Rev Port Cardiol. 2013;32(1):73---79

Revista Portuguesa de

CardiologiaPortuguese Journal of Cardiology

www.revportcardiol.org

EDITORS’ NETWORK

Almanac 2012: cardiovascular risk scores. The national societyjournals present selected research that has driven recentadvances in clinical cardiology�

Almanac 2012: Scores de risco cardiovascular. As revistas das sociedadescardiológicas nacionais apresentam investigacão selecionada que levou aavancos recentes na cardiologia clínica

Jill P. Pell ∗

Institute of Health and Wellbeing, University of Glasgow, Galsgow, UK

Received 31 October 2012; accepted 31 October 2012

tc

H

Orvbsfpi

Global risk scores use individual level information on non-modifiable risk factors (such as age, sex, ethnicity and familyhistory) and modifiable risk factors (such as smoking statusand blood pressure) to predict an individual’s absolute riskof an adverse event over a specified period of time in thefuture. Cardiovascular risk scores have two major uses inpractice. First, they can be used to dichotomise people intoa group whose baseline risk, and therefore potential abso-lute benefit, is sufficiently high to justify the costs and risksassociated with an intervention (whether treatment or pre-vention) and a group with a lower absolute risk to whom theintervention is usually denied. Second, they can be used toassess the effectiveness of an intervention (such as smok-ing cessation or antihypertensive treatment) at reducing an

individual’s risk of future adverse events. In this context,they can be helpful in informing patients, motivating them

� Secondary publication: This article was published in its entirety,with the consent of the authors and editors, in Heart.2012;98:1272-1277. Doi:10.1136/heartjnl-2012-302143 and is republished withpermission.

∗ Institute of Health and Wellbeing, University of Glasgow, 1 Lily-bank Gardens, Glasgow G12 8RZ, UK.

E-mail address: jill.pell@glasgow.as.uk

orthtwsbtwac

0870-2551/$ – see front matter © 2012 Publicado por Elsevier España, S.http://dx.doi.org/10.1016/j.repc.2012.10.004

o change their lifestyle, and reinforcing the importance ofontinued compliance.

ow have risk scores evolved?

ur understanding of how best to measure and respond toisk has evolved over a number of years. Historically, indi-idual risk factors were measured and managed in isolation,ut this has been replaced by the adoption of global riskcores that calculate overall risk based on a range of riskactors. Also, the opportunistic use of risk scores among peo-le who present to healthcare workers has been replaced byncreased use of either mass screening or targeted screeningf at-risk populations in an effort to identify unmet need andeduce health inequalities. The integration of risk calcula-ors into administrative software packages and online accessave made risk scores readily accessible to all general prac-itioners in the UK.1 The scope of risk scores has recentlyidened beyond coronary heart disease to other conditions,

uch as heart failure and diabetes mellitus. Also, as newiomarkers for cardiovascular disease have been identified,

here has been an increasing number of studies examininghether they can add value to existing risk scores. Finally,s investigators have identified genetic loci associated withardiovascular conditions, studies have started to address

L. em nome da Sociedade Portuguesa de Cardiologia.

7

wi

saumvdavct(ptdirdtbwpm

O

HneecvtA(fvrtdosrsci

srloartabbft

fihuoiiHQeaaeftfcvp

wbirf(bwtdt6aitaTidAN

W

AbdTiusmmcp2Ts

4

hether they could play a role in risk prediction, either insolation or combined with traditional risk factors.

Our approach to evaluating the performance of riskcores has also evolved over time. Initially, methods weredopted from the assessment of screening tests, using meas-res of discrimination such as sensitivity and specificity. Asany predictive models could be expressed as continuous

ariables, interest grew in assessing the performance of pre-ictive models across the whole range of values. This waschieved by plotting sensitivity versus 1-specificity for allalues to produce a receiver operating characteristic (ROC)urve. The area under the ROC curve, also referred to ashe c statistic, ranges from 0.5 (no predictive ability) to 1.0perfect discrimination). For use in clinical or public healthractice, a continuous measure of risk needs to be reducedo two or more categories, but the ROC plot can be useful inetermining the best cut-off values to apply. More recently,nvestigators have used reclassification between differentisk groups to compare the discriminatory performance ofifferent risk scores. Results can be presented simply ashe total percentage of patients into a different risk group,ut the preferred measure is the net reclassification index,hich is calculated from: (proportion of cases moving up ----roportion of cases moving down) ---- (proportion of controlsoving up ---- proportion of controls moving down).

ne hundred and ten ways to measure risk!

istorically, cardiovascular risk scores have focused on coro-ary heart disease; either predicting the risk of adversevents in the general population or among patients withstablished disease such as those presenting with acuteoronary syndromes. There are now 110 different cardio-ascular risk scores that have been developed for use inhe general population.2 More recent risk scores, such asSSIGN (ASsessing cardiovascular risk using SIGN) and QRISKQRESEARCH cardiovascular risk algorithm), have differedrom earlier scores by incorporating socioeconomic depri-ation and family history into the measurement of globalisk.3---5 As a result, they have been able to overcome some ofhe limitations of earlier risk scores, which tended to intro-uce socioeconomic bias into the detection and treatmentf cardiovascular risk.4 However, the performance of all riskcores is dependent on ready access to complete and accu-ate data. In a recent study, in which they applied six riskcores to routine general practice data, de la Iglesia andolleagues4 highlighted missing data as a concern, especiallyn relation to family history.

