Raul D. Santos MD, PhDHeart Institute-InCor

University of Sao Paulo Brazil

CVDriskassessmentusingriskscoresinprimaryandsecondaryprevention

Disclosure

• Honorariaforconsultingandspeakeractivitiesonthelastyearfrom– Amgen,AstraZeneca,Akcea– Biolab,Merck,Novo-Nordisk– Pfizer,Kowa– Sanofi/Regeneron

3

• WhydoweneedtostratifyASCVDrisk?• Whatishighrisk?

– Thresholdsbasedoncost/effectiveness• Howscoresaremade?

– Howtovalidateariskbiomarker– Externalvalidity(calibration)

• Limitationsandnewbiomarkers

Atheroscleroticcardiovasculardiseaseriskstratification

4

WhydoweneedtostratifyASCVDrisk?

ASCVD

• Firstcauseofdeathintheworld• Multifactorialdisease• Heterogeneityinrisk

– Individualswiththesameriskfactorsmayornothaveevents• Pharmacologicaltreatments

– Cost/effectiveness– Risk/Benefits

6

Bloodcholesterolandvascularmortalitybyage,sexandbloodpressure:

ameta-analysisofindividualdatafrom61prospectivestudieswith55000vasculardeaths

Lancet2007;370:1829-39

Lancet 2007; 370: 1829–39

N=900,000

Impact of 1mmol/L reduction in LDL-C upon major cardiovascular events and mortality

CTT 2010Relative Risk (95%CI)

All causemortality 0.90(0.87-0.93),p<0.0001**

CHDmortality 0.80(0.74—0.87);p<0.0001**

Other cardiac deaths 0.89(0.81—0.98);p=0.002**

Stroke deaths 0.96(0.84—1.09);p=0.5

Majorvascularevents 0.78(0·76—0·80);p<0.0001Non-fatalMI 0.73(0.70−0.77);p<0.0001

Myocardial revascularization 0.75(0.72−0.78);p<0.0001

Ischemic stroke 0.79(0.74−0.85);p<0.0001

Cancer incidence 1.00(0.96−1.04);p=0.9

Hemorrhagic stroke 1.12(0.93−1.35);p=0.2

Adapted from The Lancet 2010.; 376:1670-81 **- CI 99%

Cardiovascular events per 39 mg/dL (1 mmol/L) reduction in LDL-C in 5 years: CTT

Primary Prevention

Secondary Prevention

Relative risk reduction

20% 20%

Absolute risk reduction

2% 5%

Events avoided per 1,000 (CI 95%)

25 (19-31) 48 (39-57)

NNT 50 20

Mean LDL-C 148 (118-190) mg/dL

Adapted from CTT Lancet 2005;366:1267-78

Risks in Medicine

nRelativeRisk:proportioncomparisonbetweengroupsnAbsoluteRisk:realrateofeventsinagivengroupnAttributablerisk:percentageofeventsinagivenpopulationthatiscausedbyagivengroupofindividuals

Howtocreateriskscores?

Andvalidateriskbiomarkers?

Howtocreateriskscores?

• Crosssectionalorretrospectiveanalyses– Identifypossibleriskbiomarkers

• Prospectivestudieswithmultivariateadjustments– Developamathematicalriskmodel

• Internalandexternalvalidation– Validationcohorts– Discrimination– Calibration

13

8.75.5

13.7

9.2

16.5

11.3

23.4

17.0

2.,827.7

38.036.8

57.556.4

120 160 160 160 160 160 160 mm Hg

Cholesterol

DiabetesSmokingLVH (EKG)

2202205050

------

2202205050

------

2592595050

------

2592593535

------

2592593535++----

2592593535++++-+

2592593535++++++

Kannel WB. JAMA 1996;275:1571-6

SBP

HDL-C

Estimated 10 yearrisk %

mg/dLmm Hg

ASCVD Risk Increases With AdditionOf Risk Factors: Framingham

Measures or relative riskCalibrationDiscriminationReclassification (for new markers)

Wilson. JAMA 2009;302:2369-70

Howtovalidateariskmarker?

