.6%

93.8

%70

.9%

24.4

0.7

Cut

-off

for n

orm

oten

sive

(<13

5 an

d <

80 m

mH

g)

9449

583

65.7

%94

.3%

94.9

%62

.9%

11.6

0.4

His

tory

of h

yper

ten

sio

n s

ub

gro

up

(n=

98)

Cut

-off

for u

ncon

trol

led

hyp

erte

nsiv

e (≥

150

or ≥

95 m

mH

g)14

340

5029

.2%

100.

0%10

0.0%

59.5

%

∞0.

7

Cut

-off

for c

ontr

olle

d hy

per

tens

ive

(<13

5 an

d <

80 m

mH

g)

2525

543

50.0

%89

.6%

83

.3%

63.2

%4.

80.

6

TP, t

rue

pos

itive

; FN

, fal

se n

egat

ive;

FP,

fals

e p

ositi

ve; T

N, t

rue

nega

tive;

PPV

, pos

itive

pre

dict

ive

valu

e; N

PV, n

egat

ive

pre

dict

ive

valu

e; L

R, li

kelih

ood

ratio

.

R1R2R3R4R5R6R7R8R9


Chapter 7

92

DISCUSSION

The current study demonstrates that HBPM can be used as a reliable tool for diagnosing

hypertension in a working population. One duplicate measurement was sufficient to classify

62.5% of the screened participants. Only 37.5% of the screened participants are required to

complete the whole 3-day series of HBPM for a reliable classification. For participants with a

history of hypertension, the proposed cut-off values classified merely 45% correctly with >10%

false negatives, suggesting that one duplicate measurement at cut-off values derived from a

general sample can not sufficiently discriminate BP control in these patients.

Although HBPM has made its way to routine medical practice, most current screening

programs still make use of a single, on-site BP measurement. Not only do we show that HBPM

can be used as a reliable alternative, with the introduction of two simple cut-off values we

were also able to reduce the burden of HBPM to one duplicate reading for more than six out of

every ten participants. Lessening the burden of HBPM in a screening setting is important, as in

our population 20% failed to completely record all requested measurements. In addition, the

reduced number of readings required to accurately classify patients as either normotensive

or hypertensive reduces measurement bias. We previously showed that many hypertensive

patients do not follow the requested HBPM schedule resulting in over- or underestimation

of BP.29 The use of a single BP measurement for the majority of a screening population may

therefore increase both feasibility and accuracy.

To apply current cut-off values in a screening program it is required that participants

report their first duplicate BP reading, for example on a protected personal webpage as in the

current study, so that direct feedback can be given. If the first duplicate BP reading exceeds

150/95 mmHg or is below 135/80 mmHg, participants can be classified as hypertensive

or normotensive, respectively, without performing further measurements. The remaining

participants (whose BP values do not exceed the cut-off limits) are advised to complete the

whole series of HBPM. Because the duplicate measurement from which the cut-off values

were derived was taken in the morning, it is advised to apply the cut-off values on a duplicate

measurement which is taken in the morning.

For participants with a history of hypertension, assessment of BP status after one duplicate

measurement was considerably less accurate than in the other validation cohorts. This can most

likely be explained by the fairly large amount of participants with uncontrolled hypertension

(49%) within this subgroup combined with a higher average BP (139/84 mmHg). These

findings underscore the importance of including participants with an established history of

hypertension in health screening programs as there is evidence that uncontrolled hypertension

leads to excess CV mortality in treated hypertensive patients.30 This also suggests that patients

with a history of hypertension should always complete the minimally recommended number

of 12 HBPM readings to assess BP control.25


93

7

The prevalence of hypertension in the current study population varied from 27.6%

(derivation cohort) to 32.0% (validation cohort), which is similar to a previous report of a

random Dutch population of subjects aged 35-60 years, showing a hypertension prevalence

of 33% for men and 20% for women.31 This indicates that the current population seems a good

representation of the general population in terms of hypertension prevalence.

Because morning BP readings were significantly lower than evening readings, it could be

argued that including them both would better reflect an individual’s true BP. However, when

predicting the binary outcome of hypertension status, the second model (1st and 2nd duplicate

HBPM) showed only marginal improvement upon the first model (1st duplicate HBPM), which

would not be commensurate to the burden of taking a second duplicate HBPM.

This study has some limitations. First, although HBPM seems a useful tool for mass screening

of hypertension, we did not investigate whether its use in a screening program leads to better

hypertension awareness and control. Second, we can not know whether the participants fully

complied with the HBPM instructions. They could have, for example, taken BP outside the

standardized condition or have uploaded a wrong BP. However, the same applies to regular

HBPM. Third, in our population 20% failed to record all requested measurements. Because

these subjects were different in education and age compared to those who completed the

HBPM series, this might decrease the external validity of the proposed cut-off values. However,

addition analysis showed no difference in the performance of the cut-off values between both

education and age-categories within the validation cohort (data not shown). Finally, in the

current health program a web-based approach was used in which participants electronically

uploaded their readings. Although 94% of the Dutch households have internet-access,32 not all

health programs currently use this web-based approach. Perhaps future HBPM devices can be

developed which are equipped with a build-in algorithm or a ‘screening mode’ which can be

used in health programs.

Over the years HBPM has proven its value within medical clinics due to its reliable results

and general acceptance by both patients and clinicians. This study shows that HBPM can be

easily and reliably applied as a screening tool for hypertension. In a health screening program,

one duplicate measurement was sufficient to either diagnose or reject the presence of

(uncontrolled) hypertension in more than six out of every 10 participants. Future studies should

elucidate whether HBPM can also be used as a screening tool in primary care and, ultimately,

whether HBPM based screening programs lead to better hypertension awareness and control.

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94

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30. Benetos A, Thomas F, Bean KE, Guize L. Why cardiovascular mortality is higher in treated hypertensives versus subjects of the same age, in the general population. J Hypertens. 2003;21(9):1635-40.

31. Blokstra, A, Vissink, P, Venmans, LMAJ., Holleman, P, Smit, HA, and Verschuren, WMM. Nederland de Maat Genomen. Monitoring van risicofactoren in de algemene bevolking, 2009-2010 (Measuring the Netherlands. A monitoring study of risk factors in the general population, 2009-2010). RIVM-rapport nr. 260152001/2011. Bilthoven, 2011. 2011.

32. http://www.cbs.nl/nl-NL/menu/themas/bedrijven/publicaties/digitale -economie/artikelen/2012-3636-wm.htmn (in Dutch). (13 september 2013 data last accessed)

8A Six Question Screen to Facilitate Primary

Cardiovascular Disease Prevention

Niels V. van der Hoevena,b, Maurice A.J. Niessenb, Erik S.G. Stroesa, Lex Burdorfc,

Roderik A. Kraaijenhagenb, Bert-Jan H. van den Borna

aDepartments of Internal and Vascular Medicine, Academic Medical Center of the University

of Amsterdam, The NetherlandsbNIPED Research Foundation, Amsterdam, The Netherlands

cDepartment of Public Health, Erasmus MC, Rotterdam, The Netherlands

BMC Cardiovasc Disord. 2015 Oct 30;15:140

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ABSTRACT

Background European guidelines on primary prevention of cardiovascular diseases (CVD)

recommend the SCORE risk charts for determining CVD risk, which include blood pressure

and serum cholesterol as risk parameters. To facilitate cost-effective large-scale screening,

we aimed to construct a risk score with ‘non-invasive’ parameters as a first screening step to

identify persons at increased CVD risk requiring further risk assessment.

Methods We used data of Dutch employees from 25 organisations participating in a health

risk assessment between August 2007 and January 2013. Backward multivariate logistic

regression analysis was employed to select non-invasive, independent predictors of high CVD

risk, defined as the 10-year risk of fatal CVD of ≥5% based on the SCORE formula. The total CVD

risk score was calculated as the summed coefficients of the retained variables.

Results Data of 6,189 male participants was used for the development and validation of the

risk score. Age, tobacco use, history of hypertension, alcohol consumption, BMI, and waist

circumference were independent predictors of high CVD risk. Ten-fold cross-validation resulted

in an area under the curve of 0.95 (SE 0.01, 95% confidence interval 0.94-0.96). A cut-off score

≥45 on the CVD risk score yielded a sensitivity of 0.93, and a specificity of 0.85.

Conclusions We developed a simple, non-invasive risk score that accurately identifies persons

at increased CVD risk according to the SCORE formula in a population of working men. The risk

score enables a stepwise approach in large screening programs, strongly reducing the number

of persons that require full risk estimation including blood pressure and cholesterol measures.


99

8

INTRODUCTION

Cardiovascular disease (CVD) is the major cause of premature death in Europe [1, 2]. Despite

the identification of modifiable risk factors such as smoking, sedentary lifestyle, blood pressure

(BP) and dyslipidemia [3], prevention of CVD remains challenging. A complicating factor is that

treatable cardiovascular risk factors can be silently present for many years before detection by

routine check-up or the occurrence of a cardiovascular event.

Early detection of individuals at high CVD risk is the cornerstone of primary prevention. For

estimation of CVD risk current guidelines from the joint task force of the European Association

for Cardiovascular Prevention and Rehabilitation [4] recommend the use of the SCORE (Systemic

COronary Risk Evaluation) risk estimation [5]. Based on age, gender, smoking status, cholesterol

and BP an estimation of the 10-year risk of dying from CVD can be calculated, or derived from

a risk chart. The risk estimation is used to offer the individual tailored health advice, including

behavioral strategies to improve lifestyle and pharmacological interventions aimed at reducing

BP and cholesterol. For practical reasons it is currently recommended to assess cardiovascular

risk in all men over 40 and women over 50 years of age or post-menopausal without CVD

[6]. However, population-wide screening of all persons meeting these criteria is a very costly

and time-consuming effort, making it an unattractive approach for everyday practice [7].

The use of a simple, non-invasive risk score based on current guideline recommendations as

a first step in the screening process might overcome these barriers and facilitate large scale

CVD screening. Such a risk score can be used to select individuals who are likely to be at high

CVD risk after performing a full SCORE risk estimation including BP measurements and blood

sampling, thereby significantly reducing the costs and labour-intensiveness for CVD screening.

Risk scores have been developed to identify patients at increased risk for diabetes [8-10],

kidney disease [11], or a combination of cardiometabolic endpoints [12], but not for CVD risk

estimation according to the SCORE equation.

In the present study, our aim was therefore to facilitate cardiovascular screening in

primary care according to current European guidelines by developing a CVD risk score using

simple, non-invasive parameters to identify persons at high CVD risk according to SCORE risk

estimation. To this end, we used the data of a large web-based health risk assessment (HRA)

carried out in the Netherlands.

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MATERIALS AND METHODS

Participants

The current study was performed as part of a worksite HRA implemented in Dutch organisations

between August 2007 and January 2013. Study participants were employees aged 40-70 years

that completed the HRA within this timeframe. Pregnant woman were excluded from enrolling

in the HRA. Because the prediction tool was aimed at identifying previously undetected persons

at high CVD risk, employees with established CVD or on current treatment for hypertension,

hypercholesterolemia, diabetes or chronic kidney disease were excluded from analysis.

Informed consent was obtained from all participants prior to the study in accordance with the

requirements for identifiable data collection in the Dutch Code of Conduct for Observational

Research (http://www.federa.org/sites/default/files/digital_version_first_part_code_of_

conduct_in_uk_2011_12092012.pdf).

Health Risk Assessment

Details of the HRA have been described previously [13]. In brief, invitations to participate in the

HRA were sent by the human resources department, management, or the safety, health, and

welfare services of the organizations involved. The invitation included a description of the HRA

and informed employees that participation was voluntary and free of charge, that all personal

data would be treated confidentially, and that no individual results would be shared with their

employer or any other party.

Attendees completed a web-based electronic health questionnaire which included

~100 questions covering socio-demographics, personal health history, family risk and the

behavioral domain. This was followed by biometric measurements including length, weight

and waist circumference conducted at the worksite by certified staff. Two BP measurements

were taken after 5 minutes of relaxation with a validated oscillometric device. If both systolic

measurements were below 140 mmHg, the mean was used for analyses. When at least one

of the systolic readings was ≥140 mmHg, participants were instructed to relax for another 30

minutes in a secluded area after which a third BP measurement was taken. The mean of all three

measurements was then used for analyses. At the same visit blood samples were collected

for determination of total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, glucose,

creatinine, and HbA1C. Creatinine was used to calculate the estimated glomerular filtration

rate according to the CKD-EPI formula [14], and is expressed in mL/min per 1.73 m2. A urine

sample was detected to assess the albumin/creatinine ratio (ACR). Increased ACR was defined

as ≥3.5 for male, and ≥2.5 for female persons. A personalized web-based health report and

health plan was automatically generated when all health data were collected after which the

HRA was completed.


101

8

Assessment of CVD mortality risk

CVD mortality risk was assessed according to the SCORE risk estimation. The SCORE risk

estimation predicts the 10-year risk of dying from CVD based on data of 12 large European

cohort studies [5]. Variables in the SCORE risk estimation include age, gender, current smoking

status, systolic BP (mmHg), and total cholesterol (mmol/L) or the total cholesterol/HDL ratio.

In the current study we used total cholesterol to calculate SCORE. Because current guidelines

recommend to offer health advice and consider medical treatment in persons with a predicted

10-year CVD mortality risk of ≥5%, this threshold was used for our primary analysis [1]. The

Netherlands constitutes a low risk region in terms of CVD mortality, therefore the SCORE risk

formula for low risk regions was used [5].

Potential predictor variables

For the development of the screening tool, non-invasively assessed variables with a possible

association with CVD risk were selected from the HRA. This selection was independently

carried out by two physicians (NvdH and DES). Disagreement between the two physicians was

resolved through discussion moderated by a specialist in cardiovascular medicine (BJvdB),

who gave the decisive vote. A total of 23 non-invasively assessed variables were selected as

potential predictors for CVD.

Date of birth, gender, marital status, education and ethnicity were selected from questions

related to socio-economic status. For marital status, participants selected one of six categories.

Education level was defined as the highest education completed and was stratified in three

categories, low (lower general secondary and lower vocational), middle (higher general

secondary, pre-university and intermediate vocational), and high (higher vocational, university

and doctorate). Ethnicity was defined according to parental background. As the majority of

participants were of European descent, the non-European descent answer categories were

merged into “other”.

Self-rated health was assessed, as previously described, by the question “How do you

rate your health in general?”, and categorized in strata ranging from poor to very good [15,

16]. Frequency of tobacco use was stratified in none, occasionally, weekly, or daily. Alcohol

consumption was reported according to the Dutch Municipal Health Service questionnaire,

which records the number of consumed alcohol units per week using a semi-quantitative

scale. Low vegetable and fruit intake was defined as an average consumption of less than 3

tablespoons of vegetables or 2 pieces of fruit per day. Fat intake was estimated based on the daily

consumption of butter, margarine, cheese and other sandwich fillings. Low fish consumption

was defined as less than 1 fish meal per week. In accordance with the INTERHEART study [17],

two items relating to stress at home and stress at work were combined into a general stress

scale and graded as follows: 1) never experienced stress; 2) experienced some periods at home

or work; 3) experienced several periods at home or work; 4) experienced permanent stress at

home or work. Physical activity was self-assessed by one item derived from the Dutch version of

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Chapter 8

102

the International Physical Activity Questionnaire (IPAQ) [18]. Participants entered the number

of weekdays on which they spent at least 30 minutes on moderate to vigorous physical activity.

Moderate physical activity includes activity that increases respiratory rate, but still allows a

person to talk, such as taking a firm walk, swimming, or gardening. Vigorous physical activity

includes activity which increases respiratory rate to a level at which a person cannot easily talk

anymore, such as intensive exercise, running, or cycling with a speed of ≥17 km/h. Distress was

self-assessed with the validated Dutch version of the Extended Kessler distress scale (EK-10)

[19, 20], ranging from 10 (no distress) to 50 (severe distress) with a cut-off score of ≥20. First

degree family history of CVD (diagnosed before age 60), diabetes mellitus and hypertension

was self-reported. History of diabetes mellitus, hypertension, hypercholesterolemia, renal

insufficiency was assessed by asking if participants were ever treated for diabetes, blood

pressure, high cholesterol or renal insufficiency. Subsequently, persons were asked whether

they were still using medication for the selected condition(s). Mental health problems were

considered present if participants received treatment for a mental health disorder, such as

depression or anxiety. Self-reported length and weight were used to calculate body mass

index (BMI) which was categorized into normal (BMI <25 kg/m2), overweight (BMI ≥25 and <30

kg/m2) and obese (BMI ≥30 kg/m2). A waist circumference of ≥94 cm for men and ≥80 cm for

women was considered indicative of the presence of visceral adiposity.


Descriptive statistics were used to present the baseline characteristics of the study population.

Univariate logistic regression was performed to determine the single effects of potential

predictors on a CVD mortality risk of ≥5%. Variables with a P-value <0.10 in the univariate

logistic models were entered in the multivariate model. After stepwise backward elimination

of predictors the final model included variables with a P-value of <0.05. Continuous variables

were categorized to simplify its use. The total CVD risk score was calculated as the summed

coefficients of the retained variables. Area under the curve (AUC) analysis was used as a

measure of overall test performance. An AUC of ≥0.80 is considered indicative of a useful

screening instrument [21]. Sensitivity, specificity, positive predictive value, negative predictive

value, positive likelihood ratio and negative likelihood ratio were calculated at various cut-off

values on the total CVD risk score. All analyses were performed using IBM SPSS version 19 (SPSS

Inc., Chicago, Illinois, USA).

Internal validation

K-fold cross-validation was performed in which 10 multivariate models were developed on

one part of the data (90%) and validated on the independent part (10%). The advantage of

K-fold cross validation is that all the cases in the dataset are consecutively used for both model

development and validation. The average performance of the models was calculated using

AUC. Stepwise backward selection of variables was applied in every training sample [22, 23].


103

8

RESULTS

There were 11,407 employees from 25 organisations who completed the HRA during the

study period. A total of 1,653 participants (14.5%) met one or more exclusion criteria. Baseline

characteristics of the 9,784 included participants are described in Table 1. In total, 4.3% of men

and 0.2% of women had a SCORE estimated CVD risk ≥5%. Because the number of women with

a SCORE ≥5% was too low to produce a model of valid statistical inference (n=8), we proceeded

to develop a prediction model for men [24].

Table 1. Baseline characteristics of study sample (n = 9,784)

Men (n= 6,189) Women (n=3,565)Age (SD) 49.4 6.0 47.1 5.5

Education†

Low (%) 973 15.7 1030 28.9Middle (%) 1988 32.1 1430 40.1High (%) 3228 52.2 1105 31

EthnicityCaucasian (%) 5821 94.1 3187 89.4Other (%) 368 5.9 378 10.6

Tobacco useNone (%) 5294 85.5 2977 83.5At least once a week (%) 469 7.6 251 7.0At least 10 grams per day (%) 426 6.9 337 9.5

Body mass index (SD) 25.7 3.2 24.7 4.1BMI <25 (%) 2738 44.2 2202 61.8Overweight: BMI ≥25 - <30 (%) 2923 47.2 988 27.7Obese: BMI ≥30 (%) 528 8.5 375 10.5

Serum total cholesterol in mmol/l (SD) 5.8 1.0 5.5 1.0History of hypercholesterolemia (%) 179 2.9 58 1.6

Systolic blood pressure in mmHg (SD) 135.9 16.2 126.9 17.0History of hypertension (%) 207 3.3 155 4.3

History of diabetes mellitus (%) 19 0.3 13 0.4

SCORE-low risk 5-10 (%) 235 3.8 7 0.1 SCORE-low risk >10 (%) 31 0.5 1 0.0

History of renal insuffi ciency (%) 72 1.2 29 0.8

eGFR (mL/min per 1.73m2) ≥90 (%) 3363 54.3 1822 51.160-90 (%) 2678 43.3 1612 45.245-59 (%) 143 2.3 127 3.630-44 (%) 5 0.1 4 0.1<30 (%) 0 0 0 0

Increased ACR (%) 109 1.8 70 2.0

†Education level. Low, lower general secondary/lower vocational; Middle, higher general secondary/pre-university/ intermediate vocational; High, Higher vocational/university/doctorate; eGFR, estimated glomerular filtration rate based on the chronic kidney disease epidemiology collaboration (CKD-EPI) formula; ACR, albumin/creatinine ratio in urine; Increased values defined as ≥3.5 for male and ≥2.5 for female persons.

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Chapter 8

104

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105

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Chapter 8

106

Model development

In univariate analysis, 12 of the 23 selected variables were predictive of the SCORE estimated

CVD risk ≥5% threshold (Table 2) and subsequently entered in the multivariate analysis. Table

3 shows the six variables retained in the final model. Age, tobacco use, self-reported history of

hypertension (without current treatment), alcohol consumption, BMI and abdominal obesity

independently predicted a ≥5% SCORE risk. To facilitate practical use of the CVD risk score,

β’s of these variables were multiplied and rounded to the nearest integer. A multiplication

factor of 7 was chosen to sustain sufficient discriminative power between different predictor

variables, resulting in a total CVD risk score ranging from 0 to 96.

Table 3. Multivariate regression of high cardiovascular disease risk in men for sociodemographic, lifestyle, and biometric variables (n=6,189)

SCORE risk ≥5%*

95% C.I.