Knowledge of risk scores can translate into improved pre-cribing and reduced risk.6 However, in a recent systematiceview, Liew and colleagues7 highlighted a number of prob-ems in the development of risk scores including a lackf standardisation in the measurement of risk predictorsnd outcomes, and failure of most studies constructing newisk scores to take account of individuals who are alreadyaking medications that modify risk measurement, such asntihypertensive and lipid-lowering agents. The latter may

e misleading because primary prevention should, ideally,e directed at individuals before the development of riskactors and the occurrence of premature disease. One ofhe limitations of existing risk scores based on events over a

wssa

J.P. Pell

xed period of time, commonly 10 years, is that the score iseavily influenced by age. Therefore, young individuals arenlikely to reach the threshold for intervention irrespectivef their current and future risk factors. One approach todentifying the subgroup of young people at increased risks to use lifetime risk rather than risk over a fixed period.ippisley-Cox and colleagues8 recently compared the use ofRisk2 reported as the lifetime risk of cardiovascular dis-ase (in terms of age-sex specific centiles) with it reporteds risk over a 10-year period. The former identified a greater

proportion of younger individuals as being at risk of futurevents. It also classified a greater proportion of individualsrom ethnic minority groups and with a positive family his-ory as being at risk of future cardiovascular events. Bothactors are associated with an increased risk of prematureardiovascular events. While early identification and pre-ention are the ideal, the unselected screening of a youngeropulation may, nonetheless, be less cost-effective.

The application of risk scores to patients presentingith acute coronary syndrome is now well established inoth research and clinical practice. In a recent Educationn Heart paper, Bueno and Fernandez-Aviles9 reviewed 11isk scores developed for the prediction of adverse eventsollowing acute coronary syndrome. Of these, the GRACEGlobal Registry of Acute Coronary Events) and TIMI (Throm-olysis in Myocardial Infarction) risk scores have been mostidely adopted. Fox and colleagues10 recently reviewed

he extent to which the GRACE risk score has been vali-ated and adopted since first developed in 2003. To date,he GRACE risk score has been externally validated in7 individual studies comprising at least 500 patients withcute coronary syndrome, ST-segment elevation myocardialnfarction or non-ST-segment elevation myocardial infarc-ion. The risk score is easy to use in a clinical settingnd performs well when compared with other risk scores.herefore, it has been incorporated into many guidelines

ncluding those produced by the European Society of Car-iology, American College of Cardiologists, American Heartssociation, Scottish Intercollegiate Guidelines Network andational Institute for Health and Clinical Excellence.

here next for risk scores?

ttention is now focusing on expanding the use of risk scoreseyond coronary heart disease. Two recent studies haveeveloped risk scores for use in patients with heart failure.he HF-Action (Heart Failure: A Controlled Trial Investigat-

ng Outcomes of Exercise TraiNing) risk score was developedsing a cohort of patients with chronic heart failure andystolic dysfunction.11 The risk score was derived from infor-ation on exercise duration, serum urea nitrogen, bodyass index and sex, and performed well at predicting all-

ause death within 1-year of follow-up. Nineteen per cent ofatients in the top decile for risk score died, compared with% in the bottom decile. The score had a c statistic of 0.73.he GWTG-HR (Get With The Guidelines----Heart Failure) riskcore was developed using a cohort of patients hospitalised

ith heart failure.12 The component factors included age,

ystolic blood pressure, blood urea nitrogen, heart rate,odium, concomitant chronic obstructive pulmonary diseasend race. The risk of in-hospital death ranged from 0.4% to

fwTtiswcaors

pvdNfNgettswwamanierna

olobpBreoios

drtesgsrp

Almanac 2012: cardiovascular risk scores

9.7% across the risk score deciles and performed well amongboth patients with preserved and impaired left ventricularsystolic function with a c statistic of 0.75 in both groups.