HazardRatiosand95%CIforhardcardiovasculareventsin30years

Parameter Estimated30yearrisk

Observed30yearrisk

Malesex 1.72(1.44,2.05) 2.05(1.72,2.44)Age 2.08(1.88,2.31) 2.18(1.97,2.42)SBP 1.26(1.16,1.37) 1.28(1.19,1.39)Hypertension treatment 1.48(1.10,2.00) 1.36(1.14,1.62)Smoking 2.04(1.74,2.38) 2.74(2.32,3.24)Diabetesmellitus 2.42(1.77,3.31) 2.30(1.89,2.81)TotalCholesterol 1.32(1.22,1.43) 1.23(1.14,1.33)HDL-cholesterol 0.80(0.73,0.87) 0.75(0.68,0.81)BMI 1.10(1.00,1.20) 0.99(0.91,1.08)

Adapted from Pencina M. et al. Circulation 2009;119-3078-3084

Model calibration

How the calculated risk corresponds to the real risk?

Examplesofgoodandbadcalibration

Lloyd-Jones et al. Circulation. 2010;121:1768-1777

How well the model separates who and who will not have an event

Measured by ROC curves (C statistics )

Discrimination

ROCcurves,theirunderthecurveareasandcorrespondingoddsratios

Age, LDL , HDL, Blood pressure,Smoking Diabetes

Risk Factors orBiomarkers

Based on the paper by Pepe e. al. Am J Epidemiol 2004; 159:882-890.

True

pos

itive

rate

False positive rate0 0.2 0.4 0.6 0.8 1.0

1.0

0.8

0.6

0.4

0.2

0

OR=105; AUC=0.95OR=38; AUC=0.9

OR=11; AUC=0.8

OR=4; AUC=0.7

OR=2; AUC=0.6

OR=1; AUC=0.5

How many subjects change risk category ?

Reclassification

Reclassification

• NRI:“netreclassificationimprovement”• IDI:“integrateddiscriminationimprovement”

Helfand et al. Ann Intern Med. 2009;151:496-507

WhoisathighriskforASCVDalready?

Anddoesnotneedascore!

24

Whatthresholdforhighrisk?

• ATP-III– Highrisk=2%peryeartotalcardiovascularevents

• ACC/AHA2013– Highrisk=1.5%peryearofhardcardiovascularevents

• ESC/EAS2016– Highriskis1-2%peryearof CVDdeath– Veryhighrisk≥2%yearofCVDdeath

25

ACC/AHA20134highriskgroups=statins

• 1.ClinicalASCVD<75yearsofage*• 2.LDL≥190mg/dL (primarycause)>21yearsofage(FH)*

• 3.Individualsage40- 75yearswithdiabetesandLDL-C70-189mg/dL

• 4.IndividualswithoutclinicalASCVDordiabetesaged40-75yearswithLDL-C70-189mg/dL andestimatedriskASCVD≥7.5%*

StoneNJ,etal.JACC2013

*Highdosehighpotencystatins=Atorva40-80mgandRosuva20-40mg

27

these markers should be of interest in that group (class IIa, level ofevidence B). Cut-off values that should be used in considering thesemarkers as modifiers of total CV risk are CAC score .400 Agatstonunits, ABI ,0.9 or .1.40, aortic pulse wave velocity of 10 m/s andthe presence of plaques on carotid ultrasonography. Some factorssuch as a high HDL-C or apoA1 and a family history of longevitycan also reduce risk.

2.2 Risk levelsA total CV risk estimate is part of a continuum. The cut-off pointsthat are used to define high risk are in part arbitrary and based onthe risk levels at which benefit is evident in clinical trials. In clinicalpractice, consideration should be given to practical issues in relationto the local healthcare and health insurance systems. Not onlyshould those at high risk be identified and managed, but those atmoderate risk should also receive professional advice regarding life-style changes; in some cases drug therapy will be needed to controltheir plasma lipids.

In these subjects we realistically can

– prevent further increase in total CV risk,– increase awareness of the danger of CV risk,– improve risk communication and– promote primary prevention efforts.

Low-risk people should be given advice to help them maintain thisstatus. Thus the intensity of preventive actions should be tailored tothe patient’s total CV risk. The strongest driver of total CV risk isage, which can be considered as ‘exposure time’ to risk factors.This raises the issue that most older people in high-risk countrieswho smoke would be candidates for lipid-lowering drug treatmenteven if they have satisfactory blood pressure levels. The clinician isstrongly recommended to use clinical judgment in making

therapeutic decisions in older people, with a firm commitment toimplementing lifestyle measures such as smoking cessation in thefirst instance.