Odds Ratio

Lower Upper β Risk Score¶

Age 40-49 ¥ 0

50-54 15.517 4.644 51.844 2.742 19

55-59 206.816 64.758 660.507 5.332 37

60-70 1168.532 354.895 3847.520 7.064 49

Tobacco use None ¥ 0

At least once a week 14.232 9.977 20.300 2.655 19

Alcohol <1 units per week ¥ 0

consumption 1-7 units per week 1.142 .688 1.895 .132 1

8-14 units per week 1.278 .753 2.170 .246 2

15-21 units per week 2.035 1.135 3.648 .710 5

≥22 units per week 2.376 1.295 4.360 .866 6

Body mass index

Normal weight: BMI <25 kg/m² ¥ 0

(BMI) Overweight: BMI ≥ 25 - <30 kg/m² 1.687 1.130 2.520 .523 4

Obese: BMI ≥30 kg/m² 1.932 1.043 3.579 .659 5

Waist circumference

<94 cm 0

≥94 cm 1.849 1.238 2.760 .615 4

History of hypertension

No 0

Yes 6.158 3.551 10.680 1.818 13

*Based on the SCORE equation for countries with low cardiovascular risk. ¥ indicates reference category. ¶The risk score is produced by multiplying β’s by 7 and rounding them to the nearest integer.


107

8

Model validation

Ten-fold cross-validation resulted in an AUC of 0.95 (95% CI [0.94- 0.95]), demonstrating good

discriminatory power. Diagnostic classification accuracy of the risk score at multiple cut-off

values is shown in Table 4. To illustrate the influence of individual parameters on the outcome

of the model several case examples are depicted in Table 5 using a cut-off value of ≥45. At this

cut-off, 18% of the study population has an estimated CVD risk ≥5%.

Table 4. Diagnostic classification accuracy of predicting high CVD risk at different cut-off values

TP FN FP TN Sensitivity Specifi city PPV LR + LR-

Cut-off ≥40 254 12 1181 4742 95.5% 80.1% 17.7% 4.8 0.1

Cut-off ≥45 247 19 888 5035 92.9% 85.0% 21.8% 6.2 0.1

Cut-off ≥50 198 68 438 5485 74.4% 92.6% 31.1% 10.1 0.3

CVD, cardiovascular disease; TP, true positive; FN, false negative; FP, false positive; TN, true negative; PPV, positive predictive value; LR+, positive likelihood ratio; LR-, negative likelihood ratio.

Table 5. Case examples of the cardiovascular disease risk screening tool using a cut-off of ≥45 points

Example nr.

Age Tobacco use

Alcohol consumption

BMI Waist circumference

History of hypertension

Risk Score

Estimated SCORE ≥5%?*

1 52 No 8-14 ≥25- <30 <94cm No 25 No

2 52 Yes 15-21 ≥25- <30 ≥94cm No 51 Yes

3 57 No 8-14 ≥25- <30 <94cm No 43 No

4 57 Yes <1 <25 <94cm No 56 Yes

5 47 Yes 15-21 ≥30 ≥94cm No 33 No

6 57 No 1-7 <25 <94cm Yes 51 Yes

7 62 No <1 <25 <94 cm No 49 Yes

BMI, body mass index. *based on a cut-off of ≥45 points

DISCUSSION

We developed and validated a simple six-item CVD risk score that can be used as a first step in

identifying male employees at high CVD risk based on current European guidelines using the

SCORE risk estimation. At a cut-off of ≥45, only 18% of screened persons where qualified as

high CVD risk requiring further risk assessment, with a false-negative rate of 7%. Because of the

low prevalence of women with increased cardiovascular risk before age 65, screening women

for CVD in the context of a worksite HRA does not seem to be efficacious.

Our proposed CVD risk score is developed to facilitate large scale CVD screening programs

based on the current guidelines by offering an easy and highly accurate first step in the

screening process [4]. Instead of applying full CVD screening with BP measurement and blood

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sampling to all men aged 40 years and above, the CVD risk score can be used to preselect

persons who require a total risk estimation. Applying this stepped approach means that BP

and cholesterol measurement is required in only a small fraction of the screened population.

The CVD risk score therefore seems a useful tool in reducing the costs, means and time needed

to perform large scale screening. Choosing the cut-off value on the CVD risk score is a matter

of policy and dependent on the acceptable percentage of false negatives and false positives

in a particular screening setting. The high accuracy of our screening tool, however, seems to

provide an acceptable trade-off between both rates.

Although we emphasize that ideally – as recommended in current guidelines - a full risk

estimation is performed in every eligible person, most practices lack the time or finances to do

so [7]. In such situations our CVD risk score can be employed instead as a first step in the risk

assessment. A possible pitfall of using the CVD risk score compared to the original SCORE risk

estimate, is that individuals who are at increased CVD risk because of isolated highly elevated

BP or cholesterol could be missed as both are not measured. Other important cardiovascular

risk factors such as diabetes or chronic kidney disease are not included in the SCORE formula,

and persons with these risk factors could also be missed. However, this only applies to

persons with risk factors that are untreated, as we excluded persons on current treatment

for hypertension, hypercholesterolemia, diabetes, or chronic kidney disease because they

are already at increased CVD risk. The number of persons with untreated risk factors is likely

to be small in a relatively healthy working population. This is supported by the fact that in

our population the number of subjects with moderate to severe renal function (eGFR<45 ml/

min/1.73m2) was only 0.1%. Nonetheless, because the proposed screening strategy is based

on the identification of patients at risk for CVD with a simple questionnaire persons with these

risk factors could remain unidentified. This should be taken in consideration when using the

CVD risk score.

The performance of the original SCORE risk ranges from reasonable to good (AUC 0.71-

0.84) [5]. Our CVD risk score is likely to resemble this performance, given its high accuracy in

predicting the 10-year mortality risk of ≥5%. Age and tobacco conferred the largest predictive

value in our proposed risk score which is not surprising as they are both included in the SCORE

risk assessment. Next to these variables, alcohol consumption, BMI, waist circumference and

a history of hypertension (but currently untreated) independently predicted ≥5% SCORE

risk. It is likely that they act as a surrogate for the remaining SCORE variables, systolic BP and

total cholesterol. High BMI and a large waist circumference often coincide with a high BP or

dyslipidemia as part of the metabolic syndrome [25]. A history of hypertension indicates that

the person has or is prone to develop hypertension. Although alcohol consumption might even

be protective for development of the metabolic syndrome [26], there is a positive correlation

between alcohol consumption and increased BP [27]. The contribution to the CVD risk score of

these four variables is smaller than age and tobacco, but still these variables can be decisive in


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determining the screening outcome. In addition, these variables can also be used for a tailored

lifestyle advice.

The low prevalence of women that reached the ≥5% SCORE threshold in the current study

population is in line with findings of a previous study comprising two Dutch population

cohorts of similar age as the current population [28], where 0.1% of the women and 3.1% of the

men reached the ≥5% SCORE threshold. These findings are not surprising given that the ≥5%

threshold for low risk countries is not reached for non-smoking women until the age of 65, or

60 for smoking women, irrespective of BP or cholesterol [5]. Based on these numbers screening

for CVD in women aged <60 years seems not useful from a worksite health care perspective in

low-risk countries.

There are several limitations to our study that need further consideration. First, the proposed

CVD risk score is developed and validated in a cohort of employees, which possibly limits the

external validity of the screening tool when used in the general population. Nonetheless, the

workplace provides an ideal setting for CVD risk screening as most men in the targeted age

range (40-70) are part of the working population, and because it can facilitate the creation

of a health-conscious environment [29]. Second, we did not include sedentary lifestyle in the

questionnaire of our HRA. Sedentary lifestyle is one of the major risk factors for CVD [30], and

inclusion of this non-invasive parameter to our questionnaire could have further increased

the accuracy of our CVD risk score. Third, we have no data on CVD outcome in our population.

As the aim of the study was to build a model to identify subjects with high SCORE risk, in line

with current guideline recommendations, these data were not required. It would be, however,

interesting in future research to validate the model on actual CVD outcome. Fourth, the

proposed CVD risk score can only be used in countries with low CVD population risk. However,

the methods described in the current study can also be used to develop a similar model for

high-risk countries. Finally, the proposed CVD risk score includes self-reported length and

weight, which could lead to a slight underestimation of the calculated BMI [31].

In conclusion, we used the data of a health risk assessment conducted in 25 Dutch

organisations to construct a proposal for a simple six-item CVD risk score to identify individuals

at increased CVD risk as defined by the SCORE risk estimation. The present risk score can be

offered online as a simple, quick and inexpensive first step in the identification of persons at

high cardiovascular risk, who subsequently qualify for further risk profiling according to the

SCORE formula, including BP and cholesterol measures. We designed and validated our tool

in population of workers from a European country at low CVD risk. Future studies should

investigate whether the newly developed risk score can also be applied for other populations.

Studies implementing our screening tool are warranted to evaluate the cost-effectiveness of a

stepped approach for CVD risk screening as part of primary prevention strategies.

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Acknowledgements

We thank D.W. Eeftinck Schattenkerk, MD for his help with the selection of the predictor

variables.


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11. Kshirsagar AV, Bang H, Bomback AS, Vupputuri S, Shoham DA, Kern LM, et al. A simple algorithm to predict incident kidney disease. Arch Intern Med 2008; 168(22):2466-2473.

12. Alssema M, Newson RS, Bakker SJ, Stehouwer CD, Heymans MW, Nijpels G, et al. One risk assessment tool for cardiovascular disease, type 2 diabetes, and chronic kidney disease. Diabetes Care 2012; 35(4):741-748.

13. Niessen MAJ, Kraaijenhagen RA, Dijkgraaf MG, Van PD, Van Kalken CK, Peek N. Impact of a Web-based worksite health promotion program on absenteeism. J Occup Environ Med 2012; 54(4):404-408.

14. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, III, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009; 150(9):604-612.

15. Fayers PM, Sprangers MA. Understanding self-rated health. Lancet 2002; 359(9302):187-188.

16. Mavaddat N, Kinmonth AL, Sanderson S, Surtees P, Bingham S, Khaw KT. What determines Self-Rated Health (SRH)? A cross-sectional study of SF-36 health domains in the EPIC-Norfolk cohort. J Epidemiol Community Health 2011; 65(9):800-806.

17. Rosengren A, Hawken S, Ounpuu S, Sliwa K, Zubaid M, Almahmeed WA, et al. Association of psychosocial risk factors with risk of acute myocardial infarction in 11119 cases and 13648 controls from 52 countries (the INTERHEART study): case-control study. Lancet 2004; 364(9438):953-962.

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24. Bagley SC, White H, Golomb BA. Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain. J Clin Epidemiol 2001; 54(10):979-985.

25. Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet 2005; 365(9468):1415-1428.

26. Freiberg MS, Cabral HJ, Heeren TC, Vasan RS, Curtis ER. Alcohol consumption and the prevalence of the Metabolic Syndrome in the US.: a cross-sectional analysis of data from the Third National Health and Nutrition Examination Survey. Diabetes Care 2004; 27(12):2954-2959.

27. Puddey IB, Beilin LJ, Rakie V. Alcohol, hypertension and the cardiovascular system: a critical appraisal. Addicition Biol 1997; 2(2):159-170.

28. van D, I, Kromhout D, Geleijnse JM, Boer JM, Verschuren WM. Evaluation of cardiovascular risk predicted by different SCORE equations: the Netherlands as an example. Eur J Cardiovasc Prev Rehabil 2010, 17(2):244-249.

29. Niessen MA, Laan EL, Robroek SJ, Essink-Bot ML, Peek N, Kraaijenhagen RA, et al. Determinants of participation in a web-based health risk assessment and consequences for health promotion programs. J Med Internet Res 2013; 15(8):e151.

30. Warren TY, Barry V, Hooker SP, Sui X, Church TS, Blair SN. Sedentary behaviors increase risk of cardiovascular disease mortality in men. Med Sci Sports Exerc 2010; 42(5):879-885.

31. Nyholm M, Gullberg B, Merlo J, Lundqvist-Persson C, Rastam L, Lindblad U. The validity of obesity based on self-reported weight and height: Implications for population studies. Obesity (Silver Spring) 2007; 15(1):197-208.

9The Ankle Central Aortic Index is More Closely Associated

with Cardiovascular Risk than the Ankle Brachial Index -

the HELIUS Study

Niels V. van der Hoevena, MD; Stephanie Klein Ikkinka; Marieke B. Snijderb MD PhD,

Ron J.G. Petersc, MD PhD; Bert-Jan H. van den Borna, MD PhD

aDepartments of Internal and Vascular Medicine, Academic Medical Center of the

University of Amsterdam, the Netherlands.bDepartment of Public Health, Academic Medical Center of the University of Amsterdam,

the Netherlands.cDepartment of Cardiology, Academic Medical Center of the University of Amsterdam,

the Netherlands.

Submitted

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ABSTRACT

Background Current evidence suggests that central systolic blood pressure (BP) is superior

in predicting cardiovascular disease (CVD) compared to brachial BP. In line, the ratio between

systolic BP at the ankle and in the aorta (ankle-central BP index [ACI]) may be superior over the

ankle-brachial index (ABI) in predicting CVD.

Objectives First, we examined the determinants of ABI and ACI in a large multi-ethnic population

study, and secondly, we assessed the association of ABI and ACI with CVD among various ethnic

groups with different cardiovascular risk.

Method We used a cross-sectional design to compare the association of ABI and ACI with

cardiovascular risk and prevalent CVD in 13,483 participants of the HELIUS study in Amsterdam,

the Netherlands. Linear and logistic regression analysis were used. Analyses were stratified for

age because the age-related distribution for ABI was curve-linear in subjects ≤50 years.

Results Both ABI and ACI were lower in all ethnic minority groups compared to the Dutch

reference population after adjustment for conventional cardiovascular risk factors, except for ABI

in Moroccan subjects aged >50 years. In persons aged ≤50 years, age, male gender, hypertension,

body mass index and waist-to-hip ratio were all associated with lower ACI, whereas age and

hypertension had a positive association with ABI. A higher ACI was associated with lower CVD

prevalence (odds ratio [OR] 0.45, 95% confidence interval [CI] 0.30 to 0.67, P<0.001), while higher

ABI was not (OR 0.94, 95% CI 0.58 to 1.54, P=0.81). In persons >50 years, age and diabetes were

only associated with ACI, whereas BMI was only associated with ABI. Both lower ABI and ACI were

associated with CVD.

Conclusions ACI may be a better predictor for CVD, especially in subjects aged ≤50, and may

help identify groups at increased CVD risk.


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INTRODUCTION

Cardiovascular events often occur in individuals without known pre-existing cardiovascular

disease (CVD) (1), and are in most cases related to the presence of atherosclerosis (2;3). A simple

method for non-invasive estimation of the atherosclerotic burden is to measure the systolic

blood pressure (BP) difference between the lower extremities and the upper arm at the level of

the brachial artery, known as the ankle-brachial index (ABI) (4). A reduction in systolic BP at the

ankle, relative to the blood pressure at the brachial artery, results in a lower ABI and is believed

to reflect the degree of atherosclerosis in the lower extremities (5). ABI correlates well with

angiographically verified peripheral artery lesion s (6), is a good predictor of functional disabili ty

(7), and has been shown to predict CVD in different populati ons (8-10). Despite the established

relationship between low ABI and CVD, the use of ABI for screening of asymptomatic subjects

is not recommended (11), mainly because ABI does not significantly improve risk prediction

upon existing models of cardiovascular risk predic tion (12). However, current evidence suggests

that central BP may be a better predictor for future CVD than brachi al BP (13-16). In line, we

hypothesized that this may also apply to the ratio between BP at the ankle and in the aorta, or

‘ankle-central aortic index’ (ACI), compared to ABI.

In the present study we compared the determinants of ABI and ACI in a large population-

based study including several ethnic groups with varying cardiovascular risk. In addition, we

explored the association of ABI and ACI with the self-reported prevalence of CVD.

METHODS

Study Population

The present study is performed using baseline data of the Healthy Life in an Urban Setting

(HELIUS) study. The HELIUS protocol, aims and design have been described before (17). In brief,

the study was designed as a prospective cohort study with the aim to unravel the causes of

the unequal burden of disease across ethnic groups, and ultimately enable the improvement

of health care and prevention strategies. Baseline data collection took place in 2011-2015. The

study included subjects aged 18 to 70 years, randomly selected, stratified by ethnic origin,

through the municipally registry of Amsterdam, the Netherlands. The study is an initiative of the

Academic Medical Center (AMC) of the University of Amsterdam and the Public Health Service

of Amsterdam. Country of birth was used as a basis for identifying ethnic groups. A participant

was considered of non-Dutch ethnic origin when fulfilling one of the following two criteria: 1)

he or she was born abroad and has at least one parent born abroad (first generation); or 2) he or

she was born in the Netherlands but both parents were born abroad (second generation) (18).

Non-Dutch participants were classified as Surinamese, Turkish, Moroccan, or Ghanaian. Of the

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Surinamese immigrants in the Netherlands, approximately 80% are either Creole (African origin)

or Hindustani (South-Asian origin). Both subgroups were classified according to self-reported

ethnic origin. To examine ethnic differences in ABI and ACI we included all participants with a

valid ABI and ACI measurement. The study protocols were approved by the AMC Ethical Review

Board.

Ankle-Brachial Index (ABI) and Ankle-Central aortic Index (ACI)

ABI measurements were performed in supine position. Systolic BP was measured twice in the right

and left brachial artery and twice in the right and left posterior tibial arteries using a validated

oscillometric device (WatchBP Office ABI, Microlife, Widnau) (19). The right ABI was obtained by

dividing the mean systolic BP in the right ankle by the mean systolic BP in the right arm, and the

left ABI was calculated by dividing the mean systolic BP in the left ankle by the systolic BP in the

left arm. If the difference in systolic BP between both arms was <10 mmHg, the lower of two ABIs

was used for analysis. If this difference was ≥10 mmHg, the highest brachial systolic BP was used

and divided by the mean systolic BP in the left and right ankle to calculate the left and right ABI.

The lowest of these two ABI’s was used for analysis.

Central systolic BP was assessed using the Arteriograph system (Tensiomed Kft., Budapest,

Hungary) as validated, described, and illustrated prev iously (20;21). The Arteriograph is an

operator-independent non-invasive device, which uses oscillometric pressure curves registered

by an upper arm BP cuff to determine central systolic BP. Measurements were performed in

duplicate in supine position after 10 minutes rest. The ACI was calculated by taking the mean

systolic BP at both lower extremities and divide it by the mean central systolic BP. The lowest of

the left and right ACI measurement was used for analysis.

Anthropometry

All anthropometric measurements were performed in duplicate and the mean was used in the

analyses. Weight (kg) was measured in barefoot subjects wearing light clothes only, using a

digital scale (SECA 877). Height (cm) was measured without shoes using a stadiometer (SECA

217). Waist circumference (cm) was measured using a tape measure at the level midway between

the lowest rib margin and the iliac crest, and hip circumference (cm) was measured at the widest

level over the trochanters. Body mass index (BMI) was calculated as weight (kg) divided by height

squared (m2), and waist-to-hip ratio (WHR) was calculated as waist circumference divided by

hip circumference. Information on demographics, smoking behaviour, and history of diseases

was obtained by questionnaire. The Rose questionnaire was used to determine established CVD

(22). Participants were asked to bring their prescribed medications to the research location,

which were coded according to the Anatomical Therapeutic Chemical (ATC) classification. Blood

pressure (mmHg) was measured while seated using a validated oscillometric BP device (Microlife

WatchBP Home, Microlife AG, Heerbrugg, Switzerland). The average BP of two measurements

taken at the left arm after at least 5 minutes of rest was used.


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Laboratory measurements

Fasting blood samples were drawn and plasma samples were used to determine the

concentration of glucose by spectrophotometry, using hexokinase as primary enzyme (Roche

Diagnostics, Japan). Total cholesterol, triglycerides and high-density lipoprotein (HDL) cholesterol

were determined by colorimetric spectrophotometry (Roche Diagnostics, Japan). Low-density

lipoprotein cholesterol (LDL) was calculated according to the Friedewald formula (23).

Definition of cardiovascular disease and risk factors

The presence of CVD was defined as self-reported cerebrovascular event or myocardial infarction,

or having angina pectoris or a possible myocardial infarction according to the Rose questionnaire.

Participants who reported that they smoked were considered current smokers. Participants with

self-reported diabetes, a fasting glucose level ≥7.0 mmol/l or the use of anti-diabetic medication

were defined as having diabetes. Dyslipidemia was defined by a total cholesterol to high-density-

lipoprotein cholesterol ratio ≥5 or the use of lipid-lowering agents. Participants were considered

hypertensive if systolic BP was ≥140 mmHg, diastolic BP was ≥90 mmHg, with self-reported

hypertension, or when using BP lowering medication.

Statistical analyses

Baseline variables were expressed as mean and their standard deviation for variables with

a normal distribution, and as median and interquartile range for variables with a skewed

distribution. Categorical variables were expressed as actual numbers and percentages. Two linear

regression analyses were performed, one with ABI and one with ACI as the dependent variable,

both with age, gender, ethnicity, BMI, WHR, smoking, hypertension, diabetes and dyslipidemia

as covariates. As there was a non-linear relation between ABI and age, we performed separate

regression analyses for younger subjects (≤50 years) and middle-aged and older subjects (aged

>50 years). For a reliable comparison between ABI and ACI, the same age categories were used

for the analyses of ACI. Logistic regression analysis was performed to assess the relationship

between ABI and ACI and prevalent CVD. A P value of < .05 was considered as significant. Statistical

analyses were performed with IBM SPSS (version 20.0). Spines with four degrees of freedom were

created with R statistical package version 2.4.2 (R Foundation for Statistical Computing, Vienna,

Austria) to depict the relation of age with ABI and ACI, stratified by ethnicity.

RESULTS

At baseline, a total of 22,165 participants were included in the HELIUS study. A total of 233

subjects from Javanese-Surinamese origin were excluded because the number was too small

for reliable statistical analyses. In addition, 315 subjects were excluded because ethnicity was

unknown or could not be categorised in one of the defined ethnic groups. There were 8,134

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subjects on which data on ACI or ABI were incomplete, leaving 13,483 (61%) subjects aged 44±13

years (46 % men) for analyses. ABI and ACI measurements were performed until February 2015,

and most subjects with missing ABI or ACI values were included after this date. Figure 1 shows a

flowchart of participants eligible for inclusion by ethnicity.

