Risks of Death and Stroke in Patients Undergoing ... Atrial fibrillation ... Database (NHIRD)....

DOI: 10.1161/CIRCULATIONAHA.115.018294

1

Risks of Death and Stroke in Patients Undergoing Hemodialysis With New-

Onset Atrial Fibrillation: A Competing-Risk Analysis of a Nationwide Cohort

Running title: Shih et al.; Death and stroke in patients with dialysis and AF

Chia-Jen Shih, MD1,2,3*; Shuo-Ming Ou, MD1,4,5*; Pei-Wen Chao, MD6,7;

Shu-Chen Kuo, MD, PhD1,8,9; Yi-Jung Lee, MD1,10; Chih-Yu Yang, MD, PhD1,4;

Der-Cherng Tarng MD, PhD1,4; Chih-Ching Lin, MD, PhD1,4; Po-Hsun Huang, MD, PhD1,11;

Szu-Yuan Li, MD, PhD1,4; Yung-Tai Chen, MD1,5,12

1School of Medicine, National Yang-Ming University, Taipei, Taiwan; 2Dept of Medicine, Taipei Veterans General Hospital, Yuanshan Branch, Yilan, Taiwan; 3Deran Clinic, Yilan,

Taiwan; 4Division of Nephrology, Dept of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; 5Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan; 6Dept of Anesthesiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; 7School of Medicine, Taipei Medical University, Taipei, Taiwan; 8National Institute of Infectious Diseases

and Vaccinology, National Health Research Institutes, Miaoli County, Taiwan; 9Division of Infectious Diseases, Taipei Veterans General Hospital, Taipei, Taiwan; 10Dept of Neurology,

Taipei City Hospital, Ren Ai Branch, Taipei, Taiwan; 11Division of Cardiology, Dept of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; 12Division of Nephrology, Dept of

Medicine, Taipei City Hospital, Heping Fuyou Branch, Taipei, Taiwan *contributed equally

Address Correspondence:Yung-Tai Chen, MD Szu-Yuan Li, MD, PhD Dept of Nephrology Division of Nephrology, Dept of Medicine Taipei City Hospital Heping Fuyou Branch Taipei Veterans General Hospital Taipei, Taiwan, 112 Taipei, Taiwan, 112 Tel: 886-2-2388-9595 Tel: 886-2- 2871 2121 Fax: 886-2-2876-5215 Fax: 886-2- 2871 2121 E-mail: [email protected] E-mail: [email protected] Journal Subject Terms: Epidemiology; Atrial Fibrillation; Arrhythmias; Electrophysiology; Risk Factors

Szu-Yuan Li, MD, PhD ; Yung-Tai Chen, MD

1School of Medicine, National Yang-Ming University, Taipei, Taiwan; 2Dept of Medicine, Taipei Veterans General Hospital, Yuanshan Branch, Yilan, Taiwan; 3Deran Clinic, Yilan,

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2

Abstract

Background—Whether oral anticoagulant (OAC) use should be considered in patients

undergoing hemodialysis with atrial fibrillation (AF) remains controversial due to uncertainty

regarding risk-benefit assessments. The purpose of this study was to investigate the risk of

ischemic stroke in patients undergoing hemodialysis with new-onset AF, compared with those

without arrhythmia.

Methods and Results—This nationwide, population-based, propensity score-matched cohort

study used data from Taiwan’s National Health Insurance Research Database during 1998–2011

for patients on hemodialysis with new-onset non-valvular AF and matched subjects without

arrhythmia. The clinical endpoints were ischemic stroke (fatal or non-fatal), all-cause death, and

other serious adverse cardiovascular events. Compared with the matched cohort, patients with

AF (n = 6,772) had higher risks of ischemic stroke (adjusted hazard ratio [aHR] 1.27, 95%

confidence interval [CI] 1.13–1.43), all-cause death (aHR 1.59, 95% CI 1.52–1.67), in-hospital

cardiovascular death (aHR 1.83, 95% CI 1.71–1.94), myocardial infarction (aHR 1.33, 95% CI

1.17–1.51), and hospitalization for heart failure (aHR 1.90, 95% CI 1.76–2.05). After considering

in-hospital death as a competing risk, AF significantly increased the risk of heart failure (HR

1.56, 95% CI 1.45–1.68), but not those of ischemic stroke and myocardial infarction.

Additionally, the predictive value of the CHA2DS2 –VASc score for ischemic stroke was

diminished in the competing risk model.

Conclusions—The risk of stroke was only modestly higher in patients undergoing hemodialysis

with new-onset AF than in those without AF, and it became insignificant when accounting for the

competing risk of in-hospital death.

Key words: epidemiology; lipids; population; risk factor

other serious adverse cardiovascular events. Compared with the matched cohort, papapatititienenntststs wwwititith h h

AF (n = 6,772) had higher risks of ischemic stroke (adjusted hazard ratio [aHR] 1.1 272727, 959595% % %

confidence interval [CI] 1.13–1.43), all-cause death (aHR 1.59, 95% CI 1.52–1.67), in-hospital

carddioioiovavavascscscululularaa dddeaeaeath (aHR 1.83, 95% CI 1.71–1.1..949494),)) myocardial infarctctctioioion (aHR 1.33, 95% CI

1...1777–1.51), ananand d hohohospppitititalalalizizizatatation n n fofoforrr heheeararart tt faaailililururure (aaHHHR 1.9.9.90,0,0, 9995%%% CCCIII 1.1.1 767676–2–– .05)5)5)... AfAfAftett r cococonsnsn idididererering

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Introduction

Atrial fibrillation (AF) is an increasing prevalent heart rhythm disorder1-3 for patients with end-

stage renal disease (ESRD) undergoing hemodialysis, and associated with adverse outcomes and

death in these patients.4-6 Given the routine administration of heparin during hemodialysis and

the bleeding tendency associated with ESRD, reports about whether the risk of thromboembolic

stroke is notably higher in patients undergoing dialysis with AF than in those without AF remain

inconsistent.7-9

Current American College of Cardiology/American Heart Association AF guidelines10 do not

provide strong recommendations for patients with non-valvular AF undergoing dialysis; they

suggest only the consideration of warfarin prescription for those with higher stroke risk

(CHA2DS2-VASc 2), based on limited findings from observational studies.11 However, recent

large-scale registry studies12-16 involving patients with AF undergoing dialysis found that

warfarin use was insignificantly or positively associated with ischemic stroke, major bleeding

events, and mortality. These controversial findings have raised concern about the actual clinical

impact of AF-related cardioembolic events in patients undergoing dialysis, especially as the

perceived risk of death is considerably higher than the risk of stroke in these patients. Death is a

potential competing risk for stroke, as many patients on dialysis may die before initial stroke;

this issue has not been fully addressed,8, 17, 18 and previous studies likely overestimated the true

excess stroke risk. In addition, these studies have been limited primarily by small samples, and

prevalent cohort biases (i.e., AF diagnosed before dialysis). Thus, taking advantage of

nationwide data from Taiwan, we evaluated the actual risk of incident ischemic stroke in patients

on dialysis with new-onset AF, compared with a propensity score–matched cohort without

arrhythmia, with consideration of the competing risk of death.

uggest only the consideration of warfarin prescription for those with higher strokekeke rrrisisisk k k

CHA2DS2-VASc 2), based on limited findings from observational studies.11 However, recent

arggge-e-e-scscscalalale e e rereregistttryyy studies12-16 involving patientttss s wiww th AF undergoing dddiaiaialysis found that

wwwarrrfarin use wasasas innssigigignininififificacacantntntlylyly ooor popop siss ttivelylyly aassoociiiateeed d d wiwiwiththth iscscscheeemimimiccc sttrokekeke,, , mamamajojojor rr blblbleeeeeedididingngng

evvvenenentstt , and momm rtttalllity. Thheh see cononontrovovoverrssis aala ffininindddingggs hhhaveveve raiiisesesed d cocoonnncerere n abbouuut ttthe aaactctctuual cllinicccall t

mmmpapapactctct ooofff AFAFAF rr-relelelatatatededed cccararardididioeoeoembmbmbolololicicic eeeveeventntntsss ininin pppaaatititienenentststs undndndererergogogoinininggg dididialalalyssysisisis, esesespepepecicicialalallylly aaasss thththeee



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Methods

Data Source

For this retrospective population-based cohort study, we retrieved all medical records of patients

with ESRD undergoing chronic dialysis from Taiwan’s National Health Insurance Research

Database (NHIRD). Taiwan’s National Health Research Institutes (NHRI) released the NHIRD

for research purposes, with data encrypted to protect privacy. The national health insurance

(NHI) program offers comprehensive medical care coverage to more than 99% of the country’s

population of 23 million people. All information on clinical visits, hospitalization, and prescribed

drugs is included in the NHI system. We have described the NHIRD in detail in previous

works.19, 20 Disease diagnoses for all individuals were classified according to the International

Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). The Institutional

Review Board of Taipei City Hospital exempted this study from full review (TCHIRB-1030407-

W) because the sample comprised de-identi ed secondary data.

Study Cohort

All patients aged 20 years with ESRD undergoing maintenance hemodialysis between 1

January 1998 and 31 December 2011 were identified. ESRD diagnoses were confirmed by ICD-

9-CM code 585 and inclusion in the NHIRD’s catastrophic illness dataset. According to NHI

rules, only patients with ESRD undergoing chronic dialysis can be registered in this dataset.