With these considerations one can propose the following levelsof total CV risk (Table 4).

2.2.1 Risk-based intervention strategiesTable 5 presents suggested intervention strategies as a function oftotal CV risk and low-density lipoprotein cholesterol (LDL-C) level.This graded approach is based on evidence from multiplemeta-analyses and individual RCTs, which show a consistent andgraded reduction in CVD risk in response to reductions in TCand LDL-C levels.61 – 71 These trials are consistent in showing thatthe higher the initial LDL-C level, the greater the absolute reductionin risk, while the relative risk reduction remains constant at any givenbaseline LDL-C level. Advice on individual drug treatments is givenin section 6.

Table 4 Risk categories

Very high-risk Subjects with any of the following:• Documented cardiovascular disease (CVD), clinical or unequivocal on imaging. Documented CVD includes previous myocardial infarction (MI), acute coronary syndrome (ACS), coronary revascularisation (percutaneous coronary intervention (PCI), coronary artery bypass graft surgery (CABG)) and other arterial revascularization procedures, stroke and transient ischaemic attack (TIA), and peripheral arterial disease (PAD). Unequivocally documented CVD on imaging is what has been shown to be strongly predisposed to clinical events, such as significant plaque on coronary angiography or carotid ultrasound. • DM with target organ damage such as proteinuria or with a major risk factor such as smoking, hypertension or dyslipidaemia.• Severe CKD (GFR <30 mL/min/1.73 m2).• A calculated SCORE ≥10% for 10-year risk of fatal CVD.

High-risk Subjects with:• Markedly elevated single risk factors, in particular cholesterol >8 mmol/L (>310 mg/dL) (e.g. in familial hypercholesterolaemia) or BP ≥180/110 mmHg. • Most other people with DM (some young people with type 1 diabetes may be at low or moderate risk).• Moderate CKD (GFR 30–59 mL/min/1.73 m2).• A calculated SCORE ≥5% and <10% for 10-year risk of fatal CVD.

Moderate-risk SCORE is ≥1% and <5% for 10-year risk of fatal CVD.

Low-risk SCORE <1% for 10-year risk of fatal CVD.

ACS ¼ acute coronary syndrome; AMI ¼ acute myocardial infarction; BP¼ bloodpressure; CKD¼ chronic kidney disease; DM¼ diabetes mellitus; GFR¼ glomerularfiltration rate; PAD¼ peripheral artery disease; SCORE ¼ systematic coronary riskestimation; TIA¼ transient ischaemic attack.

Box 6 Key messages

In apparently healthy persons, CVD risk is most frequently the result of multiple, interacting risk factors. This is the basis for total CV risk estimation and management.

men >40 years old and in women >50 years of age or post-menopausal.

A risk estimation system such as SCORE can assist in making logical management decisions, and may help to avoid both under- or over-treatment.

Certain individuals declare themselves to be at high or very high CVD risk without needing risk scoring and require immediate attention to all risk factors.

This is true for patients with documented CVD, diabetes or CKD.

All risk estimation systems are relatively crude and require attention to qualifying statements.

Additional factors affecting risk can be accommodated in electronic risk estimation systems such as HeartScore (www.heartscore.org).

with one risk factor, risk can still be reduced by trying harder with the others.

Risk factor screening including the lipid profile should be considered in

The total risk approach allows flexibility–if perfection cannot be achieved

ESC/EAS Guidelines 3013

RiskClassificationESC/EAS

Catapanoetal.EuropeanHeartJournal(2016)37,2999–3058

ExampleofSCOREFatalCVDRiskCalculator

28

Figure 6 Risk function without high-density lipoprotein-cholesterol (HDL-C) for women in populations at high cardiovascular disease risk, withexamples of the corresponding estimated risk when different levels of HDL-C are included.

Box 3 How to use the risk estimation charts

to the person’s blood pressure and TC. Risk estimates will need to be adjusted upwards as the person approaches the next age category.