Due to the rising prevalence of type II diabetes, there hasbeen increased awareness of the need to target screeningand prevention efforts at people with this condition. VanDieren et al13 undertook a systematic review of studiespublished between 1966 and 2011 that had developed car-diovascular risk scores suitable for use in patients with typeII diabetes mellitus. Of the 45 scores identified, only 12were originally constructed from a cohort of individuals withdiabetes and only two of these were restricted to patientsin whom diabetes had been recently diagnosed. Only ninestudies reported the c statistic. Six scores had undergoneinternal validation, using bootstrapping or a split sample,and six had been subject to external validation. Two studieshad neither internal nor external validation. The authorsidentified an additional 33 scores that were constructedfrom the general population but included diabetes as a pre-dictive factor. Only 12 had internally validated their riskscore using a split sample, cross-validation or bootstrapping,and only eight had been externally validated in a popula-tion with diabetes. Given the increasing prevalence of typeII diabetes and its increasing contribution to cardiovasculardisease, further research is required in this area.

Do biomarkers add value?

Several recently published studies have examined whetherthe addition of biomarkers improved the performance ofrisk scores in the general population. A common focusof these studies has been trying to achieve better discrimi-nation within the subgroup of individuals currently classifiedas having intermediate risk (10-20% risk of an adverse eventover 10 years). Melander and colleagues14 evaluated theadded value of a panel of biomarkers, C-reactive protein(CRP), cystatin C, lipoprotein-associated phospholipase A2(Lp-PLA2), mid-regional pro-adrenomedullin (MR-proADM),mid-regional pro-atrial natriuretic peptide and N-terminalpro-B-type natriuretic peptide (NT-proBNP), in predictingincident cardiovascular events in a Swedish populationcohort. There was a non-significant increase in the c statis-tic. In relation to predicting cardiovascular events, 8% werereclassified overall but only 1% were moved into the high-risk category. There was no net reclassification. Among theintermediate risk group, the addition of biomarkers resultedin reclassification of 16% in terms of their risk of cardiovascu-lar events, but only 3% were moved into the high-risk group.The net reclassification improvement was 7.4%. Therefore,the improvements in classification were largely achieved bydown-grading, rather than identifying a greater proportionof high-risk individuals.

Rana and colleagues15 examined the added value ofa series of individual biomarkers in the UK populationin predicting coronary events: CRP, myelo-peroxidase,paraoxonase, group IIA secretory phospholipase A2, Lp-PLA2, fibrinogen, macrophage chemoattractant protein 1

and adiponectin. Reclassification was greatest for CRP,the addition of which resulted in 12% net reclassificationimprovement overall and 28% in the intermediate group.Zethelius and colleagues16 examined the added value of

mtifi

75

our biomarkers (troponin I, NT-proBNP, cystatin C and CRP)hen applied to a population cohort of elderly Swedish men.he addition of all four biomarkers significantly increasedhe c statistic from 0.66 to 0.77. They reported a 26% netmprovement in reclassification overall. The studies to dateuggest that biomarker assays may improve discriminationhen added to existing risk scores. However, their use hasost and logistical implications, particularly if risk scoresre applied on a wide scale. Further research is neededn the cost-effectiveness of adding biomarkers to existingisk scores, particularly in relation to general populationcreening.

Lorgis and colleagues17 demonstrated that adding NT-roBNP to the GRACE risk score can improve its prognosticalue among patients presenting with acute coronary syn-rome. Patients with both a high GRACE risk score and highT-proBNP level had a 50% risk of dying within 1 year ofollow-up. This was sixfold higher than the referent group.T-proBNP was found to be a useful addition across all ageroups but not in obese patients, in whom NT-proBNP lev-ls were much lower.18 Similar findings were reported whenroponin and brain natriuretic peptide were used in addi-ion to the TIMI risk score.19 Their addition produced only alight increase in the c statistic but, as with NT-proBNP, theyere able to identify a subgroup of the TIMI high-risk groupho were at very high risk of adverse events, and in whomn aggressive approach to drug therapy and interventionsight be warranted.18 Damman and colleagues20 examined

cohort of patients undergoing primary percutaneous coro-ary intervention (PCI) for ST-segment elevation myocardialnfarction. They demonstrated that the addition of biomark-rs (glucose, NT-proBNP and estimated glomerular filtrationate) improved the prediction of mortality, resulting in sig-ificant improvements in net reclassification (49%, p < 0.001)nd integrated discrimination (3%, p < 0.01).

Risk scores, such as CHADS2-VASC2, can predict the riskf cerebrovascular events among patients with atrial fibril-ation, and are used to inform clinical decisions on the usef anticoagulant therapy. A number of biomarkers has noween identified that are associated with the incidence andrognosis of atrial fibrillation. In a recent review paper,rugts and colleagues21 highlighted the need for furtheresearch to determine whether the use of these biomark-rs may improve the existing risk scores and whether theyffer the potential for risk prediction at an earlier stage bydentifying patients at risk of developing atrial fibrillationr at risk of progressing from the subclinical to permanenttage of the condition.