Based on internal report of Taiwan Society of Nephrology, unfractionated heparin, the most

widely used anticoagulant in Taiwan, was usually administered as a bolus at the start of dialysis

(1000–3000 U) followed by a continuous infusion (500–1000 U per hour) until 15 to 60 minutes

before the end of dialysis.21, 22

The AF cohort comprised all patients with ESRD and incident AF after dialysis. The

works.19, 20 Disease diagnoses for all individuals were classified according to the IIIntntntererernananatititionononalalal

Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). The Institutional

Reviviiewewew BBBoaoaoardrdr of f f TaTT ipei City Hospital exempteddd ttthihihis study from full reviviviewee (TCHIRB-1030407-m

WWW) because thehee sssammmplplple cococompmpmpriririssesed dd dedede-i-i-ideenttiii eeed seecoondadadarryry dddaaata.a

Stttudududy y y Cohooortrtr

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index date was defined as the date of AF diagnosis. To ensure accuracy, the diagnosis of AF was

established based on one inpatient or two outpatient records of ICD-9-CM code 427.31 in the

database, which has been validated.23-25 As the main aim of the present study was to investigate

the risk of ischemic stroke in patients undergoing dialysis with new-onset non-valvular AF, we

excluded patients with diagnoses of AF before dialysis and those with histories of valvular heart

disease. Patients without arrhythmia were included in the control cohort. Index dates for subjects

in the control cohort were randomly assigned and corresponded to those of patients in the AF

cohort, and the same exclusion criteria were applied to both cohorts.

Baseline Characteristics and Score Calculation

For each subject, information on baseline demographic characteristics, including age, gender,

monthly income (NT$ <19,100, 19,100 41,999, and 42,000), urbanization level and dialysis

vintage (i.e. time between onset of ESRD and index date), were extracted. Urbanization levels in

Taiwan are divided into four strata according to the NHRI, with level 1 corresponding to the

most urbanized and level 4 corresponding to the least urbanized areas. The Charlson

Comorbidity Index (CCI) score was used to determine overall systemic health. Each increase in

score indicates a stepwise increase in cumulative mortality.26 Other systemic diseases and risk

factors for cardiovascular disease not included in the CCI were also examined; these included

hypertension, prior major bleeding, alcohol or drug use history, and dyslipidemia. Concomitant

mediation data associated with the outcomes of interest, including data on use of alpha blockers,

beta blockers, calcium channel blockers, diuretics, angiotensin-converting enzyme inhibitors,

angiotensin II receptor blockers, other anti-hypertensive drugs, anti-hyperglycemic drugs,

aspirin, clopidogrel, ticlopidine, dipyridamole, nitrate, statins, proton pump inhibitors, and

nonsteroidal anti-inflammatory drugs, were extracted. The CHA2DS2-VASc score was calculated

For each subject, information on baseline demographic characteristics, including aaagegege, gegegendndndererer,

monthly income (NT$ <19,100, 19,100 41,999, and 42,000), urbanization level and dialysis

vintttagagagee e (((iii.e.e.e. titt meee bbbetween onset of ESRD and indndnde ), were extracteteteddd. Urbanization levels inxxx date

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momoosttt uuurbannizizizedee aaanndn leevveele 4 cccorororrererespspsponoo dddingngng tttooo tthhehe leaeaeast uuurbrbrbanannizzzededed arrereasasa . ThThThe ChChCharaa lsononon

CCComo bbrbidididiitity IIIndddex (C(C(CCICICI))) score was us dded to dddetermiiine over llallll systemiiic hhhealltlthhh. EEEachhh iiincrease iiin



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for each patient by adding 1 point each for age between 65 and 74 years, history of hypertension,

diabetes, congestive heart failure, vascular disease (myocardial infarction or peripheral artery

disease), and female sex; and 2 points each for history of stroke/transient ischemic attack and age

75 years. The CHADS2 score was calculated for each patient by adding 1 point each for history

of congestive heart failure, hypertension, diabetes, and age 75 years; and 2 points for history of

stroke/transient ischemic attack.

Propensity score matching

Given the differences in baseline characteristics and risk of cardiovascular disease between the

AF and control cohorts, we used 1:1 propensity score matching and calculated propensity scores,

which predicted the probability of AF occurrence conditional on baseline covariates, by

multivariable logistic regression (Supplementary Table 1). We attempted to match each patient in

the AF cohort with a patient in the control cohort with a similar propensity score, based on

nearest-neighbor matching without replacement, using a caliper width equal to 0.1 of the

standard deviation of the logit of the propensity score.

Outcomes

The primary outcome was hospitalization or death with a principal diagnosis of ischemic stroke

(ICD-9-CM code 433.x, 434.x, or 436). The secondary outcomes were all-cause death, in-

hospital cardiovascular death, and hospitalization or death for hemorrhagic stroke (ICD-9-CM

code 431 or 432), myocardial infarction (ICD-9-CM code 410.x), or heart failure (ICD-9-CM

code 428.x). The accuracy of stroke and myocardial infarction diagnoses recorded in the

database has been validated with a positive predictive value of 88.4 to 94% and 92%

respectively, and a negative predicted values of these diagnoses have been reported to be over

90%.27-29 All subjects were followed until death or 31 December 2012.

which predicted the probability of AF occurrence conditional on baseline covariatatateseses, bybyby

multivariable logistic regression (Supplementary Table 1). We attempted to match each patient ina

he AFAFAF cccohohohororort t wiiiththth a patient in the control cohorrrtt t wiwiw th a similar propennnsisisitytt score, based on

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Statistical Analysis

Descriptive statistics were used to characterize baseline demographic and clinical variables of the

study cohort. Standardized differences were used to check for balance between the AF and

control cohorts after matching. Propensity scores for the likelihood of AF occurrence were

determined by multivariate logistic regression analysis, conditional on baseline covariates

(Supplementary Table 1). The incidence rates of outcomes of interest in the two groups were

calculated using Poisson distribution. Adjusted hazard ratios (aHRs) and 95% confidence

intervals (CIs) were calculated for the outcomes of interest in each group. Warfarin use was

calculated as a time-varying covariate. Due to the high mortality rate in patients with ESRD,

competing-risk regression using Fine and Gray’s model was also performed.30 In-hospital death

that is not part of the outcome of interest was the competing risk. Given lack of data from

autopsies to characterize the exact cause of out-of-hospital death, out-of-hospital death was not

regarded as the competing risk for respective mortality outcomes of interest to ensure accuracy.

To validate the robustness of our results, we performed sensitivity analyses including Cox

regression models adjusted for propensity scores in quintile before propensity score matching

and different stroke risk stratification by CHA2DS2-VASc score. The SQL Server 2012

(Microsoft Corporation, Redmond, WA, USA) was used for data linkage, processing, and

sampling. Propensity scores were calculated using SAS version 9.3 (SAS Institute, Cary, NC,

USA). All other statistical analyses were conducted with STATA statistical software (version

12.0; StataCorp, College Station, TX, USA). Statistical significance was defined as two-sided P

< 0.05.

competing-risk regression using Fine and Gray’s model was also performed.30 In---hohohospspspitititalalal dddeaeaeathtt

hat is not part of the outcome of interest was the competing risk. Given lack of data from G

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8

Results

Characteristics of the Study Population

For the study period of January 1998 to December 2011, a total of 6,772 patients with ESRD and

AF and 70,625 control subjects met the inclusion criteria (Supplementary Figure 1). The mean

age of the AF cohort was 68.8 ± 11.3 years, and the majority (53.2%) of patients were female.

The median timing of AF occurrence was 38 (quartiles 13–76) months after ESRD. The mean

CHA2DS2-VASc score was 4.8 ± 2.2. A total of 566 patients (8.4%) used warfarin during the

follow-up period. After propensity score matching, we identified 6,494 patients with AF and

6,494 matched controls with similar baseline characteristics. Detailed demographic information

for these cohorts is provided in Table 1.

Long-Term Risks of Ischemic Stroke and Adverse Cardiovascular Events

During the follow-up period (mean [standard deviation] years: 3.2 [2.7]; median [quartiles] years:

2.3 [1.3–4.4]), the AF cohort had higher risks of ischemic stroke (aHR 1.27, 95% CI 1.13–1.43),

all-cause death (aHR 1.59, 95% CI 1.52–1.67), in-hospital cardiovascular death (aHR 1.83, 95%

CI 1.71–1.94), myocardial infarction (aHR 1.33, 95% CI 1.17–1.51), and heart failure (aHR 1.90,

95% CI 1.76–2.05) compared with the matched non-AF cohort (Table 2). However, after adjusting

for in-hospital death from causes other than outcomes of interest as a competing risk, AF was

associated significantly only with in-hospital cardiovascular death (HR 1.65, 95% CI 1.55–1.76)

and heart failure (HR 1.56, 95% CI 1.45–1.68), but not those of ischemic stroke and myocardial

infarction. Sensitivity analyses conducted before propensity score matching (Table 2) and

stratified by different stroke risk according to CHA2DS2-VASc score (Supplementary Table 2)

yielded consistent results.

Association between CHA2DS2-VASc Score and Risks of Ischemic Stroke and Death in

for these cohorts is provided in Table 1.

Long-Term Risks of Ischemic Stroke and Adverse Cardiovascular Events

Duririingngng ttthehehe fffoloo low-w-w-up period (mean [standard deeviviviatatation] years: 3.2 [2.7]7]]; memm dian [quartiles] years

222.3 [1.3–4.4])), thththe AFAFF cohohohororort t t hahahaddd hihihighghgherere riskskk of iscscchemimimiccc stststrororokekek (aHaHaHR RR 1.27,, 959595%%% CICICI 11.1.1.13–––11.1 434343),

allll-l-l-cacacause deeatatath h (aa(aHHRH 111.5559, 9995%5%5% CI I I 111.5552––1– .6667))), , in-hhhospipipitttal cacacardrddiooovvavascscsculllarr ddeeeatthth (aHHHRRR 1.83, 95%%

CICICI 111 77.7111–111.949494))), mmmyooyocacacardrdrdiaiaialll inininfafafarcrcrctititiononon (((aHaHaHRRR 111.333333, 959595%%% CICICI 111 11.1777–111.515151))), aaandndnd hhheaeaearrrttt fafafailililurrureee (a(a(aHRHRHR 111 99.90000



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9

Patients on Hemodialysis with new-onset AF

Of 6,772 patients with AF, 600 patients experienced ischemic stroke (3.35 per 100 person-years)

and 4,642 patients died (24.7 per 100 person-years) during the follow-up period. The annual risks

of ischemic stroke and all-cause death were 1.82% and 10.03%, respectively, for patients with a

CHA2DS2-VASc score of 0 and 8.66 % and 63.10 %, respectively, for those with a CHA2DS2-

VASc score of 9 (Table 3). Annual stroke and death rates according to CHADS2 score were also

shown in Table 3. In the Cox model, warfarin use was not associated with reducing stroke (aHR

1.24, 95% CI 0.91–1.69). CHA2DS2-VASc score showed good predictive ability for ischemic

stroke (Harrell's c statistic = 0.622). CHA2DS2-VASc scores 3 increased the risk of ischemic

stroke relative to scores of 0, although the difference was significant only for CHA2DS2-VASc

scores 5 (aHR 1.90, 95% CI 1.02–3.55 for score 5; aHR 2.11, 95% CI 1.13–3.95 for score 6;

aHR 2.39, 95% CI 1.26–4.54 for score 7; aHR 3.37, 95% CI 1.75–6.48 for score 8; aHR 3.87, 95%

CI 1.93–7.78 for score 9). However, when in-hospital death was treated as a competing risk in the

Cox model, higher CHA2DS2-VASc score was not associated with an increased risk of ischemic

stroke (Table 4).