Risk is initially assessed on the level of TC and systolic blood pressure before treatment, if known. The longer the treatment and the more effective it is, the greater the reduction in risk, but in general it will not be more than about one-third of the baseline risk. For example, for a person on antihypertensive drug treatment in whom the pre-treatment blood pressure is not known, if the total CV SCORE risk is 6%, then the pre-treatment total CV risk may have been 9%.

Low-risk persons should be offered advice to maintain their low-risk status. While no threshold is universally applicable, the intensity of advice should increase with increasing risk.

The charts may be used to give some indication of the effects of reducing risk factors, given that there will be a time lag before the risk reduces and that the results of randomized controlled trials in general give better

their cumulative risk.

To estimate a person’s 10-year risk of CVD death, find the table for his/her gender, smoking status, and age. Within the table find the cell nearest

estimates of benefits. In general, those who stop smoking rapidly halve

Box 4 Qualifiers

The charts can assist in risk assessment and management but must be interpreted in light of the clinician’s knowledge and experience and of the patient’s pre-test likelihood of CVD.

Risk will be overestimated in countries with a decreasing CVD mortality, and underestimated in countries in which mortality is increasing. This is dealt with by recalibration (www.heartscore.org).

Risk estimates appear lower in women than in men. However, risk is only deferred in women; the risk of a 60-year-old woman is similar to that of a 50-year-old man. Ultimately more women die from CVD than men.

Relative risks may be unexpectedly high in young persons, even if absolute risk levels are low. The relative risk chart (Figure 4 ) and the estimated risk age (Figure 5 ) may be helpful in identifying and counselling such persons.

ESC/EAS Guidelines 3011

Catapanoetal.EuropeanHeartJournal(2016)37,2999–3058

ACC/AHARiskEstimator

29http://tools.acc.org/ASCVD-Risk-Estimator-Plus/#!/calculate/estimate/

FamilyMatters!

30

http://www.reynoldsriskscore.org

Whatarethelimitationsofriskscores?

• Chronologicalagedependent– Younghighriskindividualsnotdetected

• Donotconsiderindividualsusceptibility– Biologicalvs.chronologicalage

• Usuallycalculateshorttermrisk– 5or10years

• Notmeasureimpactofextremeriskfactorvalues• Needtobecalibratedfordifferentpopulations

– E.g.- Oman,Braziletc…

31

Adapted from Furberg C.

Basis of Atherosclerosis Prevention

% net correctreclassification

NRI

FRS + Carotid IMTEventsNon Events

3.32.7

0.06

FRS + CACEventsNon Events

10.636

0.466

FRS +ABIEventsNon Events

4.12.7

0.068

FRS + CRPEventsNon Events

1.62.1

0.037

FRS + Family HistoryEventsNon Events

0.83.2

0.040

33

ORIGINAL CONTRIBUTION

Comparison of Novel Risk Markersfor Improvement in Cardiovascular RiskAssessment in Intermediate-Risk IndividualsJoseph Yeboah, MD, MSRobyn L. McClelland, PhDTamar S. Polonsky, MD, MSCIGregory L. Burke, MD, MSChristopher T. Sibley, MDDaniel O’Leary, MDJeffery J. Carr, MD, MScDavid C. Goff Jr, MD, PhDPhilip Greenland, MDDavid M. Herrington, MD, MHS

CURRENT TRENDS IN PRIMARYprevention of cardiovasculardisease (CVD) emphasize theneed to treat individuals based

on their global cardiovascular risk.1 Ac-cordingly, practice guidelines recom-mend approaches to classify individu-als as high, intermediate, or low riskusing the Framingham Risk Score (FRS)or other similar CVD risk predictionmodels.2,3 However, there is increasingrecognition of the imprecision of theseclassifications such that the intermediate-risk group actually represents a compos-ite of higher-risk individuals for whommore aggressive (ie, drug) therapy mightbe indicated.The intermediate-riskgroupalso contains lower-risk individuals inwhom CVD might be managed with life-style measures alone. This recognitionhas motivated researchers to identifymarkers that could offer greater discrimi-nation of higher- and lower-risk pa-tients within the intermediate-risk group.