In total 2,506 Dutch, 1,880 South-Asian Surinamese, 2,548 African Surinamese, 1,894

Ghanaian, 2,232 Turkish, and 2,423 Moroccan subjects were included. Baseline characteristics of

the study participants stratified by ethnicity are shown in Table 1. Taking Dutch subjects as a

reference, Turkish and Moroccan subjects were younger, whereas African Surinamese subjects

were older. Dutch subjects had a lower BMI and WHR than subjects from other ethnic groups.

Systolic BP was lowest in Dutch and Moroccan subjects, and highest in Ghanaian and African

Surinamese subjects. Hypertension was most prevalent in African (Ghanaian and African

Surinamese) subjects, and least prevalent in Moroccan subjects. In addition, there was a lower

prevalence of Dutch subjects with diabetes compared to other ethnic groups. Dutch subjects

had the highest ABI and ACI values, whereas South-Asians and African participants had the

lowest ABI and ACI values.

Table 1. Baseline characteristics of the study population (n=13,483)

Dutch

(n=2,506)

South-Asian Surinamese (n=1,880)

African Surinamese(n=2,548)

Ghanaian

(n=1,894)

Turkish

(n=2,232)

Moroccan

(n=2,423)

Age, years 45±14 46±14 48±13 45±11 40±12 40±13Men, n (%) 1238 (49%) 972 (52%) 1070 (42%) 818 (43%) 1124 (50%) 1009 (42%)BMI, kg/m² 25±4 26±4 27±5 28±5 28±5 27±5Waist/hip ratio 0.88±0.09 0.93±0.09 0.90±0.08 0.91±0.08 0.91±0.09 0.89±0.09Hypertension, n (%) 711 (28%) 818 (44%) 1260 (49%) 1072 (57%) 660 (30%) 597 (25%)Total cholesterol/HDL 3.5±1.2 4.0±1.3 3.4±1.1 3.2±1.0 4.1±1.4 3.6±1.3Dyslipidemia 468 (19%) 706 (38%) 444 (17%) 256 (14%) 675 (30%) 457 (19%)Glucose, mmol/l 5.3±0.7 5.8±1.5 5.5±1.3 5.4±1.2 5.5±1.2 5.5±1.4Diabetes, n (%) 83 (3%) 352 (19%) 275 (11%) 232 (12%) 221 (10%) 273 (11%)Current smokers, n (%) 609 (24%) 561 (30%) 832 (33%) 93 (5%) 776 (35%) 340 (14%)bSBP, mmHg 125±16 130±18 132±18 136±19 124±16 123±16aSBP, mmHg 145±21 143±23 151±23 152±24 140±21 140±20cSBP, mmHg 118±20 126±22 129±23 134±24 118±19 115±18ABI 1.18±0.12 1.12±0.12 1.15±0.12 1.13 ± 0.12 1.13±0.13 1.15±0.12<0.90 (%) 58 (2.3%) 97 (5.2%) 108 (4.2%) 85 (4.5%) 127 (5.7%) 83 (3.4%)≥1.40 (%) 31 (1.2%) 7 (0.4%) 14 (0.5%) 5 (0.3%) 9 (0.4%) 10 (0.4%)ACI 1.24 ± 0.15 1.14±0.14 1.17±0.14 1.14 ± 0.14 1.19±0.15 1.22±0.14 <0.90 (%) 53 (2.1%) 83 (4.4%) 93 (3.6%) 74 (3.9%) 88 (3.9%) 48 (2.0%) ≥1.40 (%) 311 (12.4%) 47 (2.5%) 117 (4.6%) 47 (2.5%) 147 (6.6%) 205 (8.5%)CVD (%) 207 (8%) 463 (25%) 414 (16%) 282 (15%) 457 (25%) 469 (20%)

BMI, body mass index; HDL, high-density lipoprotein; ABI, ankle brachial blood pressure index; bSBP, systolic blood pressure measured at the brachial artery; aSBP, systolic blood pressure measured at the ankle used to calculate ABI; cSBP; central aortic systolic blood pressure, CVD, cardiovascular diseases.


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Figure 1. Flowchart of study participants.

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groups, ABI increased with age up to 50 years. Between 50 and 59 years there was a heterogeneous

distribution of ABI, and after 60 years ABI decreased except in Ghanaian and Moroccan subjects.

In contrast, ACI showed a step-wise decrease in all subsequent age categories in all ethnic

groups, except for Ghanaian and Moroccan subjects in the highest age category. A similar trend

was observed for males and females (data not shown).

•

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Figure 2. Distribution of systolic ABI and ACI stratified by age and ethnicity. The upper panel shows the distribution for the systolic ankle-brachial blood pressure index (ABI), the lower panel shows the distribution for the systolic ankle-central blood pressure index (ACI).A

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DutchSouth−Asian SurinameseAfrican SurinameseGhanaianTurkishMaroccan

B

Age (years)

Sys

tolic

ank

le−

cent

ral B

P in

dex

1.10

1.15

1.20

1.25

1.30

20 30 40 50 60 70

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DutchSouth−Asian SurinameseAfrican SurinameseGhanaianTurkishMaroccan


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The results of the regression analyses of several determinants in relation to ABI and ACI

stratified by age shown in Table 2 (age ≤50 years) and Table 3 (Age >50 years). In persons aged

≤50 years, age, male gender, hypertension, BMI and WHR were all associated with lower ACI,

whereas age and hypertension had a positive association with ABI. Both ABI and ACI were lower

in all ethnic minority groups compared to the Dutch reference population, except for ABI in

Moroccan subjects aged >50. For subjects aged >50, age and diabetes were only associated with

ACI, whereas only BMI was associated with ABI. Compared to the Dutch reference population, ABI

and ACI were lower in all other ethnic groups, except for ABI in Moroccan subjects.

Table 2. Associations of potential determinants with systolic ankle-brachial index (ABI) and with systolic ankle-central blood pressure index (ACI) in subjects aged ≤50 years

Covariate ABI Beta (95% CI)

P ACI Beta (95% CI)

P

Age (10 years) 0.022 (0.019, 0.025) <0.001 -0.025 (-0.028, -0.021) <0.001

Male Gender -0.001 (-0.005, 0.008) 0.672 0.058 (0.050, 0.065) <0.001

Ethnicity

Dutch Reference Reference

South-Asian Surinamese -0.047 (-0.057, -0.038) <0.001 -0.096 (-0.107, -0.086) <0.001

African Surinamese -0.025 (-0.034, -0.016) <0.001 -0.053 (-0.064, -0.043) <0.001

Ghanaian -0.034 (-0.043, -0.025) <0.001 -0.090 (-0.101, -0.080) <0.001

Turkish -0.030 (-0.038, -0.021) <0.001 -0.063 (-0.073, -0.053) <0.001

Moroccan -0.010 (-0.018, -0.002) 0.014 -0.028 (-0.037, -0.018) <0.001

BMI -0.004 (-0.005, -0.004) <0.001 -0.002 (-0.002, -0.001) <0.001

Waist-to-hip-ratio 0.097 (0.051, 0.143) <0.001 0.064 (0.011, 0.118) 0.019

Current smoker 0.004 (-0.02, 0.010) 0.148 0.0003 (-0.007, 0.007) 0.926

Hypertension 0.012 (0.006, 0.019) <0.001 -0.030 (-0.038, -0.023) <0.001

Diabetes 0.003 (-0.009, 0.015) 0.637 0.009 (-0.011, 0.019) 0.499

Dyslipidemia -0.007 (-0.015, 0.0002) 0.056 -0.007 (-0.016, 0.002) 0.113

BMI, body mass index. Estimates are adjusted for age, gender, ethnicity, BMI, waist-to-hip-ratio, smoking, hypertension, diabetes and dyslipidemia.

Table 4 shows the association of ABI and ACI with prevalent CVD. In subjects ≤50 years, higher

ACI was associated with lower CVD prevalence, while higher ABI was not. In subjects aged >50

years, both lower ABI and lower ACI were associated with a higher prevalence of CVD. Analyses

with and without subjects on antihypertensive medication showed the same trends (data not

shown).

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Table 3. Associations potential determinants of systolic ankle-brachial index (ABI) and with systolic ankle-central blood pressure index (ACI) in subjects aged >50 years

Covariate ABI Beta (95% CI)

P ACI Beta (95% CI)

P

Age (10 years) -0.002 (-0.010, 0.005) 0.516 -0.024 (-0.032, -0.016) <0.001

Male Gender 0.027 (0.018, 0.036) <0.001 0.074 (0.065, 0.084) <0.001

Ethnicity

Dutch Reference Reference

South-Asian Surinamese -0.061 (-0.073, -0.049) <0.001 -0.088 (-0.101, -0.074) <0.001

Afro-Caribbean Surinamese -0.028 (-0.038, -0.017) <0.001 -0.034 (-0.046, -0.023) <0.001

Ghanaian -0.056 (-0.068, -0.043) <0.001 -0.084 (-0.098, -0.070) <0.001

Turkish -0.033 (-0.047, -0.019) <0.001 -0.061 (-0.077, -0.045) <0.001

Moroccan -0.011 (-0.023, 0.002) 0.093 -0.027 (-0.041, -0.013) <0.001

BMI -0.004 (-0.005, -0.003) <0.001 -0.001 (-0.002, 0.0001) 0.087

Waist-to-hip-ratio 0.048 (-0.010, 0.107) 0.108 0.053 (-0.013, 0.119) 0.113

Current smoker -0.026 (-0.034, 0.017) <0.001 -0.029 (-0.039, -0.019) <0.001

Hypertension -0.014 (-0.021, -0.006) <0.001 -0.043 (-0.052, -0.035) <0.001

Diabetes 0.004 (-0.005, 0.013) 0.395 0.012 (0.002, 0.022) 0.021

Dyslipidemia -0.008 (-0.016, 0.001) 0.076 -0.005 (-0.014, 0.04) 0.257

BMI, body mass index. Estimates are adjusted for age, gender, ethnicity, BMI, waist-to-hip-ratio, smoking, hypertension, diabetes and dyslipidemia.

Table 4. Association of ABI and ACI with CVD in young (≤50 years) and middle-aged and older (>50 years) subjects.

ABI ACI

Age (years)

Odds ratio 95% CI P value Odds ratio 95% CI P value

≤50 0.94 0.58, 1.54 0.81 0.45 0.30, 0.67 <0.001

>50 0.20 0.12, 0.33 <0.001 0.19 0.12, 0.30 <0.001

ABI, systolic ankle-brachial index; ACI, systolic ankle-central blood pressure index

DISCUSSION

In the present cross-sectional study, we show that ACI is more closely associated with

cardiovascular risk and prevalent CVD compared to ABI, especially in younger subjects.

Both ABI and ACI were lower in all ethnic minority groups compared to the Dutch reference

population, also after adjustment for conventional CVD risk factors, except for ABI in Moroccan

subjects aged >50 years. ACI had an overall stronger association with cardiovascular risk factors,

particularly smoking and hypertension, compared to ABI. We also found a hitherto unreported

and counterintuitive age-related increase in ABI in subjects aged 18-50 that was independent


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of ethnicity and gender. In contrast, ACI was lower across all age categories within each ethnic

group in both males and females. These results suggest that the index of the systolic ankle and

aortic BP might be superior for predicting incident CVD over ABI, especially in younger subjects.

There is ample evidence that ABI is a predictor for cardiovascular events and mo rtality (8-

10), also in multi-ethnic pop ulations (24). In the Atherosclerosis Risk in Communities (ARIC)

study, the age-adjusted prevalence of a low ABI (≤0.90), was higher in African-American subjects

compared to white subjects (25). These differences could at least be partially explained by a

higher prevalence of diabetes and hypertension in African-Americans. The Multi-Ethnic Study

of Atherosclerosis (MESA) that included subjects free of CVD at baseline showed that after

adjustment for cardiovascular risk factors, African-American subjects had a lower ABI compared

to non-Hispanic whit e subjects (26). In the National Health and Nutrition Examination surveys

(NHANES), African-American subjects also had a lower ABI compared to non-Hispanic white

subjects which remained after adjustment of cardiovascular risk factors (27). In addition to these

population studies from the US, our study shows that African origin populations from Suriname

and Ghana had a lower ABI compared to the Dutch reference population, also after adjustment

for conventional cardiovascular risk factors.

Little is known about the distribution of ABI in younger subjects, and about ABI in other

populations. Our study is unique in showing that significant differences in ABI between different

ethnic groups already occur in individuals ≤50 years of age and remain present after adjustment

for conventional risk factors for CVD. In addition to the lower ABI in African origin populations,

we found that South-Asian Surinamese had the lowest ABI. It is known, that coronary heart

disease is more prevalent in people originating from the South-Asian s ubcontinent (28), and that

subjects from African origin are at higher risk for developing stroke (29), which is consistent with

our findings and the belief that ethnic differences in ABI reflect differences in the atherosclerotic

burden. However, we also show that Turkish and Moroccan descent populations had a lower ABI

compared to the Dutch reference population. This contrasts previous findings of the lower risk

for ischemic heart disease and stroke in these populations in the Netherlands (30;31).

The superiority of ACI as predictor for cardiovascular risk and CVD prevalence was most

prominent in subjects ≤50 years. In contrast, there was a counterintuitive positive association

between ABI and age (i.e. an increasing ABI with increasing age) in younger subjects. Although

this could theoretically indicate that peripheral artery disease (PAD) was more present in the

youngest subjects, a more plausible explanation is that younger subjects have a higher brachial

compared to central BP due to pulse pressure amplification. Because of the higher BP at the

brachial artery in comparison with central (aortic) BP relative to the systolic BP at the ankle, the

ratio of the systolic BP at the ankle and the brachial artery leads to an underestimation of ABI,

especially in younger subjects, were pulse pressure amplification is known to be higher (32).

Pulse pressure amplification also explains why ABI was positively associated with hypertension

in subjects aged ≤50 years. In line with the decrease of pulse pressure amplification with ageing,

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differences between ABI and ACI become smaller in subjects aged >50 years, where ACI and ABI

also had a similar association with CVD.

Our data show that ACI is on average higher than ABI, indicating that the optimal cut-

off for detecting PAD with ACI may be different. There is consensus that PAD is present if ABI

≤0.9 (33). Because the largest difference between ACI and ABI is present in younger subjects

at low cardiovascular risk, ACI could be more specific in detecting PAD without compromising

sensitivity. However, prospective data is needed to confirm this. ABI values ≥1.4 have also been

associated with an increased risk for cardiova scular events (24) and mortality (34), because they

may indicate the presence of medial artery calcification (35). Since medial artery calcification has

no effect on central systolic BP, and average central systolic BP is lower than brachial systolic BP,

it is likely that the upper limit for indicating medial artery calcification should be higher for ACI

than ABI.

This study is, to our knowledge, the first to show the potential value of ACI as a predictor for

CVD. Strengths of our study include its large sample size and the inclusion of individuals from

different ethnic groups with large differences in CVD prevalence, increasing the generalizability

of our findings. Limitations include that the cross-sectional nature of the current data, the fact that

we relied on non-invasive estimates of central BP, and that established CVD were self-reported.

In conclusion, the current study is the first to assess the potential value of ACI as predictor for

CVD. There is a significant difference in ABI and ACI among different ethnicities after adjustment

for conventional CVD risk factors. Especially in young subjects, the ABI might be underestimated

due to pressure amplification of systolic BP. The ratio between the central systolic BP and the

ankle systolic BP might give a better reflection of the presence of PAD in these subjects. Future

studies are needed to confirm the present cross-sectional findings, and to determine optimal

cut-off values for ACI to detect PAD.


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(1) Greenland P, Alpert JS, Beller GA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation 2010; 122(25):e584-e636.

(2) Faxon DP, Fuster V, Libby P, et al. Atherosclerotic Vascular Disease Conference: Writing Group III: pathophysiology. Circulation 2004; 109(21):2617-2625.

(3) Libby P, Ridker PM, Hansson GK. Progress and challenges in translating the biology of atherosclerosis. Nature 2011; 473(7347):317-325.

(4) Kaiser V, Kester AD, Stoffers HE, et al. The influence of experience on the reproducibility of the ankle-brachial systolic pressure ratio in peripheral arterial occlusive disease. Eur J Vasc Endovasc Surg 1999; 18(1):25-29.

(5) Khan TH, Farooqui FA, Niazi K. Critical review of the ankle brachial index. Curr Cardiol Rev 2008; 4(2):101-106.

(6) Lijmer JG, Hunink MG, van den Dungen JJ, et al. ROC analysis of noninvasive tests for peripheral arterial disease. Ultrasound Med Biol 1996; 22(4):391-398.

(7) McDermott MM, Guralnik JM, Tian L, et al. Associations of borderline and low normal ankle-brachial index values with functional decline at 5-year follow-up: the WALCS (Walking and Leg Circulation Study). J Am Coll Cardiol 2009; 53(12):1056-1062.

(8) Fowkes FG, Murray GD, Butcher I, et al. Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis. JAMA 2008; 300(2):197-208.

(9) Leng GC, Fowkes FG, Lee AJ, et al. Use of ankle brachial pressure index to predict cardiovascular events and death: a cohort study. BMJ 1996; 313(7070):1440-1444.

(10) Newman AB, Siscovick DS, Manolio TA, et al. Ankle-arm index as a marker of atherosclerosis in the Cardiovascular Health Study. Cardiovascular Heart Study (CHS) Collaborative Research Group. Circulation 1993; 88(3):837-845.

(11) Moyer VA. Screening for peripheral artery disease and cardiovascular disease risk assessment with the ankle-brachial index in adults: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2013; 159(5):342-348.

(12) Lin JS, Olson CM, Johnson ES, et al. The ankle-brachial index for peripheral artery disease screening and cardiovascular disease prediction among asymptomatic adults: a systematic evidence review for the U.S. Preventive Services Task Force. Ann Intern Med 2013; 159(5):333-341.

(13) Pini R, Cavallini MC, Palmieri V, et al. Central but not brachial blood pressure predicts cardiovascular events in an unselected geriatric population: the ICARe Dicomano Study. J Am Coll Cardiol 2008; 51(25):2432-2439.

(14) Roman MJ, Devereux RB, Kizer JR, et al. Central pressure more strongly relates to vascular disease and outcome than does brachial pressure: the Strong Heart Study. Hypertension 2007; 50(1):197-203.

(15) Wang KL, Cheng HM, Chuang SY, et al. Central or peripheral systolic or pulse pressure: which best relates to target organs and future mortality? J Hypertens 2009; 27(3):461-467.

(16) Williams B, Lacy PS, Thom SM, et al. Differential impact of blood pressure-lowering drugs on central aortic pressure and clinical outcomes: principal results of the Conduit Artery Function Evaluation (CAFE) study. Circulation 2006; 113(9):1213-1225.

(17) Stronks K, Snijder MB, Peters RJ, et al. Unravelling the impact of ethnicity on health in Europe: the HELIUS study. BMC Public Health 2013; 13:402.

(18) Stronks K, Kulu-Glasgow I, Agyemang C. The utility of ‘country of birth’ for the classification of ethnic groups in health research: the Dutch experience. Ethn Health 2009; 14(3):255-269.

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(19) Saladini F, Benetti E, Masiero S, et al. Accuracy of Microlife WatchBP Office ABI monitor assessed according to the 2002 European Society of Hypertension protocol and the British Hypertension Society protocol. Blood Press Monit 2011; 16(5):258-261.

(20) Horvath IG, Nemeth A, Lenkey Z, et al. Invasive validation of a new oscillometric device (Arteriograph) for measuring augmentation index, central blood pressure and aortic pulse wave velocity. J Hypertens 2010; 28(10):2068-2075.

(21) Trachet B, Reymond P, Kips J, et al. Numerical validation of a new method to assess aortic pulse wave velocity from a single recording of a brachial artery waveform with an occluding cuff. Ann Biomed Eng 2010; 38(3):876-888.

(22) Rose GA. The diagnosis of ischaemic heart pain and intermittent claudication in field surveys. Bull World Health Organ 1962; 27:645-658.

(23) Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 1972; 18(6):499-502.

(24) Criqui MH, McClelland RL, McDermott MM, et al. The ankle-brachial index and incident cardiovascular events in the MESA (Multi-Ethnic Study of Atherosclerosis). J Am Coll Cardiol 2010; 56(18):1506-1512.

(25) Zheng ZJ, Rosamond WD, Chambless LE, et al. Lower extremity arterial disease assessed by ankle-brachial index in a middle-aged population of African Americans and whites: the Atherosclerosis Risk in Communities (ARIC) Study. Am J Prev Med 2005; 29(5 Suppl 1):42-49.

(26) Aboyans V, Criqui MH, McClelland RL, et al. Intrinsic contribution of gender and ethnicity to normal ankle-brachial index values: the Multi-Ethnic Study of Atherosclerosis (MESA). J Vasc Surg 2007; 45(2):319-327.

(27) Singh S, Bailey KR, Kullo IJ. Ethnic differences in ankle brachial index are present in middle-aged individuals without peripheral arterial disease. Int J Cardiol 2013; 162(3):228-233.

(28) Bainey KR, Jugdutt BI. Increased burden of coronary artery disease in South-Asians living in North America. Need for an aggressive management algorithm. Atherosclerosis 2009; 204(1):1-10.

(29) Liebson PR. Cardiovascular disease in special populations III: stroke. Prev Cardiol 2010; 13(1):1-7.

(30) Agyemang C, van Oeffelen AA, Norredam M, et al. Ethnic disparities in ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage incidence in the Netherlands. Stroke 2014; 45(11):3236-3242.

(31) van Oeffelen AA, Agyemang C, Stronks K, et al. Prognosis after a first hospitalisation for acute myocardial infarction and congestive heart failure by country of birth. Heart 2014; 100(18):1436-1443.