Discussion

In this analysis of a large “real-world” contemporary nationwide cohort, we found that patients

undergoing hemodialysis with new-onset AF have a high incidence of ischemic stroke,

cardiovascular and all-cause death, and other serious adverse cardiovascular events (myocardial

infarction and heart failure). However, compared with the matched cohort without arrhythmia,

patients with AF had only a modestly (1.3-fold) higher risk of ischemic stroke, which became

insignificant after accounting for the competing risk of in-hospital death. The predictive value of

troke relative to scores of 0, although the difference was significant only for CHAHAA222DSDSDS222-V-V-VASASASccc

cores 5 (aHR 1.90, 95% CI 1.02–3.55 for score 5; aHR 2.11, 95% CI 1.13–3.95 for score 6;

aHR R R 2.2.2.393939, , , 959595% CICICI 1.26–4.54 for score 7; aHR 3.3.3.373737, 95% CI 1.75–6.488 fffooor score 8; aHR 3.87, 95

CCCI 111.93–7.78 fororor scooorerere 999).).) HHHowowowevevvererr,,, wwhw een iiinn-n hhoh sppitttal dededeatatath hh wawawas trrreaeaeateteteddd aas a cococompmpmpetetetininingg g iriisksksk iiin nn thtt

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the CHA2DS2 –VASc score for ischemic stroke was also diminished in analyses considering the

competing risk of in-hospital death.

The incidence of ischemic stroke in patients on dialysis with new-onset AF was lower in the

present study (3.28/100 person-years) than in a Danish study17(5.61/100 person-years) of 901

patients undergoing dialysis with AF, including outcomes of mild stroke or transient ischemic

attack, and in a population-based study2 (9.9/100 person-years) using United States Renal Data

System (USRDS) data from 1995–2007, including patients of older age (mean age 75.9 years).

Overall, stroke rates according to CHADS2 score for patients with AF in our study were

comparable with the findings from Dialysis Outcomes and Practice Patterns Study15,but not as

high as that observed in non-dialysis general population with AF31, 32, which implicitly assumed

that AF had less impact on stroke risk in dialysis population as it did in general population. Most

notably, the USRDS data2 indicated that more than 60% of patients on dialysis with incident AF

died before the occurrence of stroke. In Danish national registries of patients on dialysis who

were discharged with a diagnosis of AF, the death rate (29.35/100 person-years) was more than

five-fold higher than the stroke rate during the follow-up period.17 These findings are similar to

those of the present study, suggesting that the risk of death overwhelms the risk of AF-related

stroke in patients undergoing dialysis. Also, Murray et al.7 reported that AF appeared not to

aggravate the risk of incident stroke in US Medicare-insured patients on dialysis. Overall, the

discrepancy that exists between competing and non-competing risk analyses in our study could

be attributed to high mortality rate in patients on dialysis with incident AF and majority of them

who died before a stroke. In addition to the competing risks between death and stroke, another

proposed explanation may be reduced risk of thrombus formation or embolization, and even

subsequent stroke due to systemic heparinization during dialysis sessions.

high as that observed in non-dialysis general population with AF31, 32, which implllicicicitititlylyly aaassssssumumumedee

hat AF had less impact on stroke risk in dialysis population as it did in general population. Most

notaaablblbly,y,y, ttthehehe UUUSRRRDSDD data2 indicated that more thahahannn 606 % of patients on dididialaa ysis with incident AF

ddidieddd before thhe e ooco ccuurrrrrrennncecece ooof f f stststrorookekeke. InInIn DDannnissshh naatiionaaalll rrregigigissstririiesee ooofff papapatieentsss ooonnn dididialalalysysysisisis wwwhohoho

weweereee discharararggeg d wwiw th aa diagngnnosososisi ooof f f AFFF, the e e deeeathh raaate e (2229.33535/1/1100000 pppererersooonn-yeaeaearsrsrs) ) waaas s s mooreee thaaann

fififiveeve ff-folololddd hihihighghghererer ttthahahannn thththeee stststrororokekeke rrratatateee dudduririringngng ttthehehe fffololollololow-uppup ppperererioioioddd.1717 TTThehehesesese fffininindididingngngsss ararareee sisisimimimilalalarrr tototo



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By contrast, other studies8, 9, 33 showed that AF increased the risk of ischemic stroke 3.1–

9.8-fold in patients undergoing dialysis, resulting in a higher stroke risk than observed in our

study. However, the results of these studies must be interpreted cautiously due to selection bias

(single centers), small samples, uncontrolled baseline risk of stroke, and lack of adjustment for

the competing risks of stroke and death. Although correspondence between a higher CHA2DS2 –

VASc score and a higher risk of stroke in patients with ESRD was observed in the present cohort

and by Chao et al.,34 no such relationship was found after accounting for the competing risk of

death, which further strengthens our results. Thus, our findings better reflect the real estimated

risk of ischemic stroke in a nationwide, unselected dialysis population. We demonstrated that

new-onset AF did not amplify stroke risk in this population to a greater extent than previously

expected. Our findings may indirectly explain the unsatisfactory outcome of warfarin treatment

in patients on dialysis with AF in most recent studies.12-16 However, we need to acknowledge that

the efficacy of warfarin treatment for stroke prevention in dialysis patients with AF is beyond the

scope of the present study due to unavailable information about warfarin use in our claims

database, such as adherence to prescribed warfarin and therapeutic values of international

normalized ratio that may confound the estimation of stroke risk.16 Thus, further prospective

randomized trials are warranted to clarify this issue.

Some limitations of our study should be addressed. First, because of its observational

nature, bias due to unmeasured confounding could not be completely ruled out, although we used

propensity score–matched analysis to balance major baseline comorbidities associated with

stroke occurrence between cohorts. Second, the precision of the disease diagnoses, which was

based on ICD-9-CM code from administrative data reported by physicians, may be a concern

although diagnostic accuracy of main events of interest such as atrial fibrillation23-25, stroke27, 28

new-onset AF did not amplify stroke risk in this population to a greater extent thaaannn prprprevevevioioiousususlylyly

expected. Our findings may indirectly explain the unsatisfactory outcome of warfarin treatment

n pppatatatieieientntnts s s ononon diaaalylylysis with AF in most recent stuuudididieees.12-16 However, weee nnneed to acknowledge tha

hhhe efficacy of wwwarrfararariinin tttrerereaatatmemementntnt fffooor ssstrr kokke ee prrreveentttionnn iiinnn dididialaa ysysisi pppatatatieieientts wiwiwiththth AAAF F F isii bbbeyeyeyononond dd thththe

cccopopopeee of theee pppresses nntn stttuddyd duuue totot uuunanan vavaailllablelele iiinformmmatititioonon aaaboboboutuu wawawarfrffara iiin useee iiinnn ourrr ccclaimsmm

dadadatatatabababasesese, sussuchchch aaasss adadadheheherererencncnceee tototo ppprererescscscririribebebeddd waawarfrfrfarararininin aaandndnd tttheheherararapepepeuttuticicic valalalueeuesss ofofof iiintntntererernananatititionononalalal ffff



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and myocardial infarction29 and other important covariates such as diabetes35 or hypertension27,

has been validated. Third, the cause of out-of-hospital death may be variable and uncertain

without autopsy confirmation, whereas it is very unlikely to perform autopsies on patients with

ESRD because they are more likely to die of cardiovascular disease. According to one Japanese

study of 93 autopsied chronic dialysis patients, there were only 5.7% of out-of-hospital deaths

due to ischemic stroke.36 Thus, this confounding may less bias the risk estimate of ischemic

stroke than expected. Besides, while all out-of-hospital deaths were excluded from competing

risk mortality outcome for ischemic stroke as our main analysis, the potential misclassification

(i.e. non-ischemic stroke related out-of-hospital deaths) would lead the estimated HR for

ischemic stroke away from the null in the competing-risk analysis and further strengthen our

main findings. Fourth, we could not include some patient data (including body weight, smoking,

alcohol consumption, and physical activity) or heparin use during hemodialysis in the adjusted

analysis because the NHIRD does not contain these data. Fifth, underestimation of true incidence

of AF was possible in our national registry cohort in which ambulatory arrhythmia monitoring

(24-hour Holter monitoring or cardiac event recorders) was not routinely performed for every

patient. However, our estimates of the incidence of new-onset AF are similar to that of previous

studies2, 15 and, therefore, support that the underestimation may be minimal. Besides, given that

the true incidence of AF and subsequent stroke risk would be less biased on low-risk patients

than high-risk patients, we further stratified the cohort into low-risk (0-1) and high-risk ( 2)

groups by CHA2DS2-VASc score. The result remained consistent in the low-risk group in

addition to the high-risk group. Thus, we believed that this underestimation was less likely to

influence the robustness of our results. Additionally, we could not specify AF type (paroxysmal

or non-paroxysmal), which was also not recorded in the database, but the risks of stroke and

schemic stroke away from the null in the competing-risk analysis and rrr further streeengngngthththenenen oooururur

we could not include somain findings. Fourth, me patient data (including body weight, smoking,

alcoohohohol l l cococonsnsnsumuu ptptptioioion, and physical activity) or hhhepepepaaarin use during hemodododialysis in the adjusted

anananalalalysis becausesese thhee NNNHIHIHIRDRDRD dddooeoesss nnonottt coonttaiaiainnn theseee daaatatata. FiFiFiftftfth, uuundndnderereresesestiimatititiononon ooof f f trtrtrueuee incncncidididenenence

offf AAAF FF was popoposssibbbleee in ouuur naaatitt onononal rrreeegiiisttrtry y cccohhhort innn whwhwhiiich h ammmbububulaaatoooryry arrrhyhyythhhmia a a mmmonnitoooringngg