Many pathophysiological mechanisms contribute to theevelopment of heart failure. Avellino and colleagues22

eviewed recently identified biomarkers associated withhe relevant pathways. They concluded that the biomark-rs currently showing most promise, in terms of risktratification, were Lp-PLA2 (inflammation), neutrophilelatinase-associated lipocalin and cystatin C (both renaltress), procollagen-1-polypeptide (extra-cellular matrixemodelling), brain natriuretic peptide, NT-proBNP, MR-roADM, soluble ST2 receptor and copeptin (all cardiac

yocyte stress), and endothelin 1 (neurohormone regula-

ion). Gustav Smith and colleagues23 demonstrated that,n terms of predicting incident heart failure and atrialbrillation in a general population cohort, the addition

7

ouctfmigpiTtaHpiwYpncahc

ititrssstanebpraoTiswgs

awnqsatbbaean

D

Ciggiwgoayrosh

mpspimtbplalq1rim(c

aaicddwolrotlviatpo

m

6

f a panel of biomarkers (mid-regional pro-atrial natri-retic peptide, NT-proBNP, MR-proADM, cystatin C, CRP andopeptin) to conventional risk factors improved discrima-ion. The net reclassification improvement was 22% for heartailure and 7% for atrial fibrillation. Reclassification wasainly achieved by the identification of additional high-risk

ndividuals. In a recent review, Ketchum and Levy24 sug-ested that risk scores had an increasing role to play amongatients with advanced heart failure whose survival hasmproved due to therapeutic and technological advances.hey suggested that risk scores could be used to assisthe selection of patients for trans-plantation, left ventriclessist devices and implantable cardioverter defibrillators.aines and colleagues25 recently developed a risk score toredict post-procedural complications associated with themplantation of cardioverter defibrillators. The risk scoreas based on 10 readily available variables: age, sex, New

ork Heart Association class, presence of atrial fibrillation,revious valve surgery, chronic lung disease, blood ureaitrogen, re-implantation for reasons other than batteryhange, use of a dual chamber or biventricular device and

non-elective procedure. The 4% of the population in theighest risk category possessed a 8% risk of complications,ompared with less than 1% in the lowest risk group.25

Studies have recently started to address whether non-nvasive imaging of the coronary vessels could add valueo existing risk scores.26 The coronary artery calcium scores a marker of vascular injury and correlates well withhe overall atherosclerotic burden.23 Coronary CT angiog-aphy can detect non-calcified plaque and indicates theeverity of coronary artery stenoses.26 Both have beenhown to be of incremental value in risk prediction amongymptomatic patients, but studies are generally lacking onhe utility of incorporating them into risk scores for usemong asymptomatic people. Carotid intima-media thick-ess is a significant predictor of the risk of cardiovascularvents in individuals without carotid plaques.27 When com-ined with information on the number of segments withlaque, to produce a total burden of carotid atheroscle-osis score, the c statistic and net reclassification indexre improved by 6.0% and 17.1%, respectively. The costf imaging is generally greater than for blood biomarkers.herefore, the incremental cost is likely to be prohibitive

n terms of the routine addition to general population riskcores. Cost-effectiveness studies are required to explorehether the additional costs can be justified in a sub-roup of asymptomatic individuals identified by existing riskcores.

One of the few studies to assess the cost-effectiveness ofdding biomarkers to clinical risk scores examined patientsith stable angina who were on the waiting list for coro-ary artery bypass grafting.28 They compared the statusuo strategy of no formalised prioritisation with prioriti-ation using a clinical risk score in isolation and prioritisationfter supplementing the clinical risk scores with addi-ional biomarker information using a routinely assessediomarker (estimated glomerular filtration rate), a noveliomarker (CRP), or both. They demonstrated that the

ddition of the routinely assessed biomarker improved cost-ffectiveness in terms of the net effect on lifetime costsnd quality-adjusted life-years. In contrast, addition of theovel biomarker was not cost-effective.

iaas

J.P. Pell

o genetic markers add value?

ardiovascular disease is a complex condition, with severalntermediate phenotypes, to which both environmental andenetic risk factors predispose. As increasing numbers ofenetic markers has been identified, it has become increas-ngly clear that the genetic component is also complex,ith relatively small contributions from a large number ofenes. Therefore, attention has focused on the developmentf a multilocus genetic risk score that summates the over-ll risk from known genetic markers. In the past couple ofears, several studies have investigated whether a geneticisk score can add value to established risk scores, somef which already include information on family history. Thetudies have been undertaken in a variety of populations butave reached consistent conclusions.