(32) Wilkinson IB, Franklin SS, Hall IR, et al. Pressure amplification explains why pulse pressure is unrelated to risk in young subjects. Hypertension 2001; 38(6):1461-1466.

(33) Aboyans V, Criqui MH, Abraham P, et al. Measurement and interpretation of the ankle-brachial index: a scientific statement from the American Heart Association. Circulation 2012; 126(24):2890-2909.

(34) Resnick HE, Lindsay RS, McDermott MM, et al. Relationship of high and low ankle brachial index to all-cause and cardiovascular disease mortality: the Strong Heart Study. Circulation 2004; 109(6):733-739.

(35) Young MJ, Adams JE, Anderson GF, et al. Medial arterial calcification in the feet of diabetic patients and matched non-diabetic control subjects. Diabetologia 1993; 36(7):615-621.

10Mortality and Cardiovascular Risk in Patients with a History

of Malignant Hypertension: a Case-Control Study

Niels V. Van Der Hoeven, MD*1, Fouad Amraoui*1, MD, Irene G. M. Van Valkengoed, PhD2,

Liff ert Vogt, MD, PhD1,3, and Bert-Jan H. Van Den Born, MD, PhD1

*These authors contributed equally

1Departments of Internal and Vascular Medicine, Academic Medical Center,

Amsterdam, the Netherlands2Department of Public Health, Academic Medical Center, Amsterdam, the Netherlands

3Department of Nephrology, Academic Medical Center, Amsterdam, the Netherlands

J Clin Hypertens. 2014 Feb;16(2):122-6.

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ABSTRACT

The survival of patients with a history of malignant hypertension (MHT) has considerably

improved over the past decades. Data regarding the excess risk of mortality and the contribution

of conventional risk factors are lacking. We retrospectively assessed cardiovascular risk factors

and all-cause mortality in 120 patients with a history of MHT and compared them with 120

normotensive and 120 hypertensive age-, sex-, and ethnicity-matched controls.

Total cholesterol, low-density lipoprotein cholesterol, and BMI were lower in MHT patients

compared with hypertensive controls, whereas blood pressure, high-density lipoprotein

cholesterol, and smoking habit were similar. Median estimated glomerular fi ltration rata was

lower in MHT patients compared with normotensive and hypertensive controls (both P<0.01).

The annual incidence of all-cause mortality per 100 patient-years was higher in MHT patients

(2.6) compared with normotensive (0.2) and hypertensive (0.5) controls (both P<0.01). Patients

with MHT have a more favourable risk profi le compared with hypertensive controls but a

higher prevalence of renal insuffi ciency.


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INTRODUCTION

Malignant hypertension (MHT) is a hypertensive emergency characterized by severe

hypertension and acute microvascular complications including grade III or IV hypertensive

retinopathy. If left untreated, 5-year survival is <5% mainly because of stroke, myocardial

infarction, congestive heart failure and end-stage renal disease [1-3]. With the availability of

antihypertensive drugs and improved patient care, mortality has been markedly reduced to

~10% after 5 years [2;4]. This is however still considerable, given the relatively young study

populations, with an average age varying between 40 and 50 years at presentation [2;5].

Previous cohort studies, including our own, have shown that renal dysfunction is an

important predictor of mortality in patients with MHT [2;4], while other studies suggested

a role of traditional cardiovascular risk factors such as excess smoking, decreased levels of

high-density lipoprotein (HDL) and poor blood pressure (BP) control [6-9]. However, most of

these studies lacked a control population thereby limiting the internal validity. Nonetheless,

insight into the excess risk of CVD and mortality in patients with a history of MHT is required to

identify which preventive measures may further improve outcome of this extreme phenotype

of hypertension related organ damage.

Therefore, the principle aim of this study was to quantify the excess mortality risk in patients

with a history of MHT. The second aim was to investigate whether traditional cardiovascular risk

factors contribute to the increased risk. To this end, we compared cardiovascular risk factors

and all-cause mortality of patients with a history of MHT with age-, sex- and ethnicity-matched

normotensive (NT) and hypertensive (HT) controls.

METHODS

Participants

We used a case-control design to compare patients with a history of MHT with NT and HT

controls. The selection of patients with a history of MHT has been previously described [4].

Briefl y, we searched the database of a large teaching hospital in Amsterdam, The Netherlands.

The diagnosis at discharge is recorded in this database according to the International

Classifi cation of Diseases codes (ICD). All charts of patients admitted between August 1992

and January 2010 with MHT or related diagnoses were reviewed for clinical criteria of MHT

including 1) diastolic BP ≥120 mmHg and 2) presence of grade III or IV hypertensive retinopathy

[10]. Excluded were patients <18 years, pregnant women and patients who were already on

dialysis before admission. Patients referred from elsewhere were excluded to prevent referral

bias.

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Patients with a history of MHT were individually matched for age, sex and ethnicity with

NT and HT control subjects from the SUNSET study (Surinamese in The Netherlands: Study

on Ethnicity and Health), a large population-based study among non-institutional adults [11].

The SUNSET study was carried out between 2001-2003 to assess the cardiovascular risk profi le

of 35-60 year old people of European, African and South-Asian origin in Amsterdam, in the

catchment area of our hospital. In total, 1383 persons of South-Asian, African and European

origin participated in an interview and physical examination and were followed through the

national medical registration database until December 23rd 2007. Self-reported ethnicity was

used for classifi cation in ethnic groups. Black subjects were from sub-Saharan African descent,

mainly from Ghana and Nigeria. Asian subjects were mainly from the Sub-Indian continent,

whereas white subjects were of West-European ancestry. Patients with a history of MHT who

were either younger or older than NT and HT controls from SUNSET were matched with

controls closest to their own age.

Data collection and defi nitions

Vital status was assessed by inquiry of the municipal administration registries. For patients

with a history of MHT, the cause of death was retrieved from the medical fi le or from general

physicians. In addition, we recorded follow-up data on cardiovascular events of these patients.

Data derived within three months after admission of patients with a history of MHT were

censored, because death or cardiovascular events occurring during this period could be

attributable to the acute episode of MHT. For NT and HT controls data on the cause of death or

the number of cardiovascular events were not available.

All conventional cardiovascular risk factors including age, sex, ethnicity, systolic and

diastolic BP, body mass index (BMI), smoking, lipid profi le, statin use, fasting glucose, presence

of diabetes mellitus, plasma creatinine, and proteinuria were assessed at the entry of the

SUNSET study for NT and HT controls. For patients with a history of MHT, age, sex, ethnicity,

smoking status and presence of left ventricular hypertrophy were assessed at initial admission.

Left ventricular hypertrophy was considered present when detected by cardiac ultrasound or

by ECG according to the Sokolow-Lyon criteria. Systolic and diastolic BP, lipid profi le, statin

use, fasting glucose, prevalence of diabetes, BMI, plasma creatinine and proteinuria were

documented during a follow-up visit at the outpatient department using a standardized risk

assessment identical to the SUNSET study. Values assessed more than 2 years after admission

were excluded. The median time between admission and cardiovascular risk assessment was 5

months, with an interquartile range (IQR) of 2-10 months after presentation.

Renal function was estimated according to the Modifi cation of Diet in Renal Disease (MDRD)

formula [12]. Macroalbuminuria was defi ned as urinary protein excretion >300 mg/day on 24-

hour urine or >200 mg/L on a morning spot sample. All laboratory tests in patients with MTH

and the NT and HT controls were performed in the hospital’s central laboratory according to

local protocols.


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Continuous data were expressed as mean and standard deviation or median and IQR for

variables with a skewed distribution. Categorical data were expressed as number and

percentages. Diff erences between groups for continuous variables were assessed by a one-way

ANOVA with post-hoc least signifi cant diff erence correction for parametric or Dunnet’s post-

hoc correction for non-parametric distributions. Chi-square tests were used for categorical

variables. Annual incidence rates were calculated for mortality to account for diff erences in

follow-up duration. The annual incidence rates were expressed as the number of events per

100 person-years of follow-up. To assess the mortality over time, Kaplan-Meier plots were

generated to express fi ve-year survival. The log-rank test was used to assess diff erences in all-

cause mortality between groups. SPSS software was used for all analyses (Statistical Package

for the Social Sciences, version 19.0, Inc. Chicago, Illinois, USA). A P-value <0.05 was considered

signifi cant.

RESULTS

Characteristics of Patients With MHT at Presentation

A total of 120 patients admitted with MHT were included with a mean age of 44 years (range

19-79), 83 (69%) were male, and 60 (50%) were from West-European ancestry. Mean BP at

admission was 230±23/145±17 mmHg. Neurologic symptoms consistent with hypertensive

encephalopathy were present in 11 (9%) patients and 66 (55%) patients had grade IV

hypertensive retinopathy. Left ventricular hypertrophy was present in 95 (79%) patients.

Hypertension was diagnosed prior to admission in 65 (54%) patients and 39 (33%) patients

were treated with antihypertensive medication. Median plasma creatinine at admission was

2.0 mg/dL with an interquartile range of 1.2–4.5 mg/dL. A primary renal disease could be

identifi ed in 9 patients (8%), and renovascular disease was diagnosed in 7 patients (6%).

Comparison of Cardiovascular Risk Profi les at Baseline

Patients with a history of MHT were well-matched for age, sex and ethnicity with HT and NT

controls (Table 1). Systolic and diastolic BP levels during follow-up were higher in patients with

a history of MHT compared with NT controls (both P<0.01), but were not diff erent from HT

controls (P=1.0 for systolic and P=0.30 for diastolic BP). BMI of patients with MHT was similar

compared with NT controls and lower compared with HT controls (P<0.01). Smoking habits

did not diff er between the groups. Patients with a history of MHT had lower total cholesterol

and low-density lipoprotein (LDL) cholesterol levels compared with HT and NT controls

(P<0.01), while statins were more frequently prescribed to patients with a history of MHT (9%)

compared with HT (3%) and NT (2%) controls (P=0.02). After excluding MHT patients who used

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statins, mean plasma total cholesterol and LDL cholesterol remained signifi cantly lower in

patients with a history of MHT compared with HT controls (P<0.01 for both total cholesterol

and LDL cholesterol). HDL cholesterol was similar in all groups. Triglycerides were comparable

in patients with a history of MHT and HT, but lower in NT controls (P<0.01). There were no

diff erences in fasting glucose levels among groups (P=0.19), however, diabetes mellitus was

more prevalent in HT controls compared with both NT controls and MHT patients (P<0.01).

Median estimated glomerular fi ltration rate (eGFR) of patients with a history of MHT (33; IQR

14-68 mL/min/1.73m2) was lower compared with NT (82; IQR 71-112 mL/min/1.73m2) and HT

(83; IQR 71-109 mL/min/1.73m2) controls (P<0.01). Macroalbuminuria was more often present

in patients with a history of MHT compared with NT and HT controls (P<0.01). Sixteen out

of 120 patients (13%) with MHT needed kidney replacement therapy at the follow-up visit

compared with none in the groups with NT and HT controls.

Table 1. Baseline Characteristics

NT HT MHT P-value

Patients, No 120 120 120 -

Follow-up, months (IQR) 66 (62-70) 67 (62-70) 62 (24-103) 0.54

Age 44 (8) 44 (6) 44 (12) -

Male, No. (%) 83 (69%) 83 (69%) 83 (69%) -

Black, No. (%)White, No (%)Asian, No (%)

57 (48%)60 (50%)

3 (3%)

57 (48%)60 (50%)

3 (3%)

57 (48%)60 (50%)

3 (3%)-

Systolic BP, mmHg 116 (12) 144 (15) ¶ 144 (23) ¶ <0.01

Diastolic BP, mmHg 75 (7) 93 (8) ¶ 91 (15) ¶ <0.01

Total cholesterol, mg/dL 204 (40) 214 (43) 196 (44)† <0.01

LDL cholesterol, mg/dL 130 (37) 133 (40) 114 (38) ¶ † <0.01

HDL cholesterol, mg/dL 56 (14) 55 (16) 54 (18) 0.50

Triglycerides, mg/dL 90 (51) 131 (99) ¶ 132 (96) ¶ <0.01

Statin prescribed , No. (%) 2 (2) 4 (3) 10 (8) <0.05

Antihypertensive drugs, n (%) 0 24 (20%) 81 (68%)† <0.01

Fasting plasma glucose, mg/dL 97 (23) 103 (22) 97 (23) 0.19

Diabetes mellitus, No (%) 5 (4%) 12 (10%) 5 (4%) <0.01

Body mass index, kg/m2 25.9 (4.8) 28.2 (5.4) ¶ 26.1 (5.1) † <0.01

Current Smoker, No (%) 67 (56%) 60 (50%) 52 (43%) 0.19

Plasma creatinine, mg/dL (IQR) 0.9 (0.8-1.0) 0.9 (0.8-1.0) 2.0 (1.2-3.4) ¶ † <0.01

eGFR mL/min/1.73m2 (IQR) 82 (71-112) 83 (71-109) 33 (14-68) ¶ † <0.01

Macroalbuminuria (%) 0 4 (3%) 66 (55%) <0.01

Cardiovascular risk factors at fi rst follow-up visit in patients with a history of malignant hypertension (MHT) as compared with baseline values of age-, sex-, and ethnicity-matched normotensive (NT) and hypertensive (HT) controls from the same area of residence. Values are mean with standard deviation, median with IQR or numbers and percentage. IQR, interquartile range; eGFR, estimated glomular fi ltration rate. ¶P<0.05 versus NT, †P<0.05 versus HT.


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Cardiovascular Events in Patients With a History of MHT

During a median follow-up of 62 months, cerebrovascular accidents were the most frequently

observed cardiovascular event in patients with a history of MHT (n=5). Other events that

occurred in MHT patients were myocardial infarction (n=4), angina pectoris (n=2), and

peripheral artery disease (n=2). In addition, two cardiovascular events (1 stroke and 1 case of

angina pectoris) occurred within three months after admission.

Comparison of All-Cause Mortality

Eighteen patients with a history of MHT died during follow-up. One patient died within three

months after admission of a malignancy and was excluded from survival-analysis. Causes

of death of the remaining 17 patients with a history of MHT included cardiovascular events

(n=6), malignancy (n=2), infectious disease (n=2), renal failure (n=2), and was uncertain for

fi ve patients. Of the control subjects, one NT and three HT subjects died during follow-up.

Annual incidence rate of all-cause mortality per 100 years of follow up was signifi cantly higher

in patients with a history of MHT (2.6) compared with both NT (0.2) and HT (0.5) controls (both

P<0.05 [Table 2]). Log-rank test of 5-year all-cause mortality showed a higher mortality in

patients with a history of MHT compared with both NT and HT controls (both P<0.05 Figure 1]).

There was no diff erence in the average age of deceased patients with a history of MHT

compared with NT and HT controls. Comparison of deceased and surviving patients with a

history of MHT showed that patients who died during follow-up tended to be older (51±19

vs 43±11 years), were less often male (41% versus 75%, P=0.01) and tended to smoke more

often (71% versus 46%). There were no signifi cant diff erences in BP (152±24 /90±17 mmHg vs

143±21/89±13 mmHg), and other cardiovascular risk factors between deceased and surviving

patients with MHT. Estimated GFR (42 [IQR 16-105] vs 37 [IQR 25-58] mL/min/1.73m2) was

similar in deceased and surviving patients with a history of MHT, whereas macroalbuminuria

tended to be present more often in deceased patients (65% vs 54%).

Table 2. Annual Incidence Rates of All-cause Mortality

Total observation yearsNT677

HT665

MHT648

Deaths 1 3 17

Annual incidence rate 0.2 0.5 2.6

RR (95%CI) compared with NT 1 3.1 (0.3-29.4) 17.8 (2.4-133.6)*

RR (95%CI) compared with HT 0.3 (0.0-3.1) 1 5.8 (1.7-19.8)**

Annual incident rate of all-cause mortality per 100 person-years of follow-up in patients with a history of malignant hypertension (MHT) as compared with age-, sex-, and ethnicity-matched normotensive and hypertensive controls from the same residence area; RR, relative risk; CI, confi dence interval; NT, normotensive controls; HT, hypertensive controls; MHT, patients with malignant hypertension, *P< 0.05 compared with NT. **P< 0.05 compared with HT.

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Figure 1. 5-year all-cause mortality for each group

Legend: NT, normotensive controls; HT, hypertensive controls; MHT, patients with a history of malignant hypertension, *P<0.05 on log-rank test for MHT compared with NT and HT. The inner panel shows an enlargement of the outer fi gure.

DISCUSSION

We show that despite considerable improvement in survival over the past decades, patients

with a history of MHT remain at increased risk of dying compared with age, sex, and ethnicity

matched NT and HT controls. Cardiovascular risk factors seem to be of little infl uence on the

excess mortality, as total and LDL cholesterol, obesity and prevalence of diabetes mellitus were

higher in HT controls compared with patients with MHT, while BP levels and smoking habit

was comparable.BP levels were also similar in patients with a history MHT and HT controls,

suggesting that adherence to antihypertensive medication in patients with a history of MHT

may have improved after admission to the hospital. However, renal function was signifi cantly

impaired in patients with MHT compared with NT and HT controls, suggesting that renal

dysfunction may be an important contributor to the higher mortality rate observed in patients

with a history of MHT.

There is ample evidence that renal dysfunction increases the risk of cardiovascular and

all-cause mortality, with both decreased eGFR and proteinuria contributing individually to

this increased risk [13-16]. In fact, all-cause mortality is similar in patients with chronic kidney

disease (CKD) when compared with diabetic patients without CKD [17]. In the present study,

we observed that patients with a history of MHT who died during follow-up, had more often

macroalbuminuria compared with surviving patients with a history of MHT. In addition, 16

(13%) patients with a history of MHT were on kidney replacement therapy.

Hypertension is associated with clustering of cardiometabolic risk factors, including obesity,

diabetes and dyslipidemia [18]. HT control subjects indeed had an increased cardiometabolic


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risk as demonstrated by the higher BMI, higher LDL and triglyceride levels and higher

prevalence of diabetes mellitus compared with NT controls. However, there was no evidence

of clustering of cardiometabolic risk factors in patients with a history of MHT except for higher

triglyceride levels. The apparent lack of cardiovascular risk factor clustering in patients with

a history of MHT contradicts previous reports, which showed that these patients had higher

plasma triglycerides, lower plasma HDL cholesterol, and smoked more often compared with

either non-malignant HT or NT controls [6-9]. Diff erences in the proportion of smoking patients

could be explained by temporal changes in smoking behaviour as studies on associations

between MHT and cardiovascular risk date back over 30 years ago. In addition, previous studies

did not use matched control subjects to account for diff erences in socioeconomic status

or cultural background, potentially infl uencing cardiometabolic risk factors and smoking

behaviour. In the present study, control groups were derived from the same residence area and

were individually matched for age, sex and ethnicity with MHT patients to limit diff erences in

socioeconomic status and cultural background. With regard to the aforementioned diff erence

in HDL cholesterol, timing of the blood collections may have been relevant as HDL levels

were previously assessed in the acute phase of MHT. Because HDL cholesterol is an acute

phase reactant, the lower HDL cholesterol levels in that study may have been infl uenced by

the infl ammatory response associated with MHT. To avoid infl uence of these acute eff ects,

cardiovascular risk profi le was completed with a fasting venous blood sample after patients

were discharged from the hospital and BP lowering treatment was instituted.

Despite the lack of an unfavourable cardiovascular risk profi le compared with HT and NT

controls, 13 (11%) patients with a history of MHT suff ered from cardiovascular events during

follow-up. The implication of this discrepancy between estimated cardiovascular risk and the

observed number of cardiovascular events is that risk predictors based on traditional risk

factors such as the Framingham or SCORE underestimate the risk in patients with a history MHT.

Our data show that patients with a history of MHT should be considered as high risk patients

and suggest, in line with ESH recommendations, that prediction models for cardiovascular risk

should be avoided in these patients.

This study has both strengths and limitations. Our study is the fi rst to compare the survival

and cardiovascular risk factors of a large group of consecutive patients with a history of MHT

with that of NT and HT controls. Limitations include fi rstly its retrospective nature. As a result

of coding errors some patients with a history of MHT could have been missed. To overcome

this, we performed a sensitivity analysis showing that no patients with a history of MHT who

visited the emergency room between 1992–2008 were missed [4]. Secondly, the age range

of SUNSET participants was limited to 35-60 year, whereas the patients with a history of MHT

were aged 19-79. Nonetheless, most patients with a history of MHT that fell outside this age

range were younger than 35, implying a lower mortality risk, and the mean age of deceased

patients was similar among all groups. Thirdly, the recruitment window of NT and HT controls

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from SUNSET was considerably smaller leading to a much smaller variation in follow-up time

compared with the patients with a history of MHT. To account for this, the annual incidence

rate of all-cause mortality was calculated. Because the median follow-up time was similar, we

estimate that the infl uence on our results is limited. Finally, average follow-up BP was similar

in patients admitted from 1992 to 2000 compared with those admitted from 2001 to 2010

(data not shown), indicating that introduction of new antihypertensive medication during the

recruitment did not change BP control rate.

In conclusion, mortality is increased in patients with a history of MHT compared with

matched normotensive and hypertensive controls. Patients with MHT had a relatively

favourable cardiovascular risk profi le compared with HT controls but had severe renal

dysfunction. Since uncontrolled hypertension is the only modifi able predictor of long-term

renal outcome and mortality in patients with MHT [4], tight BP control should be the primary

goal in the management of patients with a history of MHT.


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REFERENCES

1. Keith NM, Wagener HP, Barker NW. Some diff erent types of essential hypertension: their course and prognosis. AM J Med Sci. 1939;196:332-339.

2. Lane DA, Lip GY, Beevers DG. Improving survival of malignant hypertension patients over 40 years. Am J Hypertens. 2009;22:1199-1204.