Risk markers that have shown prom-ise in improving risk discrimination in-clude carotid intima–media thickness(CIMT), coronary artery calcium (CAC)

Author Affiliations: Departments of Internal Medicine/Cardiology (DrsYeboahandHerrington),andRadiology(DrCarr),andDivisionofPublicHealthSciences(DrsBurkeand Carr), Wake Forest University School of Medicine,Winston-Salem, North Carolina; Department of Biosta-tistics,UniversityofWashington,Seattle (DrMcClelland);SectionofCardiology,Departmentof InternalMedicine,University of Chicago, Chicago, Illinois (Dr Polonsky);National Institutes of Health, Bethesda, Maryland (Dr

Sibley); Tufts Medical Center, Brookline, Massachusetts(Dr O’Leary); University of Colorado School of PublicHealth, Aurora (Dr Goff ); and Department of Preven-tiveMedicine,NorthwesternUniversityFeinbergSchoolof Medicine, Chicago, Illinois (Dr Greenland).Corresponding Author: Joseph Yeboah, MD, MS, De-partment of Internal Medicine/Cardiology, Wake For-est Baptist Health, Medical Center Boulevard, Winston-Salem, NC 27157 (jyeboah@wakehealth.edu).

Context Risk markers including coronary artery calcium, carotid intima–media thick-ness, ankle-brachial index, brachial flow–mediated dilation, high-sensitivity C-reactive protein (CRP), and family history of coronary heart disease (CHD) have beenreported to improve on the Framingham Risk Score (FRS) for prediction of CHD, butthere are no direct comparisons of these markers for risk prediction in a single cohort.

Objective We compared improvement in prediction of incident CHD/cardiovascular disease (CVD) of these 6 risk markers within intermediate-risk partici-pants (FRS !5%-"20%) in the Multi-Ethnic Study of Atherosclerosis (MESA).

Design, Setting, and Participants Of 6814 MESA participants from 6 US fieldcenters, 1330 were intermediate risk, without diabetes mellitus, and had complete dataon all 6 markers. Recruitment spanned July 2000 to September 2002, with follow-upthrough May 2011. Probability-weighted Cox proportional hazard models were usedto estimate hazard ratios (HRs). Area under the receiver operator characteristic curve(AUC) and net reclassification improvement were used to compare incremental con-tributions of each marker when added to the FRS, plus race/ethnicity.

Main Outcome Measures Incident CHD defined as myocardial infarction, anginafollowed by revascularization, resuscitated cardiac arrest, or CHD death. Incident CVDadditionally included stroke or CVD death.

Results After 7.6-year median follow-up (IQR, 7.3-7.8), 94 CHD and 123 CVD eventsoccurred. Coronary artery calcium, ankle-brachial index, high-sensitivity CRP, and fam-ily history were independently associated with incident CHD in multivariable analyses(HR, 2.60 [95% CI, 1.94-3.50]; HR, 0.79 [95% CI, 0.66-0.95]; HR, 1.28 [95% CI,1.00-1.64]; and HR, 2.18 [95% CI, 1.38-3.42], respectively). Carotid intima–mediathickness and brachial flow–mediated dilation were not associated with incident CHDin multivariable analyses (HR, 1.17 [95% CI, 0.95-1.45] and HR, 0.95 [95% CI, 0.78-1.14]). Although addition of the markers individually to the FRS plus race/ethnicityimproved AUC, coronary artery calcium afforded the highest increment (0.623 vs 0.784),while brachial flow–mediated dilation had the least (0.623 vs 0.639). For incident CHD,the net reclassification improvement with coronary artery calcium was 0.659, brachialflow–mediated dilation was 0.024, ankle-brachial index was 0.036, carotid intima–media thickness was 0.102, family history was 0.160 and high-sensitivity CRP was 0.079.Similar results were obtained for incident CVD.

Conclusions Coronary artery calcium, ankle-brachial index, high-sensitivity CRP, andfamily history were independent predictors of incident CHD/CVD in intermediate-riskindividuals. Coronary artery calcium provided superior discrimination and risk reclas-sification compared with other risk markers.JAMA. 2012;308(8):788-795 www.jama.com

For editorial comment see p 816.