222444-hohohourrur HHHolololteteterrr momomonininitototoririringngng ooorrr cacacardrdrdiaiaiaccc eveevenenenttt rererecococordrdrdererers)s)s) wasasas nnnototot rrrouooutititinenenelylly ppperererfofoformrmrmededed fffororor eeeveeveryrry



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survival do not differ between patients with paroxysmal and non-paroxysmal AF.9, 37, 38 Last,

whether the underlying cause of ischemic stroke is AF-related thromboembolism or

atherosclerosis and thrombosis of the cerebral artery is not available, whereas neither other

prospective clinical trials had this information.39, 40

Conclusions

The risk of stroke was only modestly higher in this contemporary population of hemodialysis

patients with new-onset AF than in those without AF, and the risk became insignificant after

adjustment for the competing risk of in-hospital death. Thus, the net clinical benefit of stroke

prevention for patients on dialysis with AF needs to be re-evaluated, especially considering the

impact of the high mortality burden. Of note, our results emphasize the need to re-prioritize the

list of AF-related comorbidity risk estimates, especially with targeting of the outcome of death.

Acknowledgments: This study was based in part on data from the NHIRD provided by Bureau

of National Health Insurance (BNHI) of the Department of Health and managed by the NHRI.

The conclusions presented in this study are those of the authors and do not necessarily reflect the

views of the BNHI, the Department of Health, or the National Health Research Institute.

Conflict of Interest Disclosures: None.

References:

1. Winkelmayer WC, Patrick AR, Liu J, Brookhart MA, Setoguchi S. The increasing prevalence of atrial fibrillation among hemodialysis patients. J Am Soc Nephrol. 2011;22:349-57. 2. Goldstein BA, Arce CM, Hlatky MA, Turakhia M, Setoguchi S, Winkelmayer WC. Trends in the incidence of atrial fibrillation in older patients initiating dialysis in the United States. Circulation. 2012;126:2293-301. 3. Liao JN, Chao TF, Liu CJ, Wang KL, Chen SJ, Lin YJ, Chang SL, Lo LW, Hu YF, Tuan TC,

prevention for patients on dialysis with AF needs to be re-evaluated, especially conononsisisidededeririringngng ttthehehe

mpact of the high mortality burden. Of note, our results emphasize the need to re-prioritize the

ist ooofff AFAFAF-r-r-relelelataa eddd cccomorbidity risk estimates, espppecececiiially with targeting ofofof the outcome of death.

AcAcAcknknknowledgdgdgmmementntnts: ThThThis stuuudydydy wasasas baaaseede iiinnn ppapart onn dddattta fffrrrommm ththheee NHNN IRIIRD prprprooovidededed byy y BBBureauu a

of National Health Insurance (((BNHI))) of the Depapp rtment of Health and managgged by yy the NHRI.



ownloaded from



14

Chung FP, Chen TJ, Chen SA. Incidence and risk factors for new-onset atrial fibrillation among patients with end-stage renal disease undergoing renal replacement therapy. Kidney Int. 2015;87:1209-15. 4. Genovesi S, Vincenti A, Rossi E, Pogliani D, Acquistapace I, Stella A, Valsecchi MG. Atrial fibrillation and morbidity and mortality in a cohort of long-term hemodialysis patients. Am J Kidney Dis. 2008;51:255-62. 5. Vázquez E, Sánchez-Perales C, Borrego F, Garcia-Cortés MJ, Lozano C, Guzmán M, Gil J-M, Borrego MJ, Pérez V. Influence of atrial fibrillation on the morbido-mortality of patients on hemodialysis. Am Heart J. 2000;140:886-890. 6. Zimmerman D, Sood MM, Rigatto C, Holden RM, Hiremath S, Clase CM. Systematic review and meta-analysis of incidence, prevalence and outcomes of atrial fibrillation in patients on dialysis. Nephrol Dial Transplant. 2012;27:3816-22. 7. Murray AM, Seliger S, Lakshminarayan K, Herzog CA, Solid CA. Incidence of stroke before and after dialysis initiation in older patients. J Am Soc Nephrol. 2013;24:1166-73. 8. Vazquez E, Sanchez-Perales C, Garcia-Garcia F, Castellano P, Garcia-Cortes MJ, Liebana A, Lozano C. Atrial fibrillation in incident dialysis patients. Kidney Int. 2009;76:324-30. 9. Chou CY, Kuo HL, Wang SM, Liu JH, Lin HH, Liu YL, Huang CC. Outcome of atrial fibrillation among patients with end-stage renal disease. Nephrol Dial Transplant. 2010;25:1225-30. 10. January CT, Wann LS, Alpert JS, Calkins H, Cigarroa JE, Cleveland JC, Jr., Conti JB, Ellinor PT, Ezekowitz MD, Field ME, Murray KT, Sacco RL, Stevenson WG, Tchou PJ, Tracy CM, Yancy CW, Members AATF. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society. Circulation. 2014;130:2071-104. 11. Winkelmayer WC, Liu J, Setoguchi S, Choudhry NK. Effectiveness and safety of warfarin initiation in older hemodialysis patients with incident atrial fibrillation. Clin J Am Soc Nephrol. 2011;6:2662-8. 12. Shah M, Avgil Tsadok M, Jackevicius CA, Essebag V, Eisenberg MJ, Rahme E, Humphries KH, Tu JV, Behlouli H, Guo H, Pilote L. Warfarin use and the risk for stroke and bleeding in patients with atrial fibrillation undergoing dialysis. Circulation. 2014;129:1196-203. 13. Chen JJ, Lin LY, Yang YH, Hwang JJ, Chen PC, Lin JL. Anti-platelet or anti-coagulant agent for the prevention of ischemic stroke in patients with end-stage renal disease and atrial fibrillation--a nation-wide database analyses. Int J Cardiol. 2014;177:1008-11. 14. Sood MM, Larkina M, Thumma JR, Tentori F, Gillespie BW, Fukuhara S, Mendelssohn DC,

and after dialysis initiation in older patients. J Am Soc Nephrol. 2013;24:1166 73.

8. Vazquez E, Sanchez-Perales C, Garcia-Garcia F, Castellano P, Garcia-Cortes MMMJ,J,J, LLLieieiebababananana AAA,,,Lozano C. Atrial fibrillation in incident dialysis patients. n Kidney Int. 2009;76:324-30.

9. CChohohou u u CYCYCY,,, KuKK ooo HLHH , Wang SM, Liu JH, Lin HH,H,H, LLLiu YL, Huang CC. OOOutcome of atrial fibrbrbrililillal tion aaammmongg patients with end-stage renal diiiseeease. Nephrol l l DiDD al Transplant. 2010;25:1225-3330.

1000. . JaJaJanuary y y CTCC , WaWW nnnn LLLS, AAAlplplpeere t JSJSJS, CaCaCalkll innns HHH, CCigggarrrroooa JJJE,E,E, CCCleleevvvelalalanddd JJC, Jrr.r.,, Cooontntnti JBB,,, ElliinnorPT, EzEzEzekekekowowowitz MDMDMD, FiFFielleld dd MEEE, MuMuMurray KKKTTT, SSSaaaccocco RRRL,LL SSSteteteveveennsnson WWWGGG, TTTchhchoou PPPJJJ, TTTrar cyyy CCMMM, YaYaYancncncy CWCWCW, MeMeMembmbmbererersss AAAAAATFTFTF. 202020141414 AAAHAHAHA/A/A/ACCCCCC/H/H/HRSRSRS ggguiiuidededelililinenene fffororor ttthehehe mmmanananagagagemememenenenttt ofofof pppatatatieieientntntsss



ownloaded from



15

Chan K, de Sequera P, Komenda P, Rigatto C, Robinson BM. Major bleeding events and risk stratification of antithrombotic agents in hemodialysis: results from the DOPPS. Kidney Int. 2013;84:600-8. 15. Wizemann V, Tong L, Satayathum S, Disney A, Akiba T, Fissell RB, Kerr PG, Young EW, Robinson BM. Atrial fibrillation in hemodialysis patients: clinical features and associations with anticoagulant therapy. Kidney Int. 2010;77:1098-106. 16. Chan KE, Lazarus JM, Thadhani R, Hakim RM. Warfarin use associates with increased risk for stroke in hemodialysis patients with atrial fibrillation. J Am Soc Nephrol. 2009;20:2223-33. 17. Olesen JB, Lip GY, Kamper AL, Hommel K, Kober L, Lane DA, Lindhardsen J, Gislason GH, Torp-Pedersen C. Stroke and bleeding in atrial fibrillation with chronic kidney disease. NEngl J Med. 2012;367:625-35. 18. Reinecke H, Brand E, Mesters R, Schabitz WR, Fisher M, Pavenstadt H, Breithardt G. Dilemmas in the management of atrial fibrillation in chronic kidney disease. J Am Soc Nephrol. 2009;20:705-11. 19. Shih CJ, Chu H, Chao PW, Lee YJ, Kuo SC, Li SY, Tarng DC, Yang CY, Yang WC, Ou SM, Chen YT. Long-term clinical outcome of major adverse cardiac events in survivors of infective endocarditis: a nationwide population-based study. Circulation. 2014;130:1684-91. 20. Chao PW, Shih CJ, Lee YJ, Tseng CM, Kuo SC, Shih YN, Chou KT, Tarng DC, Li SY, Ou SM, Chen YT. Association of postdischarge rehabilitation with mortality in intensive care unit survivors of sepsis. Am J Respir Crit Care Med. 2014;190:1003-11. 21. Fischer KG. Essentials of anticoagulation in hemodialysis. Hemodial Int. 2007;11:178-89. 22. Davenport A. What are the anticoagulation options for intermittent hemodialysis? Nat Rev Nephrol. 2011;7:499-508. 23. Chang CH, Lee YC, Tsai CT, Chang SN, Chung YH, Lin MS, Lin JW, Lai MS. Continuation of statin therapy and a decreased risk of atrial fibrillation/flutter in patients with and without chronic kidney disease. Atherosclerosis. 2014;232:224-30. 24. Lin LJ, Cheng MH, Lee CH, Wung DC, Cheng CL, Kao Yang YH. Compliance with antithrombotic prescribing guidelines for patients with atrial fibrillation--a nationwide descriptive study in Taiwan. Clin Ther. 2008;30:1726-36. 25. Tsai WC, Chen CY, Kuo HF, Wu MT, Tang WH, Chu CS, Lin TH, Su HM, Hsu PC, Jhuo SJ, Lin MY, Lee KT, Sheu SH, Lai WT. Areca nut chewing and risk of atrial fibrillation in Taiwanese men: a nationwide ecological study. Int J Med Sci. 2013;10:804-11. 26. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373-83.