3. Lip GY, Beevers M, Beevers DG. Complications and survival of 315 patients with malignant-phase hypertension. J Hypertens. 1995;13:915-924.

4. Amraoui F, Bos S, Vogt L, van den Born BJ. Long-term renal outcome in patients with malignant hypertension: a retrospective cohort study. BMC Nephrol. 2012;13:71.

5. van den Born BJ, Koopmans RP, Groeneveld JO, van Montfrans GA. Ethnic disparities in the incidence, presentation and complications of malignant hypertension. J Hypertens 2006;24:2299-2304.

6. Bloxham CA, Beevers DG, Walker JM. Malignant hypertension and cigarette smoking. BMJ. 1979;1:581-583.

7. Edmunds E, Landray MJ, Li-Saw-Hee FL, Hughes BA, Beevers DG, Lip GY. Dyslipidaemia in patients with malignant-phase hypertension. QJM. 2001;94:327-332.

8. Isles C, Brown JJ, Cumming AM, Lever AF, McAreavey D, Robertson JI et al. Excess smoking in malignant-phase hypertension. BMJ. 1979;1:579-581.

9. Tuomilehto J, Elo J, Nissinen A. Smoking among patients with malignant hypertension. BMJ. (Clin Res Ed) 1982;284:1086.

10. World Health Organization. Arterial hypertension. WHO Tech Rep Ser. 1978;628:57.

11. Bindraban NR, van Valkengoed IG, Mairuhu G, Holleman F, Hoekstra JB, Michels BP et al. Prevalence of diabetes mellitus and the performance of a risk score among Hindustani Surinamese, African Surinamese and ethnic Dutch: a cross-sectional population-based study. BMC Public Health. 2008;8:271.

12. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular fi ltration rate from serum creatinine: a new prediction equation. Modifi cation of Diet in Renal Disease Study Group. Ann Intern Med. 1999;130:461-470.

13. Chronic Kidney Disease Prognosis Consortium. Association of estimated glomerular fi ltration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010;375:2073-2081.

14. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351:1296-1305.

15. Tonelli M, Muntner P, Lloyd A, Manns BJ, James MT, Klarenbach S et al. Using proteinuria and estimated glomerular fi ltration rate to classify risk in patients with chronic kidney disease: a cohort study. Ann Intern Med. 2011;154:12-21.

16. K/DOQI clinical practice guidelines on hypertension and antihypertensive agents in chronic kidney disease. Am J Kidney Dis. 2004;43:S1-290.

17. Tonelli M, Muntner P, Lloyd A, Manns BJ, Klarenbach S, Pannu N et al. Risk of coronary events in people with chronic kidney disease compared with those with diabetes: a population-level cohort study. Lancet. 2012;380:807-814.

18. Weycker D, Nichols GA, O’Keeff e-Rosetti M, Edelsberg J, Khan ZM, Kaura S et al. Risk-factor clustering and cardiovascular disease risk in hypertensive patients. Am J Hypertens. 2007;20:599-607.

11Summary and Perspectives

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Chapter 1 provides an introduction to the current thesis. The first part briefly describes the

development of blood pressure (BP) measurement, starting with the ancient Chinese who

noted that a strong pulse was associated with a clinical picture we now know to be a stroke,

until the introduction of auscultatory BP measurement as described by Nikolai Korotkoff using

the sphygmomanometer with inflatable cuff introduced by Scipione Riva-Rocci. Subsequently

the relation of BP with cardiovascular diseases (CVD) is discussed. Finally, an overview is given

on modern out-of-office BP measurement techniques, including home and ambulatory BP

measurement, which have made a more accurate and precise cardiovascular risk prediction

possible.

Part I of this thesis focusses on the reliability of BP measurement.

In Chapter 2 we compared systolic BP as measured by the palpatory technique originally

described by Riva-Rocci with the auscultatory technique as introduced by Korotkoff. We

showed that by adding 5 mmHg to the mean of three measurements, a reliable estimation of

the systolic BP can be made using Riva-Rocci’s palpatory technique. The same correction factor

can be applied irrespective of age and BP level, but tends to be less accurate in more obese

subjects.

In Chapter 3 we compared systolic BP differences between arms when assessed first at one

arm followed by the other (sequential measurements) with inter-arm BP differences assessed

at both arms at the same time (simultaneous measurement). For this study we used a novel

device capable of inflating two cuffs simultaneously. We showed that systolic inter-arm

differences are smaller when measured simultaneously. This difference was mainly explained

by an order effect during sequential measurements. The smaller inter-arm BP difference on

simultaneous measurement suggests that simultaneous assessment should be preferred

above sequential assessment to prevent unnecessary referral and diagnostic procedures. The

within-reproducibility of inter-arm BP differences was poor in both measurement techniques,

limiting its use for cardiovascular risk prediction.

In Chapter 4 we assessed the adherence of patients instructed to perform home BP

measurement (HBPM) according to the measurement schedule endorsed by the European

Society on Hypertension (ESH). For this study we used a HBPM device equipped with a

memory function to store HBPM readings. We found that merely a quarter of patients showed

full adherence to their instructed schedule, with more than half of patients performing

unscheduled BP measurements, and a third of patients missing one or more readings. When

comparing the average BP of the memory from the HBPM device with the manual logbook

entries, about ten percent of patients fell into a different BP category according to ESH criteria.

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However, average BP values were similar when relying on the memory function as compared

to the logbook entries, suggesting that for study purposes both methods are interchangeable.

In chapter 5 we investigated whether a build-in measurement schedule according to the ESH

protocol could increase patient adherence to HBPM. With this build-in schedule activated

(diagnostic mode), BP measurement was restricted to two measurements in the morning and

two in the evening for seven consecutive days. Patients were randomized to either perform

HBPM measurements using the diagnostic mode or to perform measurements in the usual

manner without time restriction (usual mode). We showed that the number of subjects with

full adherence to the measurement schedule almost doubled from nearly a quarter in the usual

mode to almost half in the diagnostic mode. Future studies are still needed to assess the impact

of improved patient’s adherence on cardiovascular outcomes.

Chapter 6 describes a case report, which demonstrates the diagnostic value of ambulatory

BP measurement (ABPM). In this case report the diagnosis was established because of a

specific ambulatory BP profile that helped to unravel the interaction between an irreversible

monoamine oxidase inhibitor used to treat a resistant major depressive disorder, and excessive

caffeine consumption in a patient with severe hypertension.

Part II of this thesis focuses on screening for hypertension and overall cardiovascular risk.

It is currently recommended to perform at least 12 BP measurements to acquire a reliable

BP value with HBPM. This limits its use when using HBPM as a screening tool for diagnosing

hypertension. In chapter 7 we used data acquired from a large worksite health risk assessment

to examine whether it is possible to reduce this number and develop cut-off values to be used

for one or two BP measurement to predict whether a subject has hypertension. We showed

that a threshold of ≥150/90 mmHg can be used to diagnose hypertension and a threshold of

<135/80 mmHg can be used to rule out hypertension after two successive measurements. With

the use of these thresholds, 6 out of 10 subjects can be diagnosed as either hypertensive of

normotensive after two successive readings, of which 1.1% are falsely labelled as hypertensive,

and 4.7% falsely labelled as normotensive. The remaining subjects need to perform the whole

BP series for reliable classification.

Chapter 8 describes the development of a CVD risk score composed of non-invasive

parameters to predict whether a person has a 10-year CVD mortality risk of ≥5% according to

the SCORE risk equation. This use of the SCORE risk equation is recommended for individual

risk prediction in current European guidelines on primary cardiovascular prevention, but many

physicians lack time or finances to perform this risk score, including BP and invasive cholesterol


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measures, in every eligible person (males >40 years and females >50 years or post-menopausal

women without CVD). We therefore developed a simple questionnaire including six non-

invasive parameters by using data of male participants of a large, worksite health promotion

program. We demonstrated that by using our questionnaire as a first screening step, the

number of subjects requiring a total risk score can be reduced to 18% of the total number of

male individuals that need to be screened according to current guidelines. This greatly reduces

the time and means needed for large-scale screening programs based on current guideline

recommendations.

In chapter 9 we used cross-sectional data from a large, multi-ethnic population study to

examine the relationship between cardiovascular risk and disease and the systolic ankle-

brachial BP index (ABI) compared to the systolic ankle-central aortic BP index (ACI). We

showed that in younger subjects (aged ≤50 year), ACI correlated better with cardiovascular

risk according to the SCORE equation than the conventional ABI. Moreover, in young subjects

ACI was also a predictor of established CVD, whereas ABI was not. This study is the first to show

that ACI can be used as a potential cardiovascular risk predictor, but prospective studies are

needed to confirm these results.

In chapter 10 we examined the cardiovascular risk profile and all-cause mortality of subjects

with a history of malignant hypertension (MHT), a hypertensive emergency with grade III of IV

retinopathy. In comparison with age, sex and gender matched normotensive and hypertensive

controls, subjects with a history of MHT did not have an unfavorable cardiovascular risk profile.

In fact, they had a more favorable cardiovascular risk profile compared to hypertensive controls.

Despite the lack of an unfavorable cardiovascular risk profile, the annual incidence of all-cause

mortality was higher in subjects with a history of MHT (2.6 per 100 patient years) compared

to hypertensive (0.5) and normotensive (0.2) controls. This indicates that in subjects with a

history of MHT mortality rate is high irrespective of cardiovascular risk profile, and that the use

of models predicting CVD such as Framingham or SCORE should be avoided in these subjects.

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PERSPECTIVES

The current thesis explored several issues regarding reliability of BP measurement and CVD risk

prediction. However, there are still many subjects that require further exploration, on top of

interesting ongoing developments. These will be discussed in the current section.

Office BP measurement

In essence, not much has changed in the measurement of office BP compared to the technique

introduced by Riva-Rocci and refined by Korotkoff. To improve reproducibility and reliability

of office BP measurement current guidelines have formulated strict recommendations. These

include the use of an appropriately sized cuff placed at heart level, letting the subject rest

before commencing the BP measurement, and averaging multiple measurements1. The

mercury sphygmomanometer has largely been abandoned because of mercury toxicity, and is

replaced by aneroid devices, that need frequent calibration or automated devices 2;3. Automatic

BP devices usually rely on the oscillometric technique, where arterial oscillations are recorded

by a cuff during deflations, and used to derive systolic and diastolic BP. A large advantage of

these devices compared to auscultatory measurement is that they are free from observer bias

and terminal digit preference. To ascertain reliability of automated devices, new devices need

to be validated against auscultatory BP measurement. Several protocols have been developed

for this purpose4-6, and on the website http://www.dableducational.org all devices that have

been tested against one of these validation protocols including the outcome have been listed.

Although office BP measurement is often referred to as the ‘cornerstone’ of BP measurement,

there is ample evidence that out-of-office BP measurements, including home and ambulatory

BP measurement, are more reliable and show a better correlation with cardiovascular

outcomes7-11. In light of this evidence it is likely that office BP measurement will more often

be considered as a screening instrument to evaluate whether subjects need to perform

additional out-of-office measurement. A good example of this development is the current

British NICE guideline, which recommends that when office BP is increased for the first time

(≥140/90mmHg), ambulatory BP measurement (ABPM) is warranted 12.

A useful addition to office BP measurement are automated devices capable of taking

multiple measurements with an interval of several (i.e. 1-5) minutes. When these measurements

are taken in a quiet, secluded room, the average of these automated BP measurements

correlate well with daytime ambulatory BP measurements13;14. Although this indicates that

subjects need to attend ~30 minutes earlier to their doctors appointment, valuable BP data

can be obtained at the office or in the hospital, which could prevent unnecessary additional

diagnostic procedures or treatment.

Another point of discussion is the relevance of inter-arm BP differences. It is currently

recommended to measure BP at both arms at a patient’s first visit1, preferably simultaneously


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as this leads to smaller inter-arm BP differences15. Although it has been established that a

systolic BP between arm difference is associated with increased cardiovascular mortality

and morbidity 16;17, there is no known direct causal link. In addition, inter-arm differences are

poorly reproducible18 making it a poor individual prognostic marker. More data are needed

to elucidate the clinical importance of large inter-arm BP differences, most notably whether

cardiovascular prevention reduces cardiovascular risk in those patients.

Home BP Measurements

The publication of two important consensus documents on home BP measurement (HBPM)

in 2008 contributed in streamlining the use of HBPM in clinical practice19;20. Although these

documents cover many aspects of HBPM – including how to measure home BP, the optimal

home BP schedule, and recommended BP thresholds - they still left some unresolved issues. For

example, the guidelines provide one cut-off value (≤135/85 mmHg) applicable to all subjects

irrespective of age. In contrast, the latest European and American guidelines recommend to

consider the use of a more liberate cut-off for systolic office BP in the elderly (defined as aged

≥80 in the European1 and aged ≥60 years in the American21 guidelines). There is increasing

evidence that a higher systolic BP cut-off is also appropriate for HBPM readings in the elderly22-24,

but there is yet no consensus on which cut-off value should be used. HBPM, like ABPM, is void

of the white coat effect, with similar reproducibility and a similar correlation with target organ

damage as ABPM7;25-34. However, a disadvantage is that nighttime BP cannot be measured with

routine HBPM devices, while nighttime BP is the most predictive BP in terms of cardiovascular

mortality10. Recently, HBPM devices have been introduced capable of taking nocturnal BP

measurements35;36. These devices take several measurements during the night. The average

of these readings corresponds fairly well with the average of nocturnal ABPM readings, with

greater patient acceptance than ABPM37. Nocturnal HBPM readings also correlate with markers

of target organ damage38, to a similar degree as ABPM39. These devices seem promising, but

prospective data on cardiovascular disease or mortality are currently lacking.

A relatively new feature of HBPM is the use of telemonitoring40;41. This means that measured

BP data are collected at home and transmitted to a health care professional, usually through a

telephone line or internet connection, with mobile phone applications as the latest innovation.

Telemonitoring could reduce the number of required doctor visits, eliminates the need of

written and often corrupted logbook data42-46, and could offer standardized treatment or

lifestyle decisions. A meta-analysis including 4389 participants of 11 randomized trials showed

a significant beneficial effect of telemonitoring on office BP 47, with a reduction of 5.64 (95%

CI 7.92-3.36) mmHg in systolic BP and 2.78 mmHg (95% CI 3.93-1.62) in diastolic BP compared

to office BP of patients receiving usual care. Also, more subjects in the telemonitoring group

achieved their target BP (relative risk 1.16, 95% CI 1.04-1.29). However, the methods of

telemonitoring used in the reviewed studies varied widely, and the follow-up of studies was

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short with a median follow-up of 24 weeks. Moreover, telemonitoring of home BP was not

compared with regular HBPM, and it is yet unknown whether telemonitoring of BP is cost-

effective. Therefore, although seemingly promising, more data are needed to decide whether

telemonitoring has a place in the field of BP measurement in the (near) feature.

Ambulatory blood pressure measurement

Although ambulatory BP measurement is expensive and labor-intensive compared to

conventional and home BP measurement, it is currently a valued tool in the diagnosis of

hypertension. As mentioned before, the British guidelines released in 2011 recommend

performing an ABPM registration in every subject on first confrontation with an elevated office

BP12. In 2013, the ESH has published a position paper on ABPM, including a comprehensive

review of all available evidence at that time, and recommendations on how and when to use

ABPM48, followed one year later by an official guideline document49. The guidelines recommend

ABPM for a wide range of indications, including compelling indications such as identifying

the white coat effect or masked hypertension, but also provide additional indications such

as assessing increased BP variability or the assessment of ambulatory hypotension. The

guidelines also include a more extensive definition of white coat and masked hypertension,

where nighttime BP is taken into account, and provides recommendations on when ABPM

should be repeated in clinical practice.

An interesting development is the introduction of ABPM devices that are capable to non-

invasively determine the central ‘or aortic’ BP, in addition to the usually measured brachial

BP50-54. It is thought that the BP near the aortic root gives a better reflection of the true load

imposed on the heart. Current evidence indeed suggest that central BP may be a stronger

predictor for future CVD than brachial BP55-58. Whether this also means that 24h central BP has

a better predictive value than 24h brachial BP has, however, yet to be determined.

Differences between home and ambulatory BP measurement

Both HBPM and ABPM show better correlation with target organ damage and CV disease

than conventional office blood pressure measurement59-63. ABPM and HBPM also have similar

reproducibility and the ability to detect the white coat and masked hypertension64-68. Although

many studies have compared ABPM or HBPM in relation to conventional office measurement,

surprisingly few studies addressed the difference in outcome between HBPM and ABPM. One

study assessing subclinical cerebrovascular disease in 1007 Japanese subjects aged ≥55 years,

showed that HBPM was more closely associated with the risk of carotid atherosclerosis than

ABPM, suggesting that both modalities might predict different types of organ damage69.

However, a systematic review revealed that ABPM and HBPM correlated equally well with

target organ damage defined as echocardiographic left ventricular mass index34. For other

indices of target organ damage, such as the glomerular filtration rate, carotid intima-media


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thickness, and pulse wave velocity the number of studies conducted was too small to draw

reliable conclusions. Cerebrovascular disease outcomes were not included in the review. In

a prospective study combining the data of two Finish cohort studies, several prediction

models including office, home, and ambulatory BP measurement were constructed to predict

a combined endpoint of CVD mortality and morbidity. Compared to office BP, adding ABPM

significantly improved the prediction model, whereas HBPM did not70. The PAMELA study, in

which over 2000 patients were included71, revealed that patients showing an elevated home

BP with normal ambulatory BP and vice versa were at intermediate risk for CV mortality

compared with patients having either both normal or both elevated BP values. The authors

concluded that the increase of risk was greater in patients with an isolated elevation of home

BP than in patients with an isolated elevation in ambulatory BP, which they found in 14% of

their population. However, after correction for age and gender this effect was not significant.

In addition, they only performed two HBPM measurements in each patient, whereas a minimal

number of 12 measurement is currently recommended19, and they used different BP thresholds

than presently recommended. A more recent study including 831 untreated outpatients

assessed the changes in BP status after adding ABPM to office BP and HBPM72. Initially patients

were classified based on office and home BP as having normotension (normal office and

home BP), sustained hypertension (elevated office and home BP), white coat hypertension

(elevated office and normal home BP) or masked hypertension (normal office and elevated

home BP). When comparing ABPM readings instead of HBPM readings with office BP, risk

was downgraded from masked hypertension to normotension (n=24), or from sustained to

white coat hypertension (n=9) in 33 (4.0%) subjects, and upgraded risk from normotension

to masked hypertension (n=179), or from white coat to sustained hypertension (n=44) in 223

(26.8%) subjects. The subjects that were upgraded in risk categories had higher urine albumin-

to-creatinine ratios and higher pulse wave velocity values compared to the subjects that

remained in the same BP category, but the authors reported no data on target organ damage

on subjects that had high HBPM but normal ABPM. In the International Database of HOme

blood pressure in relation to Cardiovascular Outcome (IDHOCO), a database comprising five

population studies on HBPM including data on cardiovascular outcome73, it was shown that

untreated subjects with elevated office, but normal home BP (white coat hypertension) were

at increased cardiovascular risk compared to subjects with normotension. In contrast, in the

International Database of Ambulatory blood pressure in relation to Cardiovascular Outcome

(IDACO), a large database containing data on ABPM from subjects from 11 countries, untreated

subjects with elevated office but normal ambulatory BP showed no elevated cardiovascular

risk compared to normotensive subjects74;75. Although both databases have similar number

of subjects and follow-up time, the data is acquired from different datasets, indicating that

this conclusion should be interpreted with caution. Nonetheless, adding up all the existing

evidence it seems that justified to consider HBPM and ABPM as two different entities, which

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are complementary to each other, but not interchangeable. Summarizing the aforementioned

studies, it seems that of all modalities ABPM is the most reliable, since it correlates betters

with CVD than office BP, it improves CVD risk prediction upon office BP whereas HBPM does

not, it identifies subjects with masked hypertension with more signs of target-organ damage

than identified by HBPM, and seems to identify white coat subjects with lower CVD risk than

subjects with white coat hypertension identified by HBPM. However, a prospective study with

a direct comparison between ABPM and HBPM on target-organ damage or cardiovascular

disease or mortality is currently lacking. Until such data are provided, it seems justified to

consider ABPM as the gold standard for diagnosing hypertension, but also to consider HBPM

as a worthy alternative when ABPM is not available.


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(24) Nomura K, Asayama K, Thijs L et al. Thresholds for conventional and home blood pressure by sex and age in 5018 participants from 5 populations. Hypertension 2014;64:695-701.

(25) Bjorklund K, Lind L, Zethelius B, Andren B, Lithell H. Isolated ambulatory hypertension predicts cardiovascular morbidity in elderly men. Circulation 2003;107:1297-1302.

(26) Hanninen MR, Niiranen TJ, Puukka PJ, Jula AM. Comparison of home and ambulatory blood pressure measurement in the diagnosis of masked hypertension. J Hypertens 2010;28:709-714.

(27) Hond ED, Celis H, Fagard R et al. Self-measured versus ambulatory blood pressure in the diagnosis of hypertension. J Hypertens 2003;21:717-722.

(28) James GD, Pickering TG, Yee LS, Harshfield GA, Riva S, Laragh JH. The reproducibility of average ambulatory, home, and clinic pressures. Hypertension 1988;11:545-549.

(29) Mule G, Caimi G, Cottone S et al. Value of home blood pressures as predictor of target organ damage in mild arterial hypertension. Journal of Cardiovascular Risk 2002;9:123-129.

(30) Niiranen TJ, Hanninen MR, Johansson J, Reunanen A, Jula AM. Home-measured blood pressure is a stronger predictor of cardiovascular risk than office blood pressure: the Finn-Home study. Hypertension 2010;55:1346-1351.