Ripatti and colleagues29 studied seven cohorts ofiddle-aged men and women recruited from the generalopulations in Finland and Sweden. They used publishedtudies to identify 13 recently discovered single nucleotideolymorphisms (SNP) associated with either myocardialnfarction or coronary heart disease. They constructed aulilocus genetic risk score for each individual by summing

he number of risk alleles for each of the 13 SNP weightedy effect size. The genetic risk score was an independentredictor of incident coronary heart disease, cardiovascu-ar disease and myocardial infarction when adjusted forge, sex and traditional risk factors. In comparison with theowest quintile of genetic risk score, individuals in the topuintile had an adjusted RR of coronary heart disease of.66 (95% CI 1.35 to 2.04). However, addition of the geneticisk score to traditional risk factors did not significantlymprove the c statistic. There was a significant improve-ent in net reclassification of people at intermediate risk

10-year predicted risk of 10-20%) but there was no signifi-ant improvement in net reclassification overall.

Paynter and colleagues30 undertook a similar study using cohort of white professional women in the USA. They usedn online catalogue of genome-wide association studies todentify 101 SNP shown to be associated with any form ofardiovascular disease (including stroke) or any interme-iate phenotype (such as diabetes and hypertension), anderived a genetic risk score from the sum of all risk allelesithout weighting. They also re-ran the analyses includingnly the 12 SNP shown to be associated with cardiovascu-ar disease. In comparison with the lowest tertile of geneticisk score, individuals in the highest tertile had a higher RRf cardiovascular events (RR 1.22, 95% CI 1.02 to 1.45) buthe difference in the absolute 10-year risk of cardiovascu-ar disease in the top and bottom tertiles was small (3.7%s 3.0%). Unlike family history (which encompasses overallnherited risk), the genetic risk score was not significantlyssociated with cardiovascular events after adjustment forraditional risk factors. Addition of the genetic risk scoreroduced no significant improvement in either the c statisticr net reclassification.

Qi and colleagues31 undertook a case- control study ofyocardial infarction survivors in Costa Rica. They exam-

ned SNP associated with myocardial infarction and coronaryrtery disease in at least two previous genome-wide associ-tion studies. Of the 14 SNP identified from the literature,even had significant associations with the risk of myocardial

qa

oTtaEptaotrcyaerCia((gpsc

cwmwsbaopbtwgnSii

uwpidp(RrsP

Almanac 2012: cardiovascular risk scores

infarction in their Hispanic cohort. These were used to calcu-late a genetic risk score based on the sum of the risk alleles.They demonstrated a dose relationship, whereby the riskof myocardial infarction increased with increasing geneticrisk score and persisted after adjustment for traditional riskfactors, including family history. However, addition of thegenetic risk score only increased the c statistic from 0.67 to0.68.

In common with the previous study by Paynter andcolleagues,30 Thanassoulis and colleagues32 calculated twodifferent genetic risk scores: a more restrictive scorederived from 13 SNP previously associated with coronaryheart disease or myocardial infarction, and a less restric-tive score that included an additional 89 SNP associatedwith intermediate phenotypes. In both approaches, theyalso used both a simple and weighted count of risk alleles.Finally, they re-ran the restrictive score adding an additional16 recently identified SNP. The genetic risk scores wereapplied to the Framingham Offspring Cohort. The restrictivegenetic risk score performed better than the less restrictivescore and was an independent predictor of both coronaryheart disease and cardiovascular events. Nonetheless, it didnot improve discrimination or classification even after addi-tion of the additional SNP.

These studies consistently demonstrate that, even ifgenotypic information is summarised into an overall riskscore, it does not improve the performance of existingrisk scores and therefore has no obvious clinical utility, atpresent, in selecting middle-aged people for interventions.Further research is required to explore whether genetic riskscores have any role to play in identifying the subgroup ofyoung people who are most likely to acquire a high-risk scorein the future and, if so, the costs, risks and benefits of pro-viding preventive interventions, such as education, to thissubgroup at an earlier stage.

Procedure risk scores

Faroq and colleagues33,34 recently reviewed the use of riskscores for patients undergoing coronary revascularisation.Clinical risk scores, such as PARSONNET (Predictive score foracquired adult heart surgery: Additive and Logistic Regres-sion models) and EuroSCORE (European System for CardiacOperative Risk Evaluation), have been widely adopted intoclinical practice for patients undergoing coronary revas-cularisation. Anatomy-based risk scores, which contain noclinical information, have been developed using informa-tion derived from diagnostic angiography. As coronary arterygrafts are used to bypass stenoses and the anastomosesare positioned distal to the diseased segment, additionalanatomical information does not significantly improve theperformance of clinical risk scores among patients beingmanaged surgically. In contrast, the severity, length anddistribution of stenoses are critical to the selection and out-come of patients undergoing PCI. Anatomy-based scores,such as SYNTAX (SYNergy between PCI with TAXus andsurgery), have been shown to be predictive of clinical out-

comes following PCI,35 but visual interpretation of coronaryangiograms is subject to interobserver variation. There-fore, functional anatomy-based scores, which incorporateobjective information from fractional flow reserve or

sdrc

77

uantitative coronary angiography, have better prognosticbility.