2009;20:705 11.

19. Shih CJ, Chu H, Chao PW, Lee YJ, Kuo SC, Li SY, Tarng DC, Yang CY, Yang gg WCWCWC,,, OuOuOu SSSM,M,M, Chen YT. Long-term clinical outcome of major adverse cardiac events in survivors of infective endocarditis: a nationwide population-based study. Circulation. 2014;130:1684-91.

2000. CCChao PW,W,W, Shih CJ, Lee YJ, Tseng CM, Kuo SCSCSC, Shih YN, Chohohou KT, Tarng DC, Li SY, Ou SMSMSM, Chen YT. AAAsssoccciaiaiatiitiononon oof f f ppopostststdididisccchahhargee reeehabbbiliitatititiononon wwwititith h momomortrtrtalalalititity in iiintntntenenensisisiveveve cccaara eee unununititit auuurvvvivors of sepsssiss. Ammm J Reesespppir Crit CCCaare MeMeMed. 220114;111909090:1:1:100003---1111.

21. FiFiFiscscscheheherr KGGG. EEsEssesesentntiaialslsl of ananantititicccoagulululatatatioioion nn iinin hhhememodododiaiaialylylysisisisss. HeHeemmomodididialal IIIntnn . 202020070707;;11:17117888-898989.



ownloaded from



16

27. Cheng CL, Kao YH, Lin SJ, Lee CH, Lai ML. Validation of the National Health Insurance Research Database with ischemic stroke cases in Taiwan. Pharmacoepidemiol Drug Saf. 2011;20:236-42. 28. Hsieh CY, Chen CH, Li CY, Lai ML. Validating the diagnosis of acute ischemic stroke in a National Health Insurance claims database. J Formos Med Assoc. 2015;114:254-9. 29. Cheng C-L, Lee C-H, Chen P-S, Li Y-H, Lin S-J and Yang Y-HK. Validation of Acute Myocardial Infarction Cases in the National Health Insurance Research Database in Taiwan. JEpidemiol. 2014;24:500-507. 30. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. JAm Stat Assoc. 1999;94:496–509. 31. Gage BF, Waterman AD, Shannon W, Boechler M, Rich MW, Radford MJ. Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation. JAMA. 2001;285:2864-70. 32. Lip GY, Nieuwlaat R, Pisters R, Lane DA, Crijns HJ. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest. 2010;137:263-72. 33. Quinn RR, Naimark DM, Oliver MJ, Bayoumi AM. Should hemodialysis patients with atrial fibrillation undergo systemic anticoagulation? A cost-utility analysis. Am J Kidney Dis. 2007;50:421-32. 34. Chao TF, Liu CJ, Wang KL, Lin YJ, Chang SL, Lo LW, Hu YF, Tuan TC, Chung FP, Liao JN, Chen TJ, Lip GY, Chen SA. Incidence and prediction of ischemic stroke among atrial fibrillation patients with end-stage renal disease requiring dialysis. Heart Rhythm. 2014;11:1752-9. 35. Lin CC, Lai MS, Syu CY, Chang SC, Tseng FY. Accuracy of diabetes diagnosis in health insurance claims data in Taiwan. J Formos Med Assoc. 2005;104:157-63. 36. Takeda K, Harada A, Okuda S, Fujimi S, Oh Y, Hattori F, Motomura K, Hirakata H, Fujishima M. Sudden death in chronic dialysis patients. Nephrol Dial Transplant. 1997;12:952-5. 37. Hart RG, Pearce LA, Rothbart RM, McAnulty JH, Asinger RW, Halperin JL. Stroke with intermittent atrial fibrillation: incidence and predictors during aspirin therapy. Stroke Prevention in Atrial Fibrillation Investigators. J Am Coll Cardiol. 2000;35:183-7. 38. Hohnloser SH, Pajitnev D, Pogue J, Healey JS, Pfeffer MA, Yusuf S, Connolly SJ, Investigators AW. Incidence of stroke in paroxysmal versus sustained atrial fibrillation in patients taking oral anticoagulation or combined antiplatelet therapy: an ACTIVE W Substudy. J Am Coll Cardiol. 2007;50:2156-61.

Fibrillation. JAMA. 2001;285:2864 70.

32. Lip GY, Nieuwlaat R, Pisters R, Lane DA, Crijns HJ. Refining clinical risk strrratatatififificicicatatatioioion n n fofofor r r predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest. 2010;137:263-72.

3333. QQQuinn RRRRRR, Naimark DM, Oliver MJ, Bayoummi AAAM. Should hhhemee odialysis patients with atrial fififibrrriillation undnddererergo sssyysy tetetemimimicc ananantititicococoagaga uluu aatioion?n?n? A ccossst-utututilililititityyy aaanalallysyssiisis. AmAmAm J KKKidididneneneyyy DiDD sss. 2022 0070 ;50:421-32.

34. ChChChaoaoao TTTFFF, LLiuiuiu CCCJ,JJ WWWananng KLKLKL, LiLiLin YJJJ, ChChChananangg SLSLSL, LoLLo LLLWWW, HHHu YFYFYF, TuTuTuanan TTTC, CCChuhuhunng FFFP,PP LLLiaiaiaoo JNNJNChChChenenen TTTJJJ, LLLipipip GGGYYY, CCChehehennn SASASA. InInIncicicidededencncnceee anananddd prprpredededicicictititiononon ooofff isisischchchemememicicic ssstrtrtrokokokeee amamamonononggg atatatriririalalal fffibibibriririllllllatatatioioionnnn



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39. Patel MR, Mahaffey KW, Garg J, Pan G, Singer DE, Hacke W, Breithardt G, Halperin JL, Hankey GJ, Piccini JP, Becker RC, Nessel CC, Paolini JF, Berkowitz SD, Fox KA, Califf RM, Investigators RA. Rivaroxaban versus warfarin in nonvalvular atrial fibrillation. N Engl J Med. 2011;365:883-91. 40. Giugliano RP, Ruff CT, Braunwald E, Murphy SA, Wiviott SD, Halperin JL, Waldo AL, Ezekowitz MD, Weitz JI, Spinar J, Ruzyllo W, Ruda M, Koretsune Y, Betcher J, Shi M, Grip LT, Patel SP, Patel I, Hanyok JJ, Mercuri M, Antman EM, Investigators EA-T. Edoxaban versus warfarin in patients with atrial fibrillation. N Engl J Med. 2013;369:2093-104.



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Clinical Perspective

There is no strong evidence regarding the use of oral anticoagulants for stroke prevention in

patients undergoing hemodialysis with atrial fibrillation (AF). A better risk estimate of AF-

related stroke in this population was required to set the realistic treatment goals while majority of

hemodialysis patients with AF would die before a stroke. Previous small studies may

overestimate the stroke risk from the Kaplan-Meier approach because of potential of competing

risk bias, especially in frail populations. The present nationwide population-based propensity

score–matched study analysed the risk of ischemic stroke in 6,772 patients undergoing

hemodialysis with new-onset AF during 1998–2011, compared with those without arrhythmia in

the Cox model and the Fine and Gray competing-risk model, respectively. In the Cox model, we

found that AF patients was associated with increased risk of death or poor cardiovascular

outcomes. However, in the competing-risk model, the risk of ischemic stroke became

insignificant. The similar findings were observed in outcomes of hemorrhagic stroke and

myocardial infarction with the exception of in-hospital cardiovascular death and heart failure that

remained significant when taking into account the competing risk of in-hospital death. Thus, this

study suggests that the association of new-onset AF with subsequent ischemic stroke was weaker

than as previously expected in patients on chronic hemodialysis. Our results have implications

for reconsideration the need of oral anticoagulants for stroke prevention and choice of primary

outcomes in trials on hemodialysis patients with AF.

during 1998–2011hemodialysis with new-onset AF , compared with those withouuuttt arararrhrhrhytytythmhmhmiaiaia in

he Cox model and the Fine and Gray competing-ri sk model, respectively. In the Cox model, we

founnnddd thththatatat AAAFFF paaatititiene ts was associated with in r creaeaeasesesed risk of death or poooooor cardiovascular

oooutcccomes. Howeweweveer, iiin n thththeee cocoompmpm etetetinining-g-g-riiskkk mmmoodo el, ttthe riririsksksk ooof f f isschchchemememicicc strrokeee bebebecacacammme

nnnsiss gngngnificannt.t.t. Theh ssimmiiilaara finnndididingngngs weww rerere ooobsseeer d vvved innn ouuutcccommemess ofofof hhhememmorrrrhhagggicicic strokkkeee annd

mymmyocococararardididialalal iiinfnfnfarararctctctioioionnn wiiwiththth ttthehehe eeexccxcepepeptititiononon ooofff ininin hh-hososospipipitatatalll cacacardrdrdioioiovaavascscscullulararar dddeaeaeaththth aaandndnd hhheaeaeartrtrt fffaiaiailullurerere ttthahahaa



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19

Table 1. Baseline Characteristics of Hemodialysis Patients.