(31) Parati G, Stergiou GS. Self measured and ambulatory blood pressure in assessing the ‘white-coat’ phenomenon. J Hypertens 2003;21:677-682.

(32) Staessen JA, Asmar R, De BM et al. Task Force II: blood pressure measurement and cardiovascular outcome. Blood Press Monit 2001;6:355-370.

(33) Stergiou GS, Efstathiou SP, Argyraki CK, Gantzarou AP, Roussias LG, Mountokalakis TD. Clinic, home and ambulatory pulse pressure: comparison and reproducibility. J Hypertens 2002;20:1987-1993.

(34) Stergiou GS, Argyraki KK, Moyssakis I et al. Home blood pressure is as reliable as ambulatory blood pressure in predicting target-organ damage in hypertension. Am J Hypertens 2007;20:616-621.

(35) Hosohata K, Kikuya M, Ohkubo T et al. Reproducibility of nocturnal blood pressure assessed by self-measurement of blood pressure at home. Hypertens Res 2007;30:707-712.

(36) Chonan K, Kikuya M, Araki T et al. Device for the self-measurement of blood pressure that can monitor blood pressure during sleep. Blood Press Monit 2001;6:203-205.

(37) Stergiou GS, Nasothimiou EG, Destounis A, Poulidakis E, Evagelou I, Tzamouranis D. Assessment of the diurnal blood pressure profile and detection of non-dippers based on home or ambulatory monitoring. Am J Hypertens 2012;25:974-978.

(38) Kario K, Hoshide S, Haimoto H et al. Sleep Blood Pressure Self-Measured at Home as a Novel Determinant of Organ Damage: Japan Morning Surge Home Blood Pressure (J-HOP) Study. J Clin Hypertens (Greenwich) 2015.

(39) Andreadis EA, Agaliotis G, Kollias A, Kolyvas G, Achimastos A, Stergiou GS. Night-time home versus ambulatory blood pressure in determining target organ damage. J Hypertens 2016;34:438-444.


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(40) Parati G, Omboni S. Role of home blood pressure telemonitoring in hypertension management: an update. Blood Press Monit 2010;15:285-295.

(41) Sivakumaran D, Earle KA. Telemonitoring: use in the management of hypertension. Vasc Health Risk Manag 2014;10:217-224.

(42) Johnson KA, Partsch DJ, Rippole LL, Mcvey DM. Reliability of self-reported blood pressure measurements. Archives of Internal Medicine 1999;159:2689-2693.

(43) Mengden T, Alvarez E, Beltran B, Kraft K, Vetter H. Reliability of reporting self-measured blood pressure values of hypertensive patients. Journal of Hypertension 1998;16:S271.

(44) Myers MG. Reporting bias in self-measurement of blood pressure. Blood Pressure Monitoring 2001;6:181-183.

(45) Nordmann A, Frach B, Walker T, Martina B, Battegay E. Reliability of patients measuring blood pressure at home: prospective observational study. British Medical Journal 1999;319:1172.

(46) van der Hoeven NV, van den Born BJ, Cammenga M, van Montfrans GA. Poor adherence to home blood pressure measurement schedule. J Hypertens 2009;27:275-279.

(47) Omboni S, Guarda A. Impact of home blood pressure telemonitoring and blood pressure control: a meta-analysis of randomized controlled studies. Am J Hypertens 2011;24:989-998.

(48) O’Brien E, Parati G, Stergiou G et al. European society of hypertension position paper on ambulatory blood pressure monitoring. J Hypertens 2013;31:1731-1768.

(49) Parati G, Stergiou G, O’Brien E et al. European Society of Hypertension practice guidelines for ambulatory blood pressure monitoring. J Hypertens 2014;32:1359-1366.

(50) Horvath IG, Nemeth A, Lenkey Z et al. Invasive validation of a new oscillometric device (Arteriograph) for measuring augmentation index, central blood pressure and aortic pulse wave velocity. J Hypertens 2010;28:2068-2075.

(51) Jatoi NA, Mahmud A, Bennett K, Feely J. Assessment of arterial stiffness in hypertension: comparison of oscillometric (Arteriograph), piezoelectronic (Complior) and tonometric (SphygmoCor) techniques. J Hypertens 2009;27:2186-2191.

(52) Luzardo L, Lujambio I, Sottolano M et al. 24-h ambulatory recording of aortic pulse wave velocity and central systolic augmentation: a feasibility study. Hypertens Res 2012;35:980-987.

(53) Parati G, De BM. Evaluating aortic stiffness through an arm cuff oscillometric device: is validation against invasive measurements enough? J Hypertens 2010;28:2003-2006.

(54) Trachet B, Reymond P, Kips J et al. Numerical validation of a new method to assess aortic pulse wave velocity from a single recording of a brachial artery waveform with an occluding cuff. Ann Biomed Eng 2010;38:876-888.

(55) Pini R, Cavallini MC, Palmieri V et al. Central but not brachial blood pressure predicts cardiovascular events in an unselected geriatric population: the ICARe Dicomano Study. J Am Coll Cardiol 2008;51:2432-2439.

(56) Roman MJ, Devereux RB, Kizer JR et al. Central pressure more strongly relates to vascular disease and outcome than does brachial pressure: the Strong Heart Study. Hypertension 2007;50:197-203.

(57) Wang KL, Cheng HM, Chuang SY et al. Central or peripheral systolic or pulse pressure: which best relates to target organs and future mortality? J Hypertens 2009;27:461-467.

(58) Williams B, Lacy PS, Thom SM et al. Differential impact of blood pressure-lowering drugs on central aortic pressure and clinical outcomes: principal results of the Conduit Artery Function Evaluation (CAFE) study. Circulation 2006;113:1213-1225.


(60) Mule G, Caimi G, Cottone S et al. Value of home blood pressures as predictor of target organ damage in mild arterial hypertension. J Cardiovasc Risk 2002;9:123-129.

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(69) Hara A, Tanaka K, Ohkubo T et al. Ambulatory versus home versus clinic blood pressure: the association with subclinical cerebrovascular diseases: the Ohasama Study. Hypertension 2012;59:22-28.

(70) Niiranen TJ, Maki J, Puukka P, Karanko H, Jula AM. Office, home, and ambulatory blood pressures as predictors of cardiovascular risk. Hypertension 2014;64:281-286.

(71) Mancia G, Facchetti R, Bombelli M, Grassi G, Sega R. Long-term risk of mortality associated with selective and combined elevation in office, home, and ambulatory blood pressure. Hypertension 2006;47:846-853.

(72) Zhang L, Li Y, Wei FF et al. Strategies for classifying patients based on office, home, and ambulatory blood pressure measurement. Hypertension 2015;65:1258-1265.

(73) Stergiou GS, Asayama K, Thijs L et al. Prognosis of white-coat and masked hypertension: International Database of HOme blood pressure in relation to Cardiovascular Outcome. Hypertension 2014;63:675-682.

(74) Franklin SS, Thijs L, Hansen TW et al. Significance of white-coat hypertension in older persons with isolated systolic hypertension: a meta-analysis using the International Database on Ambulatory Blood Pressure Monitoring in Relation to Cardiovascular Outcomes population. Hypertension 2012;59:564-571.

(75) Hansen TW, Kikuya M, Thijs L et al. Prognostic superiority of daytime ambulatory over conventional blood pressure in four populations: a meta-analysis of 7,030 individuals. J Hypertens 2007;25:1554-1564.

AddendaNederlandse samenvatting

Authors and affi liations

Dankwoord

Portfolio

Curriculum vitae

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Nederlandse samenvatting

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Hoofdstuk 1 omvat de introductie van dit proefschrift. In het eerste deel van dit hoofdstuk

wordt kort de geschiedenis van het bloeddrukmeten weergegeven. Deze geschiedenis begint

ongeveer 4000 jaar voor Christus toen de Chinezen ontdekten dat een harde polsslag was

geassocieerd met een beroerte, en loopt tot aan de introductie van het auscultatoir (met behulp

van een stethoscoop) meten van de bloeddruk door de Russische legerarts Nikolai Korotkoff

in1905. Vervolgens wordt de relatie tussen bloeddruk en hart- en vaatziekten besproken. Als

laatste wordt er een overzicht gegeven over huidige vormen van bloeddrukmeten buiten het

ziekenhuis, zoals thuisbloeddrukmeting en 24-uurs bloeddrukmeting, die een nauwkeurigere

voorspelling van het risico op hart- en vaatziekten mogelijk maakten.

Deel I van dit proefschrift richt zich op de betrouwbaarheid van de bloeddrukmeting.

In hoofdstuk 2 vergeleken we de systolische bloeddruk zoals gemeten door middel van de

palpatoire techniek zoals Riva-Rocci deze toepaste, met de auscultatoire techniek bedacht

door Korotkoff, wat tot voor kort als de klinische gouden standaard voor het bloeddrukmeten

werd gezien. Uit deze studie bleek dat wanneer er 5 mmHg werd opgeteld bij het gemiddelde

van drie metingen volgens de methode van Riva-Rocci, er een betrouwbare schatting van

de systolische bloeddruk kon worden gemaakt. Deze correctiefactor kon worden toegepast

ongeacht leeftijd of bloeddruk, maat neigde iets onnauwkeuriger te zijn bij personen met

overgewicht.

In hoofdstuk 3 vergeleken we het verschil in systolische bloeddruk tussen beide armen

wanneer deze om de beurt aan de armen werd gemeten (sequentiële bloeddrukmeting) met

wanneer deze tegelijktijdig aan beide armen werd gemeten (simultane bloeddrukmeting).

Voor deze studie werd een nieuw apparaat gebruikt dat in staat is om twee bloeddrukbanden

tegelijktijdig aan beide armen op te blazen. Uit deze studie bleek dat het verschil in systolische

bloeddruk tussen beide armen kleiner was als deze simultaan werd gemeten, dan wanneer

deze sequentieel werd gemeten. Dit verschil was voornamelijk te verklaren door een volgorde

effect gedurende de sequentiële metingen, waarbij de eerste meting gemiddeld hoger

was dan de tweede meting. Het kleinere verschil in systolische bloeddruk bij simultane

meting suggereert dat simultane bloeddrukmeting verkozen dient te worden boven

sequentiële bloeddrukmeting om onnodige verwijzingen of diagnostische test naar een

groot bloeddrukverschil tussen beide armen te voorkomen. De reproduceerbaarheid van het

verschil in systolische bloeddruk tussen beide armen was met beide methoden niet erg hoog,

wat betekent dat de aanvullende waarde van het gebruik hiervan in het kader van individuele

cardiovasculaire risicopredictie beperkt is.

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In hoofdstuk 4 werd gekeken of patiënten die thuis de bloeddruk maten volgens het schema

dat wordt aangeraden door het Europese Hypertensie Genootschap (European Society on

Hypertension, ESH), zich ook daadwerkelijk aan dat schema hielden. Voor deze studie werd

gebruik gemaakt van een thuisbloeddrukmeter die was uitgerust met een geheugenfunctie

waardoor de thuisbloeddrukmetingen konden worden opgeslagen. Uit deze studie kwam naar

voren dat amper een kwart van de patiënten de bloeddruk precies zo mat als ze werd verteld,

waarbij meer dan de helft van de patiënten extra metingen buiten het schema verrichtten

en een derde van de patiënten één of meer metingen miste. Bij ongeveer tien procent van

de patiënten valt de bloeddruk volgens het geheugen van de bloeddrukmeter in een andere

bloeddrukcategorie volgens de ESH dan de metingen die in het logboek door de patiënt

werden bijgehouden. Het gemiddelde van alle bloeddrukken in het geheugen was echter

gelijk aan dat van alle bloeddrukken in het logboek, wat suggereert dat voor studiedoeleinden

het niet uitmaakt welke van de waarden gebruikt worden.

In hoofdstuk 5 werd onderzocht of een in de bloeddrukmeter ingebouwd bloeddrukmeet-

schema volgens het ESH schema voor thuisbloeddrukmeten er voor zorgt dat patiënten zich

beter aan dit schema houden. Als deze modus aanstond (diagnostic mode), konden patiënten

alleen twee metingen in de ochtend en twee metingen in de avond opnemen, voor zeven

aansluitende dagen. Patiënten werden gerandomiseerd om thuisbloeddrukmetingen te

verrichten in deze diagnostic mode of om de bloeddrukmetingen zonder beperkingen uit

te voeren (usual mode). Uit deze studie bleek dat het aantal patiënten dat zich geheel aan

het opgedragen schema hield bijna verdubbelde van ongeveer een kwart in de usual mode

naar bijna de helft in de diagnostic mode. Toekomstige studies zullen moeten uitwijzen of dit

uiteindelijk ook leidt tot minder hart- en vaatziekten.

In hoofdstuk 6 wordt een case report beschreven waaruit blijkt dat 24-uurs bloeddrukmeting

een belangrijk diagnosticum kan zijn. In dit case report werd aan de hand van het 24-uurs

bloeddrukprofiel een interactie ontdekt tussen een irreversibele monoamine oxidase remmer,

wat werd voorgeschreven voor een therapieresistente depressie, en overmatig cafeïnegebruik

bij een patiënt met ernstige hypertensie.

Deel II van dit proefschrift richt zich op het screenen naar hypertensie en het identificeren van

personen met een hoog cardiovasculair risicoprofiel.

Huidige richtlijnen met betrekking tot het thuis meten van de bloeddruk raden aan om

tenminste 12 bloeddrukmetingen te verrichten om een betrouwbare waarde te verkrijgen. Dit

aantal metingen beperkt het gebruik van thuisbloeddrukmeten als een screeningsinstrument

naar hypertensie. In hoofdstuk 7 werd gebruikt gemaakt van data verkregen uit een groot


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gezondheidsbevorderend programma op de werkvloer om te onderzoeken of het mogelijk

is om al met één of twee metingen een voorspelling te doen over iemands bloeddrukstatus.

Uit het onderzoek bleek dat na twee opeenvolgende bloeddrukmetingen een afkapwaarde

van ≥150/90 mmHg gebruikt kan worden om de diagnose hypertensie te stellen, en dat een

afkapwaarde van <135/80 mmHg gebruikt kan worden om hypertensie uit te sluiten. Indien men

deze afkapwaarden gebruikt, kan bij 6 van de 10 personen al na twee opeenvolgende metingen

een diagnose worden gesteld, waarbij 1.1% foutief als hypertensief worden geclassificeerd, en

4.7% ten onrechte als normotensief. De overige personen die na twee metingen niet kunnen

worden geclassificeerd dienen hun thuismeetserie alsnog te completeren.

Hoofdstuk 8 beschrijft de ontwikkeling van een risicoscore, enkel gebruik makend van niet-

invasief verkregen parameters, om een kans van ≥5% om de komende 10 jaar te overlijden

aan hart- en vaatziekten volgens de SCORE-formule te voorspellen. Het gebruik van deze

SCORE-formule wordt momenteel geadviseerd in de huidige Europese richtlijn voor primaire

cardiovasculaire preventie. Veel artsen geven aan onvoldoende tijd of geld te hebben om

een volledig risicoprofiel op te stellen, inclusief een cholesterol- en bloeddrukmeting, voor

elke persoon die daarvoor in aanmerking komt (mannen >40 jaar en vrouwen >50 jaar of

postmenopauzale vrouwen zonder hart- en vaatziekten). Daarom werd een eenvoudige

vragenlijst ontwikkeld bestaande uit zes niet-invasief te verkrijgen parameters. Uit deze

studie, waarbij gebruik werd gemaakt van data van mannelijke deelnemers van een groot

gezondheid bevorderend programma op de werkvloer, bleek dat wanneer deze vragenlijst

gebruikt werd als eerste stap in een screeningtraject het aantal personen waarbij nog een

volledig cardiovasculair risicoprofiel dient te worden opgesteld gereduceerd kon worden

tot 18% van alle mannelijke deelnemers die hier volgens de richtlijnen voor in aanmerking

voor komen. Dit zorgt voor een aanzienlijke reductie in tijd en middelen die nodig zijn voor

grootschalige screeningsprogramma’s

In hoofdstuk 9 werd gebruikt gemaakt van cross-sectionele data van een grote, multi-

etnische populatiestudie om de relatie uit te zoeken tussen cardiovasculair risico en hart- en

vaatziekten en de verhouding tussen de systolische bloeddruk aan de enkel en de bovenarm

(de enkel-arm index, of EAI) vergeleken met de verhouding tussen de systolische bloeddruk

aan de enkel en de centrale bloeddruk (enkel-centrale bloeddruk index, of ECI). Uit deze studie

bleek dat in jongere personen (≤50 jaar), de ECI beter correleerde met het cardiovasculair

risico dan de ABI. De ECI bleek bij jongere personen ook een voorspeller voor bestaande hart-

en vaatziekten, waarbij de EAI dit niet was. Deze studie is de eerste waaruit blijkt dat de ECI

gebruikt kan worden als potentiële voorspeller voor hart- en vaatziekten, maar prospectieve

data zijn nodig om deze resultaten te bevestigen.

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In hoofdstuk 10 werden het cardiovasculaire risicoprofiel en de algehele sterfte onderzocht

van personen met een voorgeschiedenis van maligne hypertensie (MHT), een hypertensief

noodgeval gepaard gaande met graad III of IV retinopathie. Vergeleken met normotensieve

en hypertensieve controlepersonen, gematched voor leeftijd, geslacht en etniciteit, hebben

patiënten met MHT in de voorgeschiedenis geen ongunstiger cardiovasculair risicoprofiel.

Vergeleken met hypertensieve personen was het risicoprofiel zelfs gunstiger. Ondanks het

ontbreken van een ongunstig cardiovasculair risicoprofiel, is de jaarlijkse sterfte-incidentie

voor personen met MHT in de voorgeschiedenis hoger (2.6 per 100 patiëntjaar), dan dat van de

hypertensieve (0.5) en de normotensieve controlepersonen (0.2). Dit wijst erop dat patiënten

met MHT in de voorgeschiedenis een hoog sterftecijfer hebben, ongeacht hun cardiovasculaire

risicoprofiel, en dat risicopredictiemodellen voor hart- en vaatziekten zoals Framingham of

SCORE niet gebruikt moeten worden bij deze personen.


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TOEKOMSTPERSPECTIEVEN

In dit proefschrift werden verschillende onderwerpen beschreven die te maken hebben met

de betrouwbaarheid van het bloeddrukmeten en met cardiovasculaire risicopredictie. Er zijn

echter nog vele onderwerpen die verder onderzoek behoeven, naast een aantal interessante

ontwikkelen dat gaande is. Deze zullen hier verder worden besproken.

Bloeddrukmeting in de spreekkamer

Er is in essentie weinig veranderd aan de techniek van het bloeddruk meten in de spreekkamer

sinds de introductie door Riva-Rocci en de verfijning van de techniek door Korotkoff. Om de

betrouwbaarheid en reproduceerbaarheid van spreekkamer bloeddrukmetingen te vergroten

zijn er in de huidige richtlijnen ten aan zien van het bloeddrukmeten strikte aanbevelingen

geformuleerd. Zo dient er een juiste maat bloeddrukband gebruikt te worden die gedurende

de meting op hart-hoogte wordt gehouden, dient de persoon waar de bloeddruk gemeten

wordt minimaal vijf minuten rust te hebben gehouden, en wordt aanbevolen om het

gemiddelde van meerdere metingen te gebruiken1. De klassieke kwik sphygmomanometer

wordt tegenwoordig weinig meer gebruikt vanwege de toxiciteit van kwik. Tegenwoordig

wordt er veelal gebruik gemaakt van aneroïde meters, die frequent gekalibreerd dienen te

worden, of van geautomatiseerde bloeddrukmeters2;3.

Geautomatiseerde bloeddrukmeters maken meestal gebruik van de oscillometrische

techniek, een techniek waarbij de arteriële oscillaties worden geregistreerd tijdens het leeg

laten lopen van bloeddrukband, om zo de systolische en diastolische bloeddruk te herleiden.

Een groot voordeel van het gebruik van deze apparaten ten opzichte van de klassieke

auscultatoire bloeddrukmeting is dat er geen sprake is van observatiebias of de neiging

om de gemeten getallen af te ronden tot ronde getallen. Om de betrouwbaarheid van deze

automatische bloeddrukmeters te garanderen dienen nieuwe apparaten gevalideerd te

worden tegen auscultatoire bloeddrukmetingen. Hiervoor zijn verschillende protocollen

ontwikkeld4-6. Op de website http://www.dableducational.org staan alle apparaten genoemd

die volgens een van deze protocollen zijn getest en of ze de test hebben doorstaan of niet.

Hoewel de spreekkamerbloeddrukmeting nog vaak als de ‘hoeksteen’ van het

bloeddrukmeten wordt gezien, is er steeds meer bewijs dat de bloeddrukmetingen buiten de

spreekkamer, zoals de thuis- en de ambulante of 24-uurs bloeddrukmeting, een betrouwbaardere

inschatting van de bloeddruk geven en beter correleren met cardiovasculaire uitkomsten7-11.

Dit in ogenschouw nemend is het aannemelijk dat de spreekkamerbloeddrukmeting in de

toekomst steeds meer als screeningsinstrument gebruikt zal worden om te bepalen of er

aanvullende bloeddrukmetingen buiten de spreekkamer verricht dienen te worden. Een mooi

voorbeeld hiervan is de huidige Britse NICE richtlijn, welke aanbeveelt dat bij iedereen bij

wie er voor het eerst een verhoogde bloeddruk in de spreekkamer (≥140/90mmHg) wordt

vastgesteld, een 24-uurs bloedrukmeting verricht dient te worden12.