788 JAMA, August 22/29, 2012—Vol 308, No. 8 ©2012 American Medical Association. All rights reserved.

Downloaded From: http://jama.jamanetwork.com/ by a Universidade de São Paulo User on 03/06/2013

Adapted from Yeboah et al. JAMA. 2012;308:788-795

Why use scores for secondary prevention?Cost-effectiveness

Risk/Benefit

FOURIER: Primary Outcome

Sabatine et al. N Engl J Med. 2017;376(18):1713-1722Sabatine MS et al. Am Heart J 2016;173:94-101

Primary efficacy endpoint: Cardiovascular death, Myocardial infarction, stroke, hospitalization for unstable angina, or coronary revascularization

incremental cost-effectiveness ratio (ICER; incremental healthcare costs per quality-adjusted life-year [QALY] gained). Thesecondary outcome was the drug cost at which PCSK9 inhibi-tors would become cost-effective at a willingness-to-paythreshold of $100 000/QALY.

The simulation cohort in this update approximated theFOURIER inclusion criteria (US adults aged 40-80 years withASCVD and LDL-C ≥70 mg/dL [to convert to mmol/L, multi-ply by 0.0259] despite statin therapy, based on 2005-2012 Na-tional Health and Nutrition Examination Surveys [NHANES]).3

Reductions in myocardial infarction and stroke risk were es-timated from FOURIER, with separate hazard ratios for the firstyear and subsequent years to account for the increasing effec-tiveness over time observed in the trial (Table 1). Drug costswere based on current wholesale acquisition costs ($3818 forezetimibe [32% increase between 2015 and 2017] and $14 542for PCSK9 inhibitors [1% increase between 2015 and 2017])4;all other health care costs were inflated to 2017 US dollars.Because PCSK9 inhibitors did not reduce risk of cardiovascu-lar death in FOURIER, we conducted an additional analysis withno effect on cardiovascular death except as a direct result oflowering myocardial infarction or stroke risk. Table 1 com-pares the approach in the prior analysis and this update.

The CVDPM is programmed in Fortran 95 (Lahey Com-puter Systems). Outcomes were analyzed using QuickBasic 64and Excel 2011 (Microsoft); statistical analyses were per-formed using SAS (SAS Institute), version 9.4, and Stata(StataCorp), version 13.

Results | Approximately 8.9 million US adults meet the age,ASCVD, LDL-C, and statin treatment criteria of FOURIER. Basedon NHANES, this population is 61% men (95% CI, 55%-67%),

with a mean age of 66 years (95% CI, 65-68), mean LDL-C of104 mg/dL (95% CI, 100-108), and diabetes proportion of 33%(95% CI, 28%-39%). The CVDPM accurately reproducedFOURIER MACE rates (statin only: FOURIER estimate, 3.7% inyear 1, 3.7% in year 2; CVDPM, 3.6% in year 1, 3.8% in year 2;statin plus PCSK9 inhibitors: FOURIER estimate, 3.1% inyear 1, 2.7% in year 2; CVDPM, 3.0% in year 1, 2.7% in year 2).Adding PCSK9 inhibitors to statins was estimated to prevent2 893 500 more MACE compared with adding ezetimibe, at anICER of $450 000/QALY (80% uncertainty interval, $301 000-$787 000) (Table 2). Reducing annual drug costs by 71%(to ≤$4215) would be needed for PCSK9 inhibitors to be cost-effective at a threshold of $100 000/QALY. Assuming no di-rect effect on cardiovascular death as observed in FOURIER,the ICER increased to $1 795 000/QALY.

Discussion | PCSK9 inhibitor use in patients with ASVCD was notcost-effective at 2017 prices, and these updated analysesbased on FOURIER estimates suggest that even greater pricereductions than previously reported are required to meetcost-effectiveness thresholds. The 71% price reductionrequired to make PCSK9 inhibitor therapy cost-effective isgreater than the 25% to 30% discounts typically offered bymanufacturers.5 A rebate model proposed by 1 PCSK9 inhibi-tor manufacturer6 (refunding the drug costs if patients expe-rience MACE while receiving PCSK9 inhibitor therapy) is alsounlikely to meaningfully reduce drug expenditures giventhe low overall MACE rate (approximately 3% per year).Although computer simulations that synthesize data fromobservational studies and clinical trials may not preciselyreflect clinical effectiveness that may be observed in practiceover time, these updated results continue to demonstrate