Before Propensity Score-Matching Propensity Score-Matched Characteristics AF Non-AF StD AF Non-AF StD†

Patient (no.) 6,772 70,625 6,494 6,494 Mean age (SD), year 68.8 (11.3) 62.0 (13.8) 0.539 68.6 (11.3) 68.4 (11.6) 0.017 Male 3,166 (46.8) 35,417 (50.1) -0.068 3,030 (46.7) 3,030 (46.7) 0.000 Monthly income, NT dollars Dependent 2,628 (38.8) 23,927 (33.9) 0.103 2,511 (38.7) 2,525 (38.9) -0.004 <19,100 1,465 (21.6) 16,503 (23.4) -0.042 1,398 (21.5) 1,366 (21.0) 0.012 19,100 41,999 2,538 (37.5) 27,617 (39.1) -0.033 2,448 (37.7) 2,470 (38.0) -0.007

42,000 141 (2.1) 2,578 (3.7) -0.094 137 (2.1) 133 (2.0) 0.004 Urbanization

Level 1 2,195 (32.4) 22,574 (32.0) 0.010 2,113 (32.5) 2,172 (33.4) -0.019Level 2 4,200 (62.0) 44,466 (63.0) -0.019 4,018 (61.9) 3,960 (61.0) 0.018 Level 3 314 (4.6) 3,002 (4.3) 0.019 303 (4.7) 299 (4.6) 0.003 Level 4 (rural area) 63 (0.9) 583 (0.8) 0.011 60 (0.9) 63 (1.0) -0.005

Outpatient Visits of Cardiologist, in the past one year

0 5 visits 2,981 (44.0) 43,659 (61.8) -0.362 2,931 (45.1) 2,881 (44.4) 0.015 6 10 visits 2,698 (39.8) 20,640 (29.2) 0.225 2,550 (39.3) 2,613 (40.2) -0.020 11 15 visits 701 (10.4) 4,501 (6.4) 0.144 661 (10.2) 678 (10.4) -0.009 >15 visits 392 (5.8) 1,825 (2.6) 0.161 352 (5.4) 322 (5.0) 0.021 Charlson Comorbidity Index Score (SD)

7.2 (2.9) 6.7 (3.0) 0.159 7.2 (2.9) 7.3 (3.0) -0.013

Dialysis vintage, months (quartiles)

38 (13-76) 27 (9-65) 0.151 38 (13-76) 37 (13-75) 0.007

Medication for hypertension Alpha-blocker 249 (3.7) 3,077 (4.4) -0.035 243 (3.7) 238 (3.7) 0.004 Beta-blocker 1,555 (23.0) 14,499 (20.5) 0.059 1,460 (22.5) 1,483 (22.8) -0.008 Calcium channel blocker 2,081 (30.7) 20,516 (29.0) 0.037 1,957 (30.1) 1,998 (30.8) -0.014 Diuretics 855 (12.6) 8,240 (11.7) 0.029 805 (12.4) 806 (12.4) 0.000 ACEI or ARB 1,295 (19.1) 12,887 (18.2) 0.022 1,224 (18.8) 1,259 (19.4) -0.014

Other anti-hypertensive drug 214 (3.2) 2,076 (2.9) 0.013 198 (3.0) 170 (2.6) 0.026 Other Medications

Anti-hyperglycemic drugs 1,551 (22.9) 18,238 (25.8) -0.068 1,503 (23.1) 1,555 (23.9) -0.019Aspirin 1,423 (21.0) 10,104 (14.3) 0.177 1,319 (20.3) 1,312 (20.2) 0.003 Clopidogrel 529 (7.8) 3,437 (4.9) 0.121 512 (7.9) 513 (7.9) -0.001Ticlopidine 144 (2.1) 733 (1.0) 0.087 133 (2.0) 140 (2.2) -0.008Dipyridamole 571 (8.4) 3,882 (5.5) 0.115 514 (7.9) 510 (7.9) 0.002 Nitrate 1,471 (21.7) 8,561 (12.1) 0.258 1,327 (20.4) 1,315 (20.2) 0.005 Statin 522 (7.7) 7,165 (10.1) -0.086 508 (7.8) 544 (8.4) -0.020PPI 554 (8.2) 4,356 (6.2) 0.078 526 (8.1) 517 (8.0) 0.005 NSAID 923 (13.6) 6,671 (9.4) 0.131 867 (13.4) 871 (13.4) -0.002

Comorbid disease Congestive heart failure 4,394 (64.9) 32,804 (46.4) 0.378 4,196 (64.6) 4,174 (64.3) 0.007 Hypertension 4,394 (64.9) 32,804 (46.4) 0.378 4,196 (64.6) 4,174 (64.3) 0.007 Diabetes mellitus 3,953 (58.4) 41,873 (59.3) -0.019 3,826 (58.9) 3,852 (59.3) -0.008Previous Stroke/TIA 2,234 (33.0) 19,803 (28.0) 0.108 2,151 (33.1) 2,151 (33.1) 0.000 Vascular diseases 5,176 (76.4) 41,453 (58.7) 0.386 4,937 (76.0) 5,005 (77.1) -0.025

p y5 visits 2,981 (44.0) 43,659 (61.8) -0.362 2,931 (45.1) 222,8,8,8818181 (((444444.4.4.4) 0) 0) 0.010 visits 2,698 (39.8) 20,640 (29.2) 0.225 2,550 (39.3) 2,2,2,61113 3 3 (4(440.0.0.2)2)2) -0-0-0.0.0.0

1 15 visits 701 (10.4) 4,501 (6.4) 0.144 661 (10.2) 6778 (10.4) -0.015 visits 392 (5.8) 1,825 (2.6) 0.161 352 (5.4) 322 (5.0) 0.02

harlson Comorbidididity Index orrreee (S(S(SDDD)

7.2 (2.9) 6.7 7 7 (3(( .0) 0.159 7..2 2 2 (2( .9) 7.3 (3.0) -0.0

aalalysysysis vintageee, momomontnn hss uuau rrrtiles)

383838 (((131313-7-776))) 27 (999-65)5)) 00.1515151 38 8 (1(1(13-76) ) ) 37373 (133-7-7-75)5)5) 000.0

eeediiicacc tion for hypperrtennssioon Allphphpha-a-a-blb ockekekerrr 2424249 (((3.7.7.7))) 3,3 0777777 (44.4.4))) -000.0.0.0335 24443 (3.7)7)7) 22233838 (33.3 77)7) 0.0.Beta-bbblololockckckere 111,555 (2(22333.000) 1114,4449999 ((2000 55.5) 0) 0) 0.05999 1,4446060 (((2222.555))) 111,4483 (2(2( 22.8))8) 0-0 00.0CCCalllciiium hhchannelll blblblockkker 222,080808111 (3(3(3000.7)7)7) 202020 55,5161616 (((292929 00.0) 0) 0) 0 00.0373737 111 99,9575757 (((303030 11.1))) 111,999999888 (3(3(3000.8)8)8) 00-0 00.00



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Liver Disease 2,422 (35.8) 25,676 (36.4) -0.012 2,345 (36.1) 2,387 (36.8) -0.013Prior Major Bleeding 3,708 (54.8) 35,392 (50.1) 0.093 3,580 (55.1) 3,584 (55.2) -0.001Alcohol or drug abuse 150 (2.2) 2,540 (3.6) -0.082 148 (2.3) 165 (2.5) -0.017

Myocardial infarction 992 (14.6) 6,169 (8.7) 0.185 941 (14.5) 912 (14.0) 0.013 Peptic ulcer disease 4,312 (63.7) 42,617 (60.3) 0.069 4,172 (64.2) 4,161 (64.1) 0.004 Dyslipidemia 3,170 (46.8) 38,613 (54.7) -0.158 3,096 (47.7) 3,096 (47.7) 0.000 Cancer 1,638 (24.2) 14,429 (20.4) 0.090 1,572 (24.2) 1,589 (24.5) -0.006 Autoimmune disease 264 (3.9) 3,850 (5.5) -0.074 260 (4.0) 262 (4.0) -0.002CHA2DS2-VASc

0 130 (1.9) 4,476 (6.3) -0.223 129 (2.0) 129 (2.0) 0.000 1 322 (4.8) 9,383 (13.3) -0.301 311 (4.8) 311 (4.8) 0.000 2 578 (8.5) 9,273 (13.1) -0.148 562 (8.7) 562 (8.7) 0.000 3 836 (12.3) 9,762 (13.8) -0.044 808 (12.4) 808 (12.4) 0.000 4 1,072 (15.8) 10,751 (15.2) 0.017 1,029 (15.8) 1,029 (15.8) 0.000 5 1,156 (17.1) 8,989 (12.7) 0.122 1,098 (16.9) 1,098 (16.9) 0.000 6 1,086 (16.0) 7,681 (10.9) 0.152 1,028 (15.8) 1,028 (15.8) 0.000 7 784 (11.6) 5,468 (7.7) 0.130 760 (11.7) 760 (11.7) 0.000 8 520 (7.7) 3,290 (4.7) 0.126 499 (7.7) 499 (7.7) 0.000 9 288 (4.3) 1,552 (2.2) 0.117 270 (4.2) 270 (4.2) 0.000

* All data were descripted as number (%), except mean age, Charlson Comorbidity Index Score, dialysis vintage, and propensity score. † Imbalance defined as absolute value greater than 0.034. Abbreviations: AF, atrial fibrillation; SD, standard deviation; StD, Standardized difference; NT$, new Taiwan dollars; ACEI, angiotensin-converting-enzyme inhibitors; ARB, Angiotensin II receptor blockers; PPI, proton-pump inhibitor; NSAIDs, Non-steroidal anti-inflammatory drugs; TIA, Transient ischemic attack.