More recently, a number of risk scores has been devel-ped that combine clinical and anatomical information.36---42

he EuroHeart score is constructed from 12 clinical charac-eristics and four lesion characteristics. It was developednd validated on the 46 064 patients recruited to theuroHeart Survey of PCI and performed well at identifyingatients at risk of in-hospital death, producing a c statis-ic of 0.90.36 The Clinical SYNTAX Score (CSS) combines thenatomically derived SYNTAX score with a modified versionf the clinical ACEF (Age, Creatinine and Ejection Frac-ion) score. Patients in the highest tertile of CSS had higherates of repeat revascularisation (21%) and major adverseardiac and cerebrovascular events (MACCE) (32%) over 1-ear following PCI, with evidence of a dose relationshipcross the tertiles.37 The CSS had a higher c statistic thanither the SYNTAX score or ACEF score used in isolation inelation to predicting both MACCE and all-cause death.37

apodanno and colleagues38 compared two combined clin-cal/anatomical risk scores (the Global Risk Classificationnd the Clinical SYNTAX risk score), two clinical risk scoresACEF and EuroSCORE) and one anatomy-based risk scoreSYNTAX) among patients with left main stem stenosis under-oing either PCI or coronary artery bypass grafting. The bestredictive characteristics were obtained using a clinical riskcore (ACEF) for surgical patients compared with a combinedlinical/anatomical risk score (GRC) for PCI.

Similarly, Chen and colleagues39 compared the combinedlinical/anatomical NERS (New Risk Stratification Score)ith the CSS in terms of predicting the risk of MACCE over 6onths follow-up, among patients in whom coronary stentsere implanted for left main stem stenoses. In compari-

on with the clinical risk score, the combined score hadoth higher sensitivity and higher specificity.39 Chakravartynd colleagues40 also examined patients treated by surgeryr PCI for left main stem disease. They compared theerformance of a combined risk score, produced by com-ining the PARSONNET and SYNTAX risk scores, with usinghe latter, an anatomical risk score, in isolation. Patientsere followed up for a median of 3 years. The study sug-ested that using anatomical information in isolation didot predict outcome following surgery. In contrast, theYNTAX risk score was predictive among patients undergo-ng PCI but could be improved by the addition of clinicalnformation.

Many of the risk scores developed for use in patientsndergoing coronary revascularisation predated theidespread adoption of drug-eluting stents and, therefore,erform less well in these patients than in those undergo-ng balloon angioplasty. Stolker and colleagues43 recentlyeveloped and validated a risk score that combined clinical,rocedural and anatomical information using the EVENTEvaluation of Drug Eluting Stents and Ischaemic Events)egistry, and evaluated its ability to predict target lesionevascularisation at 1-year follow-up. The relatively simplecore was composed of only six variables: age, previousCI, left main PCI, saphenous vein graft location, minimumtent diameter and total stent length. The investigatorsemonstrated a threefold difference in target lesion

evascularisation between the highest risk and lowest riskategories (7.5% vs 2.2%).

7

C

CtanwbbatcAmreaniaaitialeb

CPpH

R

1

1

1

1

1

1

1

1

1

1

2

2

2

2

2

2

8

onclusion

ardiovascular risk scores have existed for many years buthey are still subject to new and interesting research. Theyre increasingly being applied to conditions other than coro-ary heart disease, such as type II diabetes and heart failure,hich are of increasing importance for public health. Newiomarkers have been identified that improve discriminationut, inevitably, the marginal benefit decreases with eachdditional predictor. Also, improved discrimination needso be weighed against increased cost and complexity, espe-ially when risk scores are applied to the general population.s highlighted in a recent Heart editorial, ease of use has aajor impact on the implementation of risk scores.3 Recent

esearch has focused on identifying new biomarkers andvaluating their effectiveness, but there is a paucity ofpplied research on cost-effectiveness and coverage. Thiseeds to be addressed. The conclusions may differ depend-ng on the location in which risk scores are being measurednd the subgroup of the population to which they arepplied. To date, there is no evidence that genetic markersmprove risk prediction when used in middle-aged popula-ions. If they have a role to play, it may be in younger peoplen whom traditional risk scores are of little value. Anotherpproach to identifying at-risk individuals at a younger age isifetime risk. Irrespective of the approach adopted, the cost-ffectiveness of earlier screening and intervention needs toe properly evaluated.

ompeting interests None.

rovenance and peer review Commissioned; externallyeer reviewed.eart 2012;-:1e6. doi:10.1136/heartjnl-2012-302143

eferences

1. Parkes G. Using risk scores with patients. BMJ. 2010;340:c3327.2. Cooper A, Nherera L, Calvert N, et al. Clinical Guidelines and

Evidence Review for Lipid Modification: Cardiovascular RiskAssessment and the Primary and Secondary Prevention of Car-diovascular Disease. London: National Collaborating Centre forPrimary Care and Royal College of General Practitioners; 2008.