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Een nuttige aanvulling op de bloeddrukmeter in de spreekkamer zijn geautomatiseerde

bloedrukmeetapparaten die meerdere metingen achter elkaar kunnen nemen met een

vast tijdsinterval tussen de metingen (bijvoorbeeld 1-5 minuten). Indien deze metingen

in een rustige, afgesloten ruimte worden verricht, blijkt het gemiddelde van deze

metingen goed te correleren met de gemiddelde bloeddruk overdag gemeten tijdens

24-uursbloeddrukmeting13;14. Hoewel dit wel inhoudt dat patiënten ongeveer 30 minuten

eerder in het ziekenhuis moeten verschijnen, kunnen er waardevolle bloeddrukdata worden

verkregen in het ziekenhuis, wat onnodige diagnostische procedures of therapeutische

behandelingen kan voorkomen.

Een ander discussiepunt is de relevantie van bloeddrukverschillen tussen beide armen.

Huidige richtlijnen bevelen aan om bij elk eerste polibezoek de bloeddruk aan beide kanten

te meten1, bij voorkeur tegelijkertijd omdat hierbij kleinere verschillen worden gemeten dan

wanneer men om de beurt de bloeddruk aan beide armen meet15. Hoewel het bekend is dat

een verschil in systolische bloeddruk tussen beide armen van ≥10 mmHg is geassocieerd met

cardiovasculaire ziekte en sterfte16;17, is er geen causaal verband aangetoond. Daarnaast zijn

de bloeddrukverschillen tussen beide armen slecht reproduceerbaar18, waardoor het een

beperkte waarde heeft als prognostische marker voor het individu. Er zijn meer data nodig

om het klinisch belang van grote bloeddrukverschillen tussen beide armen vast te stellen, met

name of cardiovasculaire preventie leidt tot cardiovasculaire risicoreductie.

Thuismeten van de bloeddruk

De publicatie van twee belangrijke consensusdocumenten met betrekking tot het thuismeten

van de bloeddruk in 2008 was een belangrijke stap voor het thuisbloeddrukmeten in de

klinische praktijk19;20. Hoewel deze documenten vele aspecten van het thuisbloeddrukmeten

omvatten – inclusief hoe te meten, het optimale meetschema en de aanbevolen afkapwaarden

- zijn er nog vele onbeantwoorde vragen. Zo bevelen deze richtlijnen één afkapwaarde

aan (≤135/85 mmHg) voor iedereen, ongeacht geslacht of leeftijd. De laatste Europese en

Amerikaanse richtlijnen bevelen echter aan om bij oudere patiënten (gedefinieerd als ≥80

jaar in de Europese1 en ≥60 jaar in de Amerikaanse richtlijnen21) een liberalere afkapwaarde

voor de systolische bloeddruk te gebruiken. Er is steeds meer bewijs beschikbaar dat een

hogere afkapwaarde voor de systolische bloeddruk bij ouderen ook toepasbaar is voor het

thuisbloeddrukmeten22-24, maar consensus over de exacte hoogte van de afkapwaarde is er

nog niet.

Thuismetingen van de bloeddruk zijn, net als bij de 24-uursmetingen, gevrijwaard

van het witte-jassen-effect. Ook hebben thuis- en 24-uursbloeddrukmetingen een

vergelijkbare reproduceerbaarheid en correlatie met eindorgaanschade7;25-34. Een nadeel

van thuisbloeddrukmetingen is dat de nachtelijke bloeddruk niet routinematig wordt

meegenomen, terwijl bekend is dat de nachtelijke bloeddruk de beste voorspeller is voor


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cardiovasculaire sterfte10. Recentelijk zijn er echter bloeddrukmeters voor het thuismeten

van de bloeddruk op de markt gekomen die ook ’s nachts de bloeddruk kunnen meten35;36.

Deze apparaten meten een aantal keer de bloeddruk gedurende de nacht. Het gemiddelde

van deze metingen komt aardig overeen met de gemiddelde nachtelijke bloeddruk van een

24-uurs bloeddrukmeting, en deze metingen worden door patiënten beter getolereerd dan

de nachtelijke metingen van een 24-uurs bloeddrukmeting37. Daarnaast correleren deze

nachtelijke metingen met eindorgaanschade38, ongeveer in dezelfde mate als de nachtelijke

metingen van een 24-uurs bloeddrukmeting39. Deze apparaten zien er dus veelbelovend uit,

maar prospectieve data met betrekking tot cardiovasculaire ziekte of sterfte ontbreken nog.

Een relatief nieuw fenomeen in het thuisbloeddrukmeten is het gebruik van

telemonitoring40;41. Dit houdt in dat gemeten bloeddrukdata thuis worden verzameld en dan

naar een professional worden gestuurd, meestal via een telefoon- of internetverbinding, en

tegenwoordig steeds vaker via een applicatie op de mobiele telefoon. Telemonitoring zou het

aantal bezoeken aan de arts kunnen reduceren, kan er voor zorgen dat het niet langer nodig

is om geschreven, vaak incorrecte42-46, data in een dagboek bij te houden, en kan het mogelijk

maken om gestandaardiseerde behandeling of adviezen met betrekken tot de levensstijl aan

te bieden. Een meta-analyse bestaande uit 4389 deelnemers van 11 gerandomiseerde studies

liet een significant gunstig effect zien van telemonitoring op de spreekkamerbloeddruk47,

waarbij de systolische bloeddruk gemiddeld 5.64 mmHg (95% CI 7.92-3.36), en de diastolische

bloeddruk 2.78 mmHg (95% CI 3.93-1.62) lager was dan bij patiënten die routinematige zorg

kregen aangeboden. Daarnaast behaalde een groter deel van de patiënten die telemonitoring

kregen aangeboden hun streefbloeddruk (relatieve risico 1.16, 95% CI 1.04-1.29). De

methoden die gebruikt werden in de verschillende studies van de meta-analyse varieerden

echter behoorlijk, en de follow-up duur was kort met een mediane follow-up van 24 weken.

Bovendien werd telemonitoring niet vergeleken met reguliere thuisbloeddrukmeting en

werd er niet uitgezocht of telemonitoring kosteneffectief is. Derhalve zijn er meer data nodig

om te bepalen of er een plaats is voor telemonitoring met betrekking tot het meten van de

bloeddruk in de (nabije) toekomst.

24-uurs meting van de bloeddruk

Hoewel 24-uursmeting van de bloeddruk duur en arbeidsintensief is in vergelijking met

spreekkamer- of thuis bloeddrukmeting, wordt het als een waardevol diagnosticum gezien

bij het vaststellen van hypertensie. Zoals eerder genoemd bevelen de Britse richtlijnen

uit 2011 omtrent bloeddrukmeten aan om bij iedereen die voor het eerst een verhoogde

spreekkamerbloeddruk heeft een 24-uurs bloeddrukmeting te verrichten12. In 2013 publiceerde

de ESH een belangrijke position paper met betrekking tot de 24-uursbloeddrukmeting,

wat onder meer een uitgebreide samenvatting van de literatuur tot dan toe bevatte, en

aanbevelingen over hoe en wanneer de 24-uurs bloeddrukmeting toe te passen48. Een jaar

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later verscheen er een officieel richtlijndocument49. Hierin wordt 24-uurs bloeddrukmeting

aanbevolen voor vele indicaties, waarin er onderscheid wordt gemaakt in indicaties waarin dit

strikt wordt aanbevolen, zoals het diagnosticeren van het witte jassen effect of gemaskeerde

hypertensie, en additionele indicaties zoals het vastleggen van de bloeddrukvariabiliteit

of het vastleggen van hypotensie. Ook is de definitie van witte jassen en gemaskeerde

hypertensie uitgebreid, waarbij de nachtelijke bloeddruk nu ook wordt meegenomen, en zijn

er duidelijke aanbevelingen wanneer de 24-uurs bloeddrukmeting moet worden herhaald.

Een interessante ontwikkeling is de introductie van 24-uurs bloeddrukmeters die naast de

gebruikelijke brachiale bloeddruk ook op een niet-invasieve manier de centrale bloeddruk

kunnen bepalen50-54. De onderliggende gedachte is dat de bloeddruk bij de aortawortel een

betere weergave geeft van de ware opgelegde druk op het hart dan de aan de bovenarm

gemeten bloeddruk. De studies die gedaan zijn over dit onderwerp suggereren inderdaad dat

de centrale bloeddruk een betere voorspeller is voor het ontwikkelen van hart- en vaatziekten

dan de brachiale bloeddruk55-58. Of dit ook daadwerkelijke betekent dat de centrale 24-uurs

bloeddruk een betere voorspeller is dan de 24-uurs brachiale bloeddruk dient echter nog te

worden onderzocht.

Verschillen tussen thuis- en 24-uursmeting van de bloeddruk

Zowel thuis- als 24-uursbloeddrukmeting correleren beter met eindorgaanschade en

cardiovasculaire ziekte dan de conventionele bloeddrukmeting in de spreekkamer59-63. Ook

hebben ze een vergelijkbare reproduceerbaarheid en zijn ze in staat om patiënten met witte

jassen en gemaskeerde hypertensie te identificeren 64-68. Hoewel er vele studies zijn die thuis-

en 24-uursbloeddrukken vergeleken hebben met de spreekkamerbloeddruk, zijn er verrassend

weinig studies die het verschil in thuis- en 24-uursbloeddruk vergeleken hebben. Uit één

studie die keek naar subklinische cerebrovasculaire schade onder 1007 Japanse personen

van 55 jaar of ouder, bleek dat de gemiddelde thuisbloeddruk sterker geassocieerd was

met het risico op het krijgen van atherosclerose in de carotiden dan de 24-uurs bloeddruk69.

Dit suggereert dat beide modaliteiten mogelijk verschillende type orgaanschade kunnen

voorspellen. Uit een systematisch overzichtsartikel blijkt echter dat thuis- en 24-uursmeting

van de bloeddruk een vergelijkbare correlatie hebben met eindorgaanschade, gedefinieerd

als de echografisch vastgestelde linker ventrikel massa index34. Voor andere indicatoren van

eindorgaanschade, zoals de glomerulaire filtratie snelheid, de intima-media dikte van de

carotis en de polsgolfsnelheid was het aantal studies te klein om een betrouwbare vergelijking

te kunnen maken. Cerebrovasculaire eindpunten, zoals in de eerder genoemde Japanse

studie, werden niet in dit artikel meegenomen. In een prospectieve studie waarin de data van

twee Finse cohort studies werden gecombineerd, werden verschillende predictiemodellen

voor het voorspellen van een gecombineerde uitkomst van hart- en vaatziekte en -sterfte met

elkaar vergeleken. In deze modellen werd onder meer gekeken naar de spreekkamer-, thuis-,


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en 24-uursbloeddruk70. Vergeleken met de spreekkamerbloeddruk, leidde het toevoegen

van de 24-uursbloeddruk tot een significante verbetering van het predictiemodel, terwijl

het toevoegen van de thuisbloeddrukmeting niet leidde tot een verbetering. Uit de PAMELA

studie, waarin meer dan 2000 patiënten werden geïncludeerd71, bleek dat bij patiënten met

een verhoogde thuisbloeddruk maar een normale 24-uursbloeddruk of omgekeerd, een risico

op het ontwikkelen van hart- en vaatziekten hadden wat hoger was dan dat van patiënten

met een normale bloeddruk bij beide meetmethoden, maar lager was dan dat van patiënten

met een hoge bloeddruk bij beide meetmethoden. In de ruwe data leek het risico groter bij

patiënten met een hoge thuis- en normale 24-uursbloeddrukmeting dan andersom, wat bij

14% van de studiedeelnemers voorkwam. Echter, na correctie voor leeftijd en geslacht was dit

verschil niet meer significant. Daarnaast werden slechts 2 thuisbloeddrukmetingen verricht,

waar tegenwoordig een minimaal aantal van 12 metingen wordt aanbevolen19, en werden

afkapwaarden gebruikt die nu niet meer gangbaar zijn. In een recentere studie waarin 831

poliklinische patiënten werden geïncludeerd werd gekeken in hoeverre de bloeddrukstatus

veranderde na het toevoegen van de 24-uursbloeddruk aan de spreekkamer- en thuisbloeddruk72.

Patiënten werden eerst geclassificeerd aan de hand van spreekkamer- en thuisbloeddruk als

normotensief (bij beiden een normale bloeddruk), aanhoudende hypertensie (bij beiden een

verhoogde bloeddruk), witte-jassen-hypertensie (verhoogde spreekkamerbloeddruk met

een normale thuisbloeddruk) of gemaskeerde hypertensie (normale spreekkamerbloeddruk

met een verhoogde thuisbloeddruk). Vervolgens werd de thuisbloeddruk vervangen

voor de 24-uursbloeddruk. Hierdoor werd de risicoclassificatie naar beneden bijgesteld

van gemaskeerde hypertensie naar een normale bloeddruk (n=24), of van aanhoudende

hypertensie naar witte-jassen-hypertensie (n=9) in 33 (4.0%) van de deelnemers. Daarnaast

werd het risico omhoog bijgesteld van een normale bloedruk naar gemaskeerde hypertensie

(n=179), of van witte jassen hypertensie naar aanhoudende hypertensie (n=44) in 223 (26.8%)

van de deelnemers. De deelnemers waarbij het risico op basis van de 24-uursbloeddruk

omhoog werd bijgesteld hadden een hogere albumine-kreatinine ratio in de urine en een

hogere polsgolfsnelheid vergeleken met de deelnemers waarbij de bloeddrukstatus niet

veranderde. Er werd echter niets gerapporteerd over eindorgaanschade bij patiënten die een

verhoogde thuis- maar normale 24-uursbloeddruk hadden. In de International Database of

HOme blood pressure in relation to Cardiovascular Outcome (IDHOCO), een database bestaande

uit vijf populatiestudies waarin gebruik gemaakt wordt van thuisbloeddrukmeting inclusief

data met betrekking tot cardiovasculaire uitkomsten73, werd aangetoond dat onbehandelde

patiënten met een verhoogde spreekkamer-, maar een normale thuisbloeddruk (witte-jassen-

hypertensie) een verhoogd cardiovasculair risico hadden vergeleken met normotensieve

deelnemers. Uit gegevens van de International Database of Ambulatory blood pressure in

relation to Cardiovascular Outcome (IDACO), een grote database bestaande uit 24-uurs

bloeddrukmetingen van personen uit 11 landen, blijkt echter dat onbehandelde patiënten met

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een verhoogde spreekkamer- maar normale 24-uursbloeddruk geen verhoogd cardiovasculair

risico hebben in vergelijking met normotensieve deelnemers74;75. Hoewel beide databasen een

vergelijkbaar aantal deelnemers bevatten met een vergelijkbare follow-up duur, zijn ze wel

afkomstig van verschillende datasets, wat betekent dat deze vergelijking met voorzichtigheid

geïnterpreteerd dient te worden. Desalniettemin lijkt het gerechtvaardigd te concluderen dat

thuis- en 24-uursbloeddrukmeting twee verschillende entiteiten zijn, die complementair aan

elkaar zijn, maar niet simpelweg inwisselbaar. Als we al het bovengenoemde bij elkaar optellen,

lijkt van alle bloeddrukmodaliteiten de 24-uursbloeddrukmeting het meest betrouwbaar,

gezien het beter correleert met hart- en vaatziekten dan de spreekkamerbloedruk, het in

tegenstelling tot thuisbloeddrukmeting leidt tot verbetering van een risicopredictiemodel

voor het voorspellen van hart- en vaatziekten ten opzichte van de spreekkamerbloeddruk en

het patiënten met gemaskeerde hypertensie identificeert met meer eindorgaanschade dan

patiënten die met thuisbloeddrukmeting worden geïdentificeerd. Daarbij lijkt het zo te zijn

dat patiënten met witte-jassen-hypertensie op basis van de 24-uursbloeddruk geen verhoogd

cardiovasculair risico hebben, in tegenstelling tot patiënten met witte-jassen-hypertensie op

basis van de thuisbloeddruk. Een goede, prospectieve studie waarin beide modaliteiten met

elkaar worden vergeleken en wordt gekeken naar eindorgaanschade of cardiovasculaire ziekte

of sterfte om uitsluitsel te geven ontbreekt helaas. Tot die tijd lijkt het dus gerechtvaardigd om

de 24-uursbloeddrukmeting te beschouwen als de gouden standaard voor het vaststellen van

hypertensie, maar ook om het thuismeten van de bloeddruk als een waardig alternatief te zien

indien dit niet beschikbaar is.


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(21) James PA, Oparil S, Carter BL et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA 2014;311:507-520.

(22) Aparicio LS, Thijs L, Boggia J et al. Defining thresholds for home blood pressure monitoring in octogenarians. Hypertension 2015;66:865-873.

(23) Barochiner J, Aparicio LS, Cuffaro PE et al. Home blood pressure profile in very elderly hypertensives: should we use the same thresholds as in younger patients? J Am Soc Hypertens 2015;9:184-190.

(24) Nomura K, Asayama K, Thijs L et al. Thresholds for conventional and home blood pressure by sex and age in 5018 participants from 5 populations. Hypertension 2014;64:695-701.





(29) Mule G, Caimi G, Cottone S et al. Value of home blood pressures as predictor of target organ damage in mild arterial hypertension. Journal of Cardiovascular Risk 2002;9:123-129.






(35) Hosohata K, Kikuya M, Ohkubo T et al. Reproducibility of nocturnal blood pressure assessed by self-measurement of blood pressure at home. Hypertens Res 2007;30:707-712.

(36) Chonan K, Kikuya M, Araki T et al. Device for the self-measurement of blood pressure that can monitor blood pressure during sleep. Blood Press Monit 2001;6:203-205.

(37) Stergiou GS, Nasothimiou EG, Destounis A, Poulidakis E, Evagelou I, Tzamouranis D. Assessment of the diurnal blood pressure profile and detection of non-dippers based on home or ambulatory monitoring. Am J Hypertens 2012;25:974-978.

(38) Kario K, Hoshide S, Haimoto H et al. Sleep Blood Pressure Self-Measured at Home as a Novel Determinant of Organ Damage: Japan Morning Surge Home Blood Pressure (J-HOP) Study. J Clin Hypertens (Greenwich) 2015.

(39) Andreadis EA, Agaliotis G, Kollias A, Kolyvas G, Achimastos A, Stergiou GS. Night-time home versus ambulatory blood pressure in determining target organ damage. J Hypertens 2016;34:438-444.


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(40) Parati G, Omboni S. Role of home blood pressure telemonitoring in hypertension management: an update. Blood Press Monit 2010;15:285-295.

(41) Sivakumaran D, Earle KA. Telemonitoring: use in the management of hypertension. Vasc Health Risk Manag 2014;10:217-224.

(42) Johnson KA, Partsch DJ, Rippole LL, Mcvey DM. Reliability of self-reported blood pressure measurements. Archives of Internal Medicine 1999;159:2689-2693.

(43) Mengden T, Alvarez E, Beltran B, Kraft K, Vetter H. Reliability of reporting self-measured blood pressure values of hypertensive patients. Journal of Hypertension 1998;16:S271.

(44) Myers MG. Reporting bias in self-measurement of blood pressure. Blood Pressure Monitoring 2001;6:181-183.

(45) Nordmann A, Frach B, Walker T, Martina B, Battegay E. Reliability of patients measuring blood pressure at home: prospective observational study. British Medical Journal 1999;319:1172.

(46) van der Hoeven NV, van den Born BJ, Cammenga M, van Montfrans GA. Poor adherence to home blood pressure measurement schedule. J Hypertens 2009;27:275-279.

(47) Omboni S, Guarda A. Impact of home blood pressure telemonitoring and blood pressure control: a meta-analysis of randomized controlled studies. Am J Hypertens 2011;24:989-998.

(48) O’Brien E, Parati G, Stergiou G et al. European society of hypertension position paper on ambulatory blood pressure monitoring. J Hypertens 2013;31:1731-1768.

(49) Parati G, Stergiou G, O’Brien E et al. European Society of Hypertension practice guidelines for ambulatory blood pressure monitoring. J Hypertens 2014;32:1359-1366.

(50) Horvath IG, Nemeth A, Lenkey Z et al. Invasive validation of a new oscillometric device (Arteriograph) for measuring augmentation index, central blood pressure and aortic pulse wave velocity. J Hypertens 2010;28:2068-2075.

(51) Jatoi NA, Mahmud A, Bennett K, Feely J. Assessment of arterial stiffness in hypertension: comparison of oscillometric (Arteriograph), piezoelectronic (Complior) and tonometric (SphygmoCor) techniques. J Hypertens 2009;27:2186-2191.

(52) Luzardo L, Lujambio I, Sottolano M et al. 24-h ambulatory recording of aortic pulse wave velocity and central systolic augmentation: a feasibility study. Hypertens Res 2012;35:980-987.

(53) Parati G, De BM. Evaluating aortic stiffness through an arm cuff oscillometric device: is validation against invasive measurements enough? J Hypertens 2010;28:2003-2006.

(54) Trachet B, Reymond P, Kips J et al. Numerical validation of a new method to assess aortic pulse wave velocity from a single recording of a brachial artery waveform with an occluding cuff. Ann Biomed Eng 2010;38:876-888.

(55) Pini R, Cavallini MC, Palmieri V et al. Central but not brachial blood pressure predicts cardiovascular events in an unselected geriatric population: the ICARe Dicomano Study. J Am Coll Cardiol 2008;51:2432-2439.

(56) Roman MJ, Devereux RB, Kizer JR et al. Central pressure more strongly relates to vascular disease and outcome than does brachial pressure: the Strong Heart Study. Hypertension 2007;50:197-203.

(57) Wang KL, Cheng HM, Chuang SY et al. Central or peripheral systolic or pulse pressure: which best relates to target organs and future mortality? J Hypertens 2009;27:461-467.

(58) Williams B, Lacy PS, Thom SM et al. Differential impact of blood pressure-lowering drugs on central aortic pressure and clinical outcomes: principal results of the Conduit Artery Function Evaluation (CAFE) study. Circulation 2006;113:1213-1225.


(60) Mule G, Caimi G, Cottone S et al. Value of home blood pressures as predictor of target organ damage in mild arterial hypertension. J Cardiovasc Risk 2002;9:123-129.