All data were descripted as number (%), except mean age, Charlson Comorbidity Index Score, dialylyysisisis vivivintntagagage,e,, aand opensity score.mbalance defined as absolute value greater than 0.034. breviations: AF, atrial fibrillation; SD, standard deviation; StD, Standardized difference; NT$, new Taiwan dollars

CEI, angiotensin-converting-enzyme inhibitors; ARB, Angiotensin II receptor blockers; PPI, proton-pump inhibitorSAIDs, Non-steroidadaal l anti-inflammatory drugs; TIA, Transienenent ischemic attack.



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Table 2. Incidence and Risk of Stroke, Myocardial Infarction, and Major Bleeding among Hemodialysis Patients With and Without AF.

AF Non-AF Crude Adjusted† Competing Risk‡

No. ofEvent

Annualrate

No. of Event

Annualrate

Hazard Ratio(95% CI)

pValue


pValue


pValue

Before Propensity Score Matching All-cause death 4,642 24.72 22,008 9.56 2.57

(2.49-2.66) <0.001 1.64 (1.59-1.70) <0.001 -

In-hospitalcardiovascular death 2,461 13.11 10,403 4.52 2.88

(2.76-3.01) <0.001 1.89 (1.80-1.99) <0.001 1.71

(1.63-1.80) <0.001

Ischemic stroke 600 3.35 3,963 1.77 1.88 (1.73-2.05) <0.001 1.25

(1.15-1.37) <0.001 1.03 (0.94-1.12) 0.558

Hemorrhagic stroke 278 1.40 2,101 0.92 1.52 (1.34-1.72) <0.001 1.28(

1.12-1.46) <0.001 1.03 (0.90-1.18) 0.683

Myocardial infarction 517 2.84 3,312 1.46 1.94 (1.76-2.12) <0.001 1.30

(1.18-1.44) <0.001 1.14 (1.03-1.27) 0.010

Hospitalization for heart failure 1,719 10.88 7,822 3.58 2.95

(2.80-3.11) <0.001 1.95 (1.84-2.06) <0.001 1.63

(1.54-1.72) <0.001

After Propensity Score Matching All-cause death 4,380 24.33 3,548 14.84 1.59

(1.52-1.66) <0.001 1.59 (1.52-1.67) <0.001 -

In-hospitalcardiovascular death 2,322 12.90 1,629 6.81 1.82

(1.71-1.94) <0.001 1.83 (1.71-1.94) <0.001 1.65

(1.55-1.76) <0.001

Ischemic Stroke 563 3.28 573 2.50 1.27 (1.13-1.42) <0.001 1.27

(1.13-1.43) <0.001 1.01 (0.90-1.14) 0.832

Hemorrhagic stroke 245 1.38 254 1.07 1.24 (1.04-1.49) 0.015 1.24

(1.04-1.48) 0.015 0.99 (0.83-1.18) 0.882

Myocardial infarction 499 2.86 483 2.07 1.33 (1.18-1.51) <0.001 1.33

(1.17-1.51) <0.001 1.06 (0.94-1.21) 0.327

Hospitalization for heart failure 1,636 10.77 1,153 5.25 1.90

(1.76-2.05) <0.001 1.90 (1.76-2.05) <0.001 1.56

(1.45-1.68) <0.001 † Adjusted for propensity score quintile and warfarin use which was calculated as a time-varying covariate. ‡Adjusted for propensity score quintile, using antiplatelet drugs, and warfarin use which was calculated as a time-varying covariate. Abbreviations: CI, confidence interval.

mic stroke 600 3.35 3,963 1.77 1.88 (1.73-2.05) <0.001 1.25

(1.15-1.37) <0.001 1.0333 (0(0(0.9994-4-4 1.1.1.121212) ) ) 0.00 55

orrhagic stroke 278 1.40 2,101 0.92 1.52 (1.34-1.72) <0.001 1.28(

1.12-1.46) <0.001 1.1.1 03033 (00.90-1.18) 000.6868688

ardial infarction 517 2.84 3,312 1.46 1.94 (1.76-2.12) <0.001 1.30

(1.18-1.44) <0.001 1.14 (1.03-1.27) 0.01

talizatiooonnn fofofor rfailililurururee e 1,1,1 11719 10.88 7,822 3.58 2.959595

(2 88.80-0-0-3.1111 ) <0.001 1.95 (1.8.8.84-2. 6606))) <0.001 1.63

(1.54-1.72) <0.0

PrPrPropopopensity S ocorerere Matchingaaausuu e e e death 4,44 8380 2444 33.3333 3,33 54888 1411 88.84 1.5955

(1(( .5 --2-1.6666 ) <0< 00.0 1101 111.59595 (1(( 55.52-1. 7767) <000.0.0.00100 -

ssspipp atatalovavava ccsculuu ar deathhh ,22 2322 2212.90 1,11 629 6.66 8188 1.8288

(1(( .7 --1-1.9499 ) <0<< 00.001 1.83 (1(( 77.71-1.1 4494) <0<0< 00.0010 1. 5655

(1(1( .5.5.55- .1 6676) 00<0.0

mic SSStrtrtrokokokeee 565656333 333.282828 555737373 22 55.5000 1.1.1.27272 (1.13-1.42) <0<0<0.00010101 1.1.1.2722

(1.13-1.43) <0 0.0010101 1..010101 (0.90-1.14) 000.838383

rrhagic stroke 245 1.38 254 1.07 1.24 (1 04 1 49) 0.015 1.24

(1 04 1 48) 0.015 0.99(0 83 1 18) 0. 8888



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22

Table 3. Incidence Rate of Ischemic Stroke and Death in Hemodialysis Patients with AF.

Ischemic Stroke Death No. of Event Annual rate (%)* No. of Event Annual rate (%)*

CHA2DS2-VASc 0 11 1.82 64 10.03 1 17 1.15 168 11.06 2 43 1.97 332 14.65 3 77 2.81 532 18.63 4 77 2.56 721 22.93 5 110 3.84 806 26.43 6 103 4.30 798 31.47 7 74 5.02 577 36.80 8 57 7.25 408 49.51 9 31 8.66 236 63.10

Total patients 600 3.35 4,642 24.72 CHADS2 Score 0 56 1.60 415 11.48 1 61 2.70 519 22.11 2 79 2.22 716 19.35 3 182 4.01 1294 26.64 4 70 4.23 612 35.44 5 101 5.97 657 36.34 6 51 7.37 429 59.58

Total patients 600 3.35 4,642 24.72 *per 100 person-years.

CHADS2 Score 0 56 1.60 415 1111.4448881 61 2.70 519 222.11 2 79 2.22 716 19.35 3 182 4.4.4.010101 1294 26.64 444 70 4.23233 6166 2 35.44 555 11010111 5.97977 665657 7 363636.333444666 51 7.37377 44429 55959.588

Tootatatalll papapatitit enenentststs 606060000 3..353535 444,66,642424 222444.7222 per 100 person-years.



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Table 4. Event Rate and Risks of Ischemic Stroke in Hemodialysis Patients with AF.

Ischemic Stroke Crude Adjusted† Competing Risk‡

No. ofEvent

Annualrate (%)*

Hazard Ratio (95% CI) p Value Hazard Ratio

(95% CI) p Value Hazard Ratio (95% CI) p Value

CHA2DS2-VASc score 0 11 1.82 Reference Reference Reference 1 17 1.15 0.63 (0.30-1.35) 0.237 0.62 (0.29-1.32) 0.215 0.58 (0.28-1.24) 0.148 2 43 1.97 1.08 (0.56-2.10) 0.821 1.05 (0.54-2.04) 0.889 0.86 (0.45-1.65) 0.645 3 77 2.81 1.52 (0.81-2.86) 0.195 1.46 (0.77-2.75) 0.244 1.07 (0.57-1.99) 0.831 4 77 2.56 1.36 (0.72-2.57) 0.338 1.30 (0.69-2.45) 0.418 0.86 (0.46-1.60) 0.634 5 110 3.84 2.03 (1.09-3.78) 0.026 1.90 (1.02-3.55) 0.044 1.13 (0.61-2.08) 0.706 6 103 4.30 2.24 (1.20-4.18) 0.012 2.11 (1.13-3.95) 0.020 1.14 (0.62-2.11) 0.667 7 74 5.02 2.57 (1.36-4.87) 0.004 2.39 (1.26-4.54) 0.008 1.17 (0.63-2.18) 0.622 8 57 7.25 3.58 (1.87-6.87) <0.001 3.37 (1.75-6.48) <0.001 1.43 (0.75-2.72) 0.274 9 31 8.66 4.12 (2.06-8.24) <0.001 3.87 (1.93-7.78) <0.001 1.43 (0.72-2.82) 0.306

*per 100 person-years. †Adjusted for using antiplatelet drugs and warfarin use which was calculated as a time-varying covariate. ‡In this model, non-ischemic stroke death was calculated as a competing risk, adjusted for using antiplatelet drugs and warfarin use which was calculated as a time-varying covariate. Abbreviations: CI, confidence interval.

( ) ( ) ( )77 2.81 1.52 (0.81-2.86) 0.195 1.46 (0.77-2.75) 0.244 1.000777 (0(0(0.5.5.57-7-7-1.1.1.999999) 0)) .877 2.56 1.36 (0.72-2.57) 0.338 1.30 (0.69-2.45) 0.418 0 88.86 (0(( 44.46-6-6-11.1.606060) 0) 0) 0.6110 3.84 2.03 (1.09-3.78) 0.026 1.90 (1.02-3.55) 0.044 1. 313 (((0.00 1611 22-2 0.008)8)8) 00.0 777103 4.30 2.24 (1.20-4.18) 0.012 2.11 (1.13-3.95) 0.020 1.14 (0.62-2.11) 0.674 5.02 2.57 (1.36-4.87) 0.004 2.39 (1.26-4.54) 0.008 1.17 (0.63-2.18) 0.657 7.25 3.58 (1.87-6.87) <0<0<0.001 3.37 (1.75-6.48) <0<< .001 1.43 (0.75-2.72) 0.231 8.66 4.12 (2.06-8.24) <0<0<0.001 3.87 (1.93-3-3-7.77 78) <0.001 1.43 (0.72-2.82) 0.3

000 00 eeperson-years. ttted ffor using antiplatelel t tt rdrugs nnand wawaw rfrfrfaraa ninin uuuse hhwhich h h aawas ac lcul tata dded as a titt emem -v rraryiyiy ngnn covoo rarariate. s s s oomodel, non-ischemic tstror kek d aaeath wwas c lalal uuculated as a compmm etee ing rirr kksk, ad uujust dee for usisisingngng aaantipii la etelel t drugs nnand wa frfrfarinii use wwwhihh hhch wasaa cccalcu alaated dd asnarararyingngng covariate. v aaiatititi nnons:s:s: CI, connnfififideded nce inii eeterval.