3. Tunstall-Pedoe H. Cardiovascular risk and risk scores: ASSIGN,Framingham, QRISK and others: how to choose. Heart.2011;97:442---4.

4. de la Iglesia B, Potter JF, Poulter NR, et al. Performance ofthe ASSIGN cardiovascular disease risk score on a UK cohort ofpatients from general practice. Heart. 2011;97:491---9.

5. Collins GS, Altman DG. An independent and external validationof QRISK2 cardiovascular disease risk score: a prospective opencohort study. BMJ. 2010;340:c3442.

6. Sheridan SL, Crespo E. Does the routine use of global coronaryheart disease risk scores translate into clinical benefit or harms?A systematic review of the literature. BMC Health Serv Res.2008;8:60.

7. Liew SM, Doust J, Glasziou P. Cardiovascular risk scores donot account for the effect of treatment: a review. Heart.2011;97:689---97.

8. Hippisley-Cox J, Coupland C, Robson R, et al. Derivation, vali-dation, and evaluation of a new QRISK model to estimatelifetime risk of cardiovascular disease: cohort study using QRe-search database. BMJ. 2010;341:c6624.

2

J.P. Pell

9. Bueno H, Fernandez-Aviles F. Use of risk scores in acute coronarysyndromes. Heart. 2012;98:162---8.

0. Fox KA, Eagle KA, Gore JM, et al., for the GRACE and GRACE2Investigators. The Global Registry of acute coronary events,1999 to 2009 e GRACE. Heart. 2010;96:1095---101.

1. O’Connor CM, Whellan DJ, Wojdyla D, et al. Factors related tomorbidity and mortality in patients with chronic heart failurewith systolic dysfunction: the HFeACTION Predictive Risk ScoreModel. Circ Heart Fail. 2012;5:63---71.

2. Peterson PN, Rumsfeld JS, Liang L, et al. A validatedrisk score for in-hospitality mortality in patients with heartfailure from the American Heart Association get with theguidelines program. Circ Cardiovasc Qual Outcomes. 2010;3:25---32.

3. Van Dieren S, Beulens JW, Kengne AP, et al. Predictionmodels for the risk of cardiovascular disease in patientswith type 2 diabetes: a systematic review. Heart. 2012;98:360---9.

4. Melander O, Newton-Cheh C, Almgren P, et al. Novel and con-ventional biomarkers for prediction of incident cardiovascularevents in the community. JAMA. 2009;302:49---57.

5. Rana JS, Cole M, Despres JP, et al. Inflammatory biomarkers andthe prediction of coronary event among people at intermedi-ate risk: the EPICeNorfolk prospective population study. Heart.2009;95:1682---7.

6. Zethelius B, Berglund L, Sundstrom J, et al. Use of multiplebiomarkers to improve the prediction of death from cardiovas-cular causes. N Engl J Med. 2008;358:2107---16.

7. Lorgis L, Zeller M, Dentan G, et al. Prognostic value of N-terminal pro-brain natriuretic peptide in elderly people withacute myocardial infarction: prospective observational study.BMJ. 2009;338:b1605.

8. Lorgis L, Cottin Y, Danchin N, et al. Impact of obesity on theprognostic value of the N-terminal pro-B-type natriuretic pep-tide (NT-proBMP) in patients with acute myocardial infarction.Heart. 2011;97:551---6.

9. Tello-Montoliu A, Marin F, Roldan V, et al. The additive valueof biomarkers to clinical risk scores in acute coronary syn-drome. Are biomarkers really ready for real world usage? Heart.2010;96:227---8.

0. Damman P, Beijk MA, Kuijt WJ, et al. Multiple biomarkers atadmission significantly improves the prediction of mortality inpatients undergoing primary percutaneous coronary interven-tion for acute ST-segment elevation myocardial infarction. JAm Coll Cardiol. 2011;57:29---36.

1. Brugts JJ, Akin S, Helming AM, et al. The predictive valueof cardiac biomarkers in prognosis and risk stratification ofpatients with atrial fibrillation. Curr Opin Cardiol. 2011;26:449---56.

2. Avellino A, Collins SP, Fermann GJ. Risk stratification andshort-term prognosis in acute heart failure syndromes:a review of novel biomarkers. Biomarkers. 2011;16:379---92.