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(69) Hara A, Tanaka K, Ohkubo T et al. Ambulatory versus home versus clinic blood pressure: the association with subclinical cerebrovascular diseases: the Ohasama Study. Hypertension 2012;59:22-28.

(70) Niiranen TJ, Maki J, Puukka P, Karanko H, Jula AM. Office, home, and ambulatory blood pressures as predictors of cardiovascular risk. Hypertension 2014;64:281-286.

(71) Mancia G, Facchetti R, Bombelli M, Grassi G, Sega R. Long-term risk of mortality associated with selective and combined elevation in office, home, and ambulatory blood pressure. Hypertension 2006;47:846-853.

(72) Zhang L, Li Y, Wei FF et al. Strategies for classifying patients based on office, home, and ambulatory blood pressure measurement. Hypertension 2015;65:1258-1265.

(73) Stergiou GS, Asayama K, Thijs L et al. Prognosis of white-coat and masked hypertension: International Database of HOme blood pressure in relation to Cardiovascular Outcome. Hypertension 2014;63:675-682.

(74) Franklin SS, Thijs L, Hansen TW et al. Significance of white-coat hypertension in older persons with isolated systolic hypertension: a meta-analysis using the International Database on Ambulatory Blood Pressure Monitoring in Relation to Cardiovascular Outcomes population. Hypertension 2012;59:564-571.

(75) Hansen TW, Kikuya M, Thijs L et al. Prognostic superiority of daytime ambulatory over conventional blood pressure in four populations: a meta-analysis of 7,030 individuals. J Hypertens 2007;25:1554-1564.


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AUTHORS AND AFFILIATIONS

Fouad Amraoui

Departments of Internal and Vascular Medicine, Academic Medical Center of the University of


Bert-Jan H. van den Born



Lex Burdorf

Department of Public Health, Erasmus MC, Rotterdam, The Netherlands

Marianne Cammenga



Stephanie Klein Ikkink



Coen K. van Kalken

NIPED Research Foundation, Amsterdam, The Netherlands

Sophie Lodestijn



Gert A. van Montfrans



Stephanie Nanninga



Maurice A.J. Niessen

NIPED Research Foundation, Amsterdam, The Netherlands

Ron J.G. Peters

Department of Cardiology, Academic Medical Center of the University of Amsterdam, the

Netherlands

Aart Schene

Department of Psychiatry, Academic Medical Center, University of Amsterdam; Amsterdam,

the Netherlands

Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands

Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen,

the Netherlands

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Authors and affi liations

170

Marieke B. Snijder

Department of Public Health, Academic Medical Center of the University of Amsterdam, the

Netherlands

Erik S.G. Stroes



Irene G.M. Van Valkengoed

Department of Public Health, Academic Medical Center, Amsterdam, the Netherlands

Ieke Visser

Department of Psychiatry, Academic Medical Center, University of Amsterdam; Amsterdam,

the Netherlands

Liffert Vogt

Department of Nephrology, Academic Medical Center, Amsterdam, the Netherlands

Stephanie E. Wessel




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DANKWOORD

Na vele jaren ploeteren is er met het verschijnen van dit boekje een einde gekomen aan een

10 jaar durend avontuur. Hoewel dit avontuur, met name op het einde, vaak in eenzaamheid

werd beleefd, ben ik onderweg vele mensen tegengekomen die mij op vele manieren hebben

geholpen. Daarom wil ik iedereen bedanken die om wat voor reden dan ook heeft bijgedragen

aan dit proefschrift. Een aantal personen wil ik in het bijzonder bedanken.

Ten eerste mijn co-promotoren dr. Bert-Jan van den Born en dr. Roderik Kraaijenhagen.

Beste Bert-Jan, lang geleden is het allemaal begonnen als een studentenproject voor mijn

wetenschappelijke stage. Destijds had ik zeker nog niet de ambitie om te promoveren, maar

jouw uitgebreide kennis en enthousiasme werkten aanstekelijk. Daarnaast heb ik het gemak

waarmee jij te bereiken was altijd zeer gewaardeerd. Ik verheug me mede daarom nu ook al op

mijn differentiatie bij de vasculaire geneeskunde, waar ik opnieuw onder jouw hoede terecht

zal komen. Beste Roderik, zonder jou was mijn hele promotietraject waarschijnlijk niet eens tot

stand gekomen. Ik heb genoten van jouw energieke houding en jouw gave om bij elke tegenslag

nieuwe kansen te zien. Daarnaast gaf je mij de mogelijkheid om bij het NIPED te kunnen werken,

wat ik zeer gewaardeerd heb.

Mijn promotor, professor dr. Erik Stroes. Beste Erik, hoewel je niet direct bij alle studies was

betrokken heb ik jouw helikoptervisie en jouw vermogen om een studie in enkele minuten te

doorgronden en tot een plan van aanpak te komen altijd zeer bewonderd en gewaardeerd.

De leden van de promotiecommissie: prof. dr. J.B.L. Hoekstra, prof. dr. P.M.M. Bossuyt, prof. dr. J.J.

van Lieshout, prof. dr. Y.M. Smulders, prof. dr. A J. Smit en prof. dr. R. J. G. Peters. Allen hartelijk dank

voor de bereidheid om zitting te nemen in de commissie en het inzetten van hun deskundigheid

ter beoordeling van dit proefschrift.

Dr. Gert van Montfrans. Beste Gert, ik heb jou altijd gezien als een mentor. Ik heb veel bewondering

voor jouw encyclopedische kennis op het gebied van hypertensie (en daarbuiten). Daarnaast is

menig hoofdstuk in dit proefschrift er qua leesbaarheid aanzienlijk op vooruit gegaan dankzij

jouw vermogen om de juiste linguïstische smeuïgheid aan een tekst toe te voegen zonder

afbreuk te doen aan de wetenschappelijke inhoud.

Marianne Cammenga. Beste Marianne, hoewel ik een proefschrift vol heb geschreven over

bloeddrukmeten, kan ik (en naar mijn idee niemand in het AMC) niet tippen aan jouw

praktijkervaring met bloeddruk meten. Dank voor alle bloeddrukmetingen voor studies zowel

binnen als buiten dit proefschrift. Daarnaast was het voor mij natuurlijk ook gewoon prettig om

een mede-Zaankanter op de werkvloer te hebben.

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Dankwoord

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Veel dank gaat ook uit aan mijn collega’s van de vasculaire geneeskunde. De fijne sfeer die er

heerst is werkelijk uniek en heerlijk om in te werken, met de juiste balans tussen hard werken en

ontspannen. Als eerste wil ik (uiteraard) Joyce bedanken. Joyce, zoals elke promovendus was ik

vanaf dag 1 afhankelijk van jouw kennis en kunde om ook maar iets te presteren. Nu je (bijna)

weg bent op de vasculaire is er echt een tijdperk ten einde gekomen en zal de afdeling nooit

meer zo zijn als hij was. Ook wil ik mijn kamergenoten op F4-139 bedanken. Joost ‘Besselmans’,

met bewondering heb ik gekeken hoe je ondanks je vele OCD-achtige trekken stoïcijns kon

doorwerken en al luisterend naar ‘het foute uur’ vele hoogwaardige artikelen wist te publiceren.

Daarnaast kon ik erg genieten van je West-Friese nuchterheid en gezelligheid tijdens de vele

borrels. Juul, onze zonnestraal in de kamer (met wellicht hier en daar een klein wolkje). Ik vond

het gezellig samen te werken met als hoogtepunt ons uitje naar Bad Religion in de Melkweg.

Nick, mijn co-genootje. Onze rust en rots in de branding op onze kamer. Als enige PSV-er tussen

alle ajacieden hield je je prima staande. Ik hoop dat mijn proefschrift de eer krijgt te mogen

fungeren als jouw beeldschermondersteuner.

Verder wil ik ‘de Bezemkast’ bedanken voor de gezellige uurtjes onder het genot van een goed

gevulde Barcelonamok koffie (waarvan ik overigens niet weet waar deze gebleven is). Fouad,

wij zijn beiden ooit als student aan ons promotietraject begonnen en het was leuk om zoveel

samen op te trekken. Het is daarom ook leuk dat we nog een artikel samen hebben geschreven.

Ik heb je nuchtere humor altijd zeer gewaardeerd en ik hoop dat we elkaar nog veel zien in de

toekomst. Daan, mijn jaargenoot, ook jij bent ooit begonnen als student bij de hypertensieclub.

Met Fouad vormden we een mooi hypertensieblok op een doorgaans door lipiden en stolling

gedomineerde afdeling. Ik hoop dat we elkaar blijven tegenkomen, ook na het promoveren.

Ruud, hoewel je niet behoort tot de hypertensieclub, had je er zomaar een van kunnen zijn. Het

is dan ook erg leuk dat we elkaar in Beverwijk weer tegen zijn gekomen, zowel op zaal als op de

poli.

Veel dank gaat ook uit naar de overige (oud-)leden van het ‘hypertensieclubje’. Lizzy, Liffert, Leon,

Nanne, Bas, Inge, Rick, Nienke, Linn, René, Ties, Fares en Doortje, alleen heel erg bedankt voor

een leerzame tijd!

Verder wil ik natuurlijk ook al mijn andere collega’s van F4 bedanken. Annick, Fleur, Kang, Laura,

Luuk, Marjolein B, Marjolein van den B, Nick, Sophie, Suzanne, Thijs, Whitney, Aart, Andrea, Ankie,

Barbara, Brigitte, Carlijne, Danka, Danny, Elise, Josien, Katia, Maurits, Katrijn, Maayke, Mandy,

Mara, Marjet, Meeike, Paulien, Remi, Stefano en iedereen wie ik nu vergeten ben: allen heel erg

bedankt voor een heel gezellige tijd!


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Ook veel dank gaat uit naar alle medewerkers van de polikliniek interne en vasculaire

geneeskunde die mij hebben bijgestaan, met in het bijzonder veel dank aan Andrea Schmitz-

Piel. Andrea, dank voor al die keren dat je me hebt geholpen met het regelen van een kamer, het

juiste formulier en talloze andere problemen!

Mijn collega’s van de stichting Begeleide Zelfzorg wil ik ook bedanken voor een fijne, gezellige en

leerzame tijd. Sabine, Lucretia, Lotte, Charissa, Luca en Thijs, allen heel erg bedankt!

Mijn dank gaat ook uit naar mijn collega’s van het NIPED, waar ik met veel plezier heb gewerkt

tijdens mijn promotietijd. Hierbij wil ik Maurice in het bijzonder bedanken. Beste Maurice, ik

heb erg genoten van onze vruchtbare samenwerking. Met veel bewondering, respect en enige

jaloezie heb ik gezien hoe jij ondanks alle drukte het voor elkaar kreeg een prachtig proefschrift

af te ronden. Ik hoop dat deze samenwerking in de toekomst nog een vervolg zal krijgen.

Ook ben ik de leden van Trialbureau van de vasculaire geneeskunde veel dank verschuldigd. Met

name Elsa en Liesbeth wil ik bedanken voor het altijd geduldig klaarstaan en hulp voor de vele

patiëntbezoeken voor menig lipideverlagend middel, maar ook vooral voor de gezelligheid!

Mijn dank gaat ook uit naar de overige stafleden van de vasculaire geneeskunde. Beste Saskia,

Max, Michiel en Kees, allen hartelijk bedankt voor een fijne tijd op de afdeling. Het is bijzonder

om te kunnen werken op een afdeling waar zo een fijne sfeer hangt en waar kwalitatief zulk

hoogstaand onderzoek wordt verricht.

Ook wil ik mijn opleiders prof. Dr. Suzanne Geerlings van het AMC en dr. Niek Valk en Hanneke

van den Broek uit het Rode Kruis Ziekenhuis bedanken voor de prettige begeleiding tijdens de

opleiding. Daarnaast ook veel dank voor mijn overige collega’s in het Rode Kruis Ziekenhuis, en

mijn collega’s op de cardiologie en de IC van het AMC bedanken voor een fijne tijd.

Verder wil ik een aantal studenten, van wie de meesten inmiddels ook werkzaam zijn als arts,

bedanken die hebben bijgedragen aan de verschillende onderzoeken. Stephanie Wessels,

Stephanie Nanninga, Sophie Lodestijn, Jacqueline van Gorp en Stephanie Klein Ikkink, allen

hartelijk dank voor jullie onmisbare bijdrage.

Mijn paranimfen Paul ‘Corinoco’ den Brave en Noach de Haas. Wat is het fijn om een dag als

deze twee goede vrienden naast mijn zijde te hebben. Ik weet daarom zeker dat er tijdens mijn

promotie niets mis kan gaan.

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174

Stan, Billy, al meer dan 25 jaar kennen wij elkaar. Naast dat het fijn is om je als vriend te hebben

ben ik ook zeer vereerd dat je deze prachtige omslag van mijn kaft hebt willen ontwerpen!

Gelukkig bestaat het leven uit meer dan alleen promoveren, en wil ik juist ook de mensen

bedanken die gezorgd hebben voor alle mooie ontspanning buiten dit boekje om tijdens

pokeravondjes (ik doe weer mee!), etentjes, concerten, speciaalbiertjes en ga zo maar door.

Uiteraard ook dank aan de leden van mij band Trip, want muziek blijft toch de mooiste uitlaatklep

die er bestaat.

Lieve papa en mama, ook jullie wil ik bedanken voor een fijne jeugd en voor het feit dat jullie

me altijd de vrijheid gaven om mijn weg in te slaan en zo te komen tot dit proefschrift. Mark en

Wendy, en natuurlijk ook Leon, Julian en Silvan, het is fijn om jullie als familie om me heen te

hebben. Dit geldt ook voor mijn schoonfamilie, Jan, Gerdien, Wouter en Anne, dank voor alle

warmte en gastvrijheid gedurende al die jaren.

Emma, liefste Emma. Waar moet ik beginnen met jou te bedanken? Voor jou was het misschien

wel zwaarder dan voor mij, met al die uren dat je niets aan me had omdat ik ‘weer even aan mijn

proefschrift’ zat. Desondanks heb je me altijd gesteund, en ik prijs me dan ook zeer gelukkig dat

ik jou in mijn leven heb. Lieve Emma, dank voor alles, maar vooral voor dat je mijn leven zoveel

mooier maakt.


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PORTFOLIO

PhD student: N. V. van der HoevenPhD period: January 2012-March 2014 Supervisor: Prof. dr. E.S.G. StroesCo-supervisors: dr. B.J.H. Van den Born; dr. R.A. Kraaijenhagen

1. PhD training

Year Workload(ECTS)

General coursesAMC world of Science, Graduateschool AMC Practical Biostatistics, Graduateschool AMCClinical Epidemiologiy, Graduateschool AMCBasiscursus Regelgeving en Organisatie voor Klinisch Onderzoekers, Graduateschool AMCGood Clinical Practice, Clinical Research Unit AMC

201220122012

20122012

0.71.11.0

1.00.4

Specifi c courses Advanced Topics in Biostatics, Graduateschool AMCDe kunst van het doseren, Federatie Nederlandse Trombosediensten

20132013

2.11.0

Seminars, workshops and master classesWeekly department seminars (Vasculare Medicine)Workshop subsidie aanvragen, ZonMW

2012-20142013

3.00.4

Presentations and confererencesEuropean Society of Hypertension, Milan, Italy. Two oral presentations.Cardio Vascular Conference, Noorwijkerhout, the Netherlands. Guided poster presentation.European Society of Hypertension, Londen, UK. Poster presentation.European Society of Hypertension, Milan, Italy. Poster presentation.European Society of Hypertension, Berlin, GermanyPoster presentation. European Society of Hypertension, Milan, Italy.

2013

201320122009

20082007

1.5

1.01.01.0

1.00.5

2. Teaching

Year Workload (ECTS)

SupervisingStudent internship Stephanie Wessels, Medicine, University of Amsterdam.Student internship Sophie Lodestijn, Medicine, University of Amsterdam.Student internship Stephanie Nanninga, Medicine, University of Amsterdam.Student internship Stephanie Klein Ikkink, Medicine, University of Amsterdam.

200920102010

2012

1.01.01.0

1.0

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3. Parameters of Esteem

Year

GrantsEuropean Society of Hypertension, Berlin, Germany. Travel Grant.European Society of Hypertension, Milan, Italy. Travel Grant.European Society of Hypertension, Milan, Italy. Travel Grant

200820092013

4. PublicationsYear

Peer reviewedA six question screen to identify subjects at increased CVD Risk. Van der Hoeven NV, Niessen MAJ, Burdorf A, Kraaijenhagen RA, van den Born BJ. BMC Cardiovascular Disorders. 2015 Oct 30;15:140.

Severe hypertension related to caff einated coff ee and tranylcypromineVan der Hoeven N, Visser I, Schene A, van den Born BJ. Ann Intern Med. 2014 May 6;160(9):657-8.

Mortality and cardiovascular risk in patients with a history of malignant hypertension: a case-control studyVan der Hoeven NV, Amraoui F, van Valkengoed IGM, Vogt L, van den Born BJ. Journal of Clinical Hypertension. 2014 Feb;16(2):122-126.

Home blood pressure measurement as a screening tool for hypertension in a web-based worksite health promotion programme Niessen MAJ, van der Hoeven NV, van den Born BJ, van Kalken C, Kraaijenhagen RA. Eur J Public Health. 2014 Oct;24(5):776-81.

Simultaneous compared to sequential blood pressure measurement results in smaller inter-arm blood pressure diff erences: a randomized cross-over trialvan der Hoeven NV, Lodestijn S, Nanninga S, van Montfrans GA, van den Born BJ. Journal of Clinical Hypertension 2013. Nov;15(11):839-44.

‘Diagnostic mode’ improves adherence to the home blood pressure measurement schedule. Wessels SE, van der Hoeven NV, Cammenga M, van Montfrans GA, van den Born BJ. Blood Press Monit. 2012 Oct;17(5):214-9.

Endothelial dysfunction, platelet activation, coagulation activation and fi brinolytic activity in patients with hypertensive crisisvan den Born BJ, Löwenberg EC, van der Hoeven NV, de Laat B, Meijers JC, Levi M, van Montfrans GA. J Hypertens. 2011 May;29(5):922-7.

Reliability of palpation of the radial artery compared with auscultation of the brachial artery in measuring SBP.van der Hoeven NV, van den Born BJ, van Montfrans GA. J Hypertens. 2011 Jan;29(1):51-5.

Poor adherence to home blood pressure measurement schedule.van der Hoeven NV, van den Born BJ, Cammenga M, van Montfrans GA.J Hypertens. 2009 Feb;27(2):275-9.

2015

2014

2014

2014

2013

2012

2011

2011

2009


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Association between thrombotic microangiopathy and reduced ADAMTS13 activity in malignant hypertension.van den Born BJ, van der Hoeven NV, Groot E, Lenting PJ, Meijers JC, Levi M, van Montfrans GA. Hypertension. 2008 Apr;51(4):862-6.

Fibrinogen Aalpha312 and Bbeta448 polymorphisms are not related to bleeding during oral vitamin K-antagonist treatment.Garcia AA, van der Hoeven NV, Boellaard TN, van der Heijden JF, Groot AP, Reitsma PH. Thromb Haemost. 2007 Apr;97(4):676-8.

2008

2007

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CURRICULUM VITAE

Niels Vincent van der Hoeven was born on July 25th 1984 in Zaandam. In 2002 he graduated from

secondary school at the St. Michaël College in Zaandam, after which he studied Medicine at the

University of Amsterdam in the AMC. In 2006 he started a scientific internship at the department

of vascular medicine in the AMC under supervision of dr. Bert-Jan van den Born. The same year he

started his study Psychology at the University of Amsterdam, from which he graduated in 2009

for his Bachelor of Science in the field of psychonomics. In 2012 he graduated for his M.D., after

which he became a PhD-candidate at the department of vascular medicine in the AMC with prof.

dr. Erik Stroes as his supervisor, and dr. Bert-Jan van den Born and dr. Roderik Kraaijenhagen as

his co-supervisors. In 2014 he started his specialization in Internal Medicine at the AMC under

supervision of prof. dr. Suzanne Geerlings in the AMC, and dr. Niek Valk and Hanneke van den

Broek at the Rode Kruis Ziekenhuis in Beverwijk.

Niels Vincent van der Hoeven werd op 25 juli 1984 geboren in Zaandam. In 2002 behaalde hij

zijn VWO diploma aan het St. Michaël College te Zaandam, waarna hij begon aan de studie

geneeskunde aan de Universiteit van Amsterdam in het AMC. In 2006 begon hij met zijn

wetenschappelijke stage op de afdeling vasculaire geneeskunde bij dr. Bert-Jan van den Born in

het AMC. In hetzelfde jaar startte hij met de studie Psychologie aan de Universiteit van Amsterdam.

In 2009 behaalde hij hiervoor zijn Bachelor of Science in de richting van de psychonomie

(functieleer). In 2012 behaalde hij zijn artsendiploma en vervolgde hij zijn onderzoek op de

vasculaire geneeskunde als promovendus met prof. dr. Erik Stroes als zijn promotor en dr. Bert-

Jan van den Born en dr. Roderik Kraaijenhagen als zijn copromotoren. In 2014 startte hij zijn

opleiding Interne Geneeskunde aan het AMC met prof. dr. Suzanne Geerlings als hoofdopleider.

Inmiddels is hij in het kader van zijn opleiding werkzaam in het Rode Kruisziekenhuis te Beverwijk,

met dr. Niek Valk en later Hanneke van den Broek als opleiders.


UITNODIGINGVoor het bijwonen van de openbare verdediging van

het proefschrift

POLDERWEG 132, 1093KP [email protected]

Paranimfen:Paul den BraveNoach de Haas

Op vrijdag 28 oktober 201612:00 uur in de Agnietenkapel

Oudezijds Voorburgwal 231Amsterdam



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