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Der-Cherng Tarng, Chih-Ching Lin, Po-Hsun Huang, Szu-Yuan Li and Yung-Tai ChenChia-Jen Shih, Shuo-Ming Ou, Pei-Wen Chao, Shu-Chen Kuo, Yi-Jung Lee, Chih-Yu Yang,

Fibrillation: A Competing-Risk Analysis of a Nationwide CohortRisks of Death and Stroke in Patients Undergoing Hemodialysis With New-Onset Atrial

Print ISSN: 0009-7322. Online ISSN: 1524-4539 Copyright © 2015 American Heart Association, Inc. All rights reserved.

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SUPPLEMENTAL MATERIAL

A concise listing of the variables used in the propensity score model is provided

below.

1. Demographic factors included age, gender, year and month of index date,

monthly income, urbanization level, dialysis vintage, outpatient visits of diabetes

specialist, nephrologist and cardiologist in the past one year.

2. Comorbid factors included Charlson Comorbidity Index score, diabetes

mellitus, congestive heart failure, coronary artery disease, myocardial infarction,

prior stroke/transient ischemic accident, liver diseases, peptic ulcer disease, prior

major bleeding, alcohol or drug abuse, dyslipidemia, proteinuria, cancer and

autoimmune disease.

3. Concomitant medications included anti-hyperglycemic drugs, aspirin,

clopidogrel, ticlopidine, dipyridamole, nitrate, statin, proton pump inhibitors,

non-steroidal anti-inflammatory drugs, and anti-hypertensive drugs such as

alpha-blockers, beta-blockers, calcium channel blockers, diuretics,

angiotensin-converting-enzyme inhibitors/angiotensin II receptor blockers, and

others.

Table 1. Propensity Score Model Results of Probability of Arterial Fibrillation.

Table 2. Incidence and Risk of Stroke among Hemodialysis Patients with and without

AF, Stratified by CHA2DS2-VASc Score

Figure legends

Figure 1. Flow Chart of Patient Selection.

Supplementary Table 1. Propensity Score Model Results of Probability of Arterial

Fibrillation.

95% CI

Parameter Estimate Odds Ratios Lower Upper P value

Age, per year 0.0449 1.046 1.043 1.049 <.0001

Male 0.00583 1.006 0.952 1.063 0.8365

Year of Index Date

1998 1

1999 -0.4464 0.64 0.499 0.821 0.0005

2000 -0.7656 0.465 0.368 0.588 <.0001

2001 -1.0307 0.357 0.284 0.449 <.0001

2002 -1.1986 0.302 0.241 0.378 <.0001

2003 -1.4703 0.23 0.183 0.288 <.0001

2004 -1.7181 0.179 0.143 0.224 <.0001

2005 -1.9033 0.149 0.12 0.186 <.0001

2006 -2.0537 0.128 0.103 0.16 <.0001

2007 -2.214 0.109 0.088 0.136 <.0001

2008 -2.4096 0.09 0.072 0.112 <.0001

2009 -2.6466 0.071 0.057 0.088 <.0001

2010 -3.0255 0.049 0.039 0.06 <.0001

2011 -3.4682 0.031 0.025 0.039 <.0001

Month of Index Date

January 1

February -0.0703 0.932 0.818 1.063 0.2932

March -0.0267 0.974 0.857 1.106 0.681

April -0.0667 0.935 0.822 1.065 0.3138

May -0.1129 0.893 0.786 1.015 0.0843

June -0.1523 0.859 0.754 0.978 0.0221

July -0.1163 0.89 0.783 1.012 0.0766

August -0.148 0.862 0.756 0.984 0.0274

September -0.2467 0.781 0.684 0.893 0.0003

October -0.1529 0.858 0.753 0.979 0.0223

November -0.236 0.79 0.693 0.9 0.0004

December -0.2762 0.759 0.669 0.861 <.0001

Monthly income, NT dollars

Dependent 1

<19,100 0.0115 1.012 0.94 1.089 0.7574

19,10041,999 0.0441 1.045 0.982 1.112 0.1665

42,000 0.03 1.03 0.855 1.242 0.753

Urbanizationa

Level 1 1

Level 2 0.0395 1.04 0.982 1.102 0.1829

Level 3 0.0401 1.041 0.911 1.189 0.5553

Level 4 (rural area) 0.0228 1.023 0.772 1.356 0.8742

Outpatient Visits of diabetes

specialist, in the past one year

05 visits 1

610 visits -0.0457 0.955 0.866 1.054 0.361

1115 visits -0.0234 0.977 0.79 1.207 0.8289

>15 visits -0.056 0.946 0.741 1.206 0.6518

Outpatient Visits of Nephrologist, in

the past one year

05 visits 1

610 visits 1.5246 4.593 3.658 5.768 <.0001

1115 visits 1.3921 4.023 3.235 5.004 <.0001

>15 visits 1.4245 4.156 3.428 5.038 <.0001

Outpatient Visits of Cardiologist, in

the past one year

05 visits 1

610 visits 0.4281 1.534 1.445 1.629 <.0001

1115 visits 0.4258 1.531 1.387 1.69 <.0001

>15 visits 0.5025 1.653 1.453 1.88 <.0001

Dialysis vintage 0.00861 1.009 1.008 1.009 <.0001

Charlson Comorbidity Index Scoreb 0.0264 1.027 1.008 1.046 0.0056

Anti-hypertensive drug

Alpha-blocker -0.1615 0.851 0.736 0.984 0.029

Beta-blocker 0.1483 1.16 1.079 1.246 <.0001

Calcium channel blocker 0.1172 1.124 1.051 1.203 0.0007

Diuretics 0.317 1.373 1.258 1.498 <.0001

Other anti-hypertensive drug 0.0388 1.04 0.888 1.217 0.6295

ACEI or ARB -0.0013 0.999 0.925 1.078 0.9736

Other concomitant medications

Anti-hyperglycemic drugs -0.0915 0.913 0.848 0.982 0.015

Aspirin 0.1786 1.196 1.111 1.286 <.0001

Clopidogrel 0.2292 1.258 1.126 1.404 <.0001

Ticlopidine 0.2374 1.268 1.04 1.545 0.0187

Dipyridamole -0.0915 0.913 0.848 0.982 0.015

Nitrate 0.0583 1.06 0.958 1.172 0.257

Statin 0.1537 1.166 1.082 1.257 <.0001

PPI -0.1991 0.819 0.739 0.909 0.0002

NSAID 0.3355 1.399 1.263 1.548 <.0001

Comorbidities

Diabetes mellitus -0.0793 0.924 0.855 0.998 0.0456

Congestive heart failure 0.5242 1.689 1.585 1.8 <.0001

Previous Stroke/TIA 0.0402 1.041 0.941 1.152 0.4375

Vascular disease 0.1002 1.105 0.93 1.314 0.2553

Liver disease 0.0157 1.016 0.955 1.08 0.6163

Prior Major Bleeding 0.1755 1.192 1.121 1.267 <.0001

Alcohol or drug abuse -0.0744 0.928 0.777 1.11 0.4136

Proteinuria 0.0495 1.051 0.954 1.158 0.3171

Coronary artery disease 0.3891 1.476 1.25 1.742 <.0001

Peptic ulcer disease -0.055 0.946 0.885 1.013 0.1098

Myocardial infarction -0.0828 0.921 0.865 0.98 0.0096

Dyslipidemia -0.0037 0.996 0.92 1.079 0.9283

Cancer 0.217 1.242 1.141 1.353 <.0001

Autoimmune disease -0.1636 0.849 0.742 0.972 0.0175

a Urbanization levels in Taiwan are divided into four strata according to the Taiwan National

Health Research Institute publications. Level 1 designates the most urbanized areas, and level

4 designates the least urbanized areas. b

Charlson Comorbidity Index score is used to determine overall systemic health. With each

increased level of CCI score, there are stepwise increases in the cumulative mortality.

Abbreviations: NT dollars, new Taiwan dollars; ACEI, Angiotensin-converting-enzyme

inhibitors; ARB, Angiotensin II receptor blockers; PPI, Proton-pump inhibitor; NSAIDs,

Non-steroidal anti-inflammatory drugs; TIA, Transient ischemic attack

Supplementary Table 2. Incidence and Risk of Stroke among Hemodialysis Patients With and Without AF, Stratified by CHA2DS2-VASc Score

Stroke risk according to

CHA2DS2-VASc Score

AF Non-AF Crude Adjusted* Competing Risk

†

No. of

Event

Annual

rate

No. of

Event

Annual

rate

Hazard Ratio

(95% CI) p Value

Hazard Ratio

(95% CI) p Value

Hazard Ratio

(95% CI) p Value

Low risk (0-1) 25 1.23 21 0.78 1.56 (0.87-2.80) 0.138 1.55 (0.86-2.79) 0.144 1.29 (0.72-2.32) 0.395

High risk (≥2) 538 3.55 552 2.73 1.26 (1.12-1.42) <0.001 1.27 (1.12-1.43) <0.001 1.00 (0.89-1.13) 0.936

* Adjusted for propensity score quintile and warfarin use which was calculated as a time-vary covariate. † Adjusted for propensity score quintile, using antiplatelet drugs and warfarin use which was calculated as a time-vary covariate.

Abbreviations: CI, confidence interval.

Supplementary Figure 1. Flow Chart of Patient Selection.

Risks of Death and Stroke in Patients Undergoing ... Atrial fibrillation ... Database (NHIRD)....

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