3. Gustav Smith J, Newton-Cheh C, Almgren P, et al. Assess-ment of conventional cardiovascular risk factors and multiplebiomarkers for the prediction of incident heart failure and atrialfibrillation. J Am Coll Cardiol. 2010;56:1712---9.

4. Ketchum ES, Levy WC. Multivariate risk scores and patientoutcomes in advanced heart failure. Congest Heart Fail.2011;17:205---12.

5. Haines DE, Wang Y, Curtis J. Implantable cardioverter-defibrillator registry risk score models for acute proce-dural complications of death after implantable cardioverter-defibrillator implantation. Circulation. 2011;123:2069---76.

6. Wilson SR, Lin FY, Min JK. Role of coronary artery calcium scoreand coronary CT angiography in the diagnosis and risk stratifica-tion of individuals with suspected coronary artery disease. CurrCardiol Rep. 2011;13:271---9.

3

3

3

4

4

4

Almanac 2012: cardiovascular risk scores

27. Xie W, Liang L, Zhao L, et al. Combination of carotid intima-media thickness and plaque for better predicting risk ofischaemic cardiovascular events. Heart. 2011;97:1326---31.

28. Henriksson M, Palmer S, Chen R, et al. Assessing the cost-effectiveness of using prognostic biomarkers with decisionmarkers: case study in prioritizing patients waiting for coronaryartery surgery. BMJ. 2010;340:b5606.

29. Ripatti S, Tikkanen E, Orho-Melander M, et al. A multi-locusgenetic risk score for coronary heart disease: caseecontrol andprospective cohort analyses. Lancet. 2010;376:1393---400.

30. Paynter NP, Chasman DI, Par’e G, et al. Association between aliterature-based genetic risk score and cardiovascular events inwomen. JAMA. 2010;303:631---7.

31. Qi L, Ma J, Qi Q, et al. Genetic risk score and risk of myocardialinfarction in Hispanics. Circulation. 2011;123:374---80.

32. Thanassoulis G, Peloso GM, Pencina MJ, et al. A genetic riskscore is associated with incident cardiovascular disease andcoronary artery calciumdthe Framingham Heart Study. Circ Car-diovasc Genet. 2012;5:113---21.

33. Farooq V, Brugaletta S, Serruys PW. Utilizing risk scores indetermining the optimal revascularization strategy for com-plex coronary artery disease. Curr Cardiol Rep. 2011;13:415---23.

34. Farooq V, Brugaletta S, Serruys PW. Contemporary and evolvingrisk scoring algorithms for percutaneous coronary intervention.Heart. 2011;97:1902---13.

35. Capodanno D, Tamburino C. Integrating the Synergy betweenpercutaneous coronary intervention with Taxus and Cardiac

Surgery (SYNTAX) score into practice: use, pitfalls, and newdirections. Am Heart J. 2011;161:462---70.

36. de Mulder M, Gitt A, van Domburg R, et al. EuroHeartscore for the evaluation of in-hospital mortality in patients

4

79

undergoing percutaneous coronary intervention. Eur Heart J.2011;32:1398---408.

7. Garg S, Sarno G, Garcia-Garcia HM, et al. A new tool for therisk stratification of patients with complex coronary arterydisease. The clinical SYNTAX score. Circ Cardiovasc Interv.2010;3:317---26.

8. Capodanno D, Caggegi A, Miano M, et al. Global risk classi-fication and clinical SYNTAX (synergy between percutaneouscoronary intervention with TAXUS and cardiac surgery) score inpatients undergoing percutaneous or surgical left main revas-cularisation. J Am Coll Cardiol Intv. 2011;4:287---97.

9. Chen SL, Chen JP, Mintz G, et al. Comparison between theNERS (New Risk Stratification) score and the SYNTAX (Syn-ergy between Percutaneous Coronary Intervention with Taxusand Cardiac Surgery) score in outcome prediction for unpro-tected left main stenting. J Am Coll Cardiol Intv. 2010;3:632---41.

0. Chakravarty T, Buch MH, Naik H, et al. Predictive accuracy ofSYNTAX score for predicting long-term outcomes of unprotectedleft main coronary artery revascularization. Am J Cardiol.2011;107:360---6.

1. Peterson ED, Dai D, DeLong ER, et al. Contemporary mortalityrisk prediction for percutaneous coronary intervention. J AmColl Cardiol. 2010;55:1923---32.

2. Singh M, Holmes DR, Lennon RJ, et al. Development and vali-dation of risk adjustment models for long-term mortality andmyocardial infarction following percutaneous coronary inter-vention. Circ Cardiovasc Interv. 2010;3:423---30.

3. Stolker JM, Kennedy KF, Lindsey JB, et al. Predicting resteno-sis of drug-eluting stents placed in real-world clinical practice.Derivation and validation of a risk model from the EVENT Reg-istry. Circ Cardiovasc Interven. 2010;3:327---34.