Table 2. Clinical and Economic Outcomes of Treatment Strategies in ASCVDa

Statin + Ezetimibe Relative to Statin Alone,Difference (80% Uncertainty Interval)

Statin + PCSK9 Inhibitor Relative to Statin +Ezetimibe, Difference (80% Uncertainty Interval)

Total MACE avertedb 2 164 000 (1 305 300 to 2 913 100) 2 893 500 (1 647 600 to 4 295 800)

NNT, No. (80% uncertainty interval)c 41 (30 to 67) 37 (25 to 65)d

Life-years gained 4 849 000 (2 924 100 to 6 491 900) 6 087 500 (3 390 400 to 9 081 200)

QALYs gained 4 423 700 (2 661 900 to 5 938 100) 5 558 400 (3 085 600 to 8 333 700)

Incremental costs, $ millionse

Drugs 870 084 (866 573 to 873 118) 2 485 684 (2 470 148 to 2 501 282)

Cardiovascular care −85 540 (−115 905 to −51 262) −109 478 (−162 994 to −60 892)

Noncardiovascular caref 97 002 (58 462 to 129 960) 123 415 (69 214 to 184 453)

Incremental cost-effectiveness ratio

Per life-year gained 182 000 (137 000 to 299 000) 411 000 (277 000 to 721 000)

Per QALY gained (primary outcome) 199 000 (150 000 to 328 000) 450 000 (301 000-787 000)g

Abbreviations: ASCVD, atherosclerotic cardiovascular disease; MACE, majoradverse cardiovascular events; NNT, number needed to treat; PCSK9,proprotein convertase subtilisin/kexin type 9; QALY, quality-adjusted life-year.a The model assumed the health system perspective and a lifetime analytic

horizon, and discounted future costs and QALYs at 3% a year. To reflect theprecision of the model, MACE and QALYs are rounded to the 100s; costs arerounded to the millions; and incremental cost-effectiveness ratios to the1000s. This analysis included patients with a history of ASCVD andlow-density lipoprotein cholesterol of 70 mg/dL or more taking statin therapy(n = 8 947 000 in 2015).

b MACE was defined as a composite of nonfatal myocardial infarction, nonfatalstroke, and death from cardiovascular causes.

c No. of patients that would need to be treated for 5 years to avert 1 MACE.d This is the number of patients that would have to be treated for 5 years with

statin + PCSK9 inhibitor compared with statin + ezetimibe to avoid 1 MACE.For context, 20 patients would have to be treated for 5 years withstatin + PCSK9 inhibitor compared with statin alone to avoid 1 MACE.

e All costs are reported in 2017 US dollars.f Noncardiovascular costs include age-specific background health care costs

(ie, health care costs unrelated to management of cardiovascular disease).g For reference purposes, the incremental cost-effectiveness ratio of the

statins + PCSK9 inhibitor group relative to statin therapy was $339 000/QALY(80% uncertainty interval, $284 000 to $430 000).

Letters

jama.com (Reprinted) JAMA August 22/29, 2017 Volume 318, Number 8 749

© 2017 American Medical Association. All rights reserved.

Downloaded From: by a USP User on 01/26/2018

BenefitofEvoMabBasedonTimefromQualifyingMI

QualifyingMI<2yrsago

MonthsafterRandomization

CVDeath,M

I,orStroke

0 6 12 18 24 30 36

24%RRR

HR 0.76(95% CI 0.64-0.89)

P<0.001 7.9%

10.8%

Pinteraction=0.18

D 2.9%NNT 35

Evolocumab

Placebo

8.3%

9.3%

D 1.0%NNT 101

QualifyingMI≥2yrsago

13%RRR

HR 0.87(95% CI 0.76-0.99)

P=0.04

0 6 12 18 24 30 36

SabatineMSAHA2017

TheTIMIRiskScoreForSecondaryPrevention:IMPROVE-ITStudy

38component endpoints (Figures 2A to 2C) (p trend<0.0001 for each endpoint).

The chi-square value for goodness-of-fit was 4.5(p ¼ 0.48) for the comparison of annualized rates ofCV death, MI, or ischemic stroke in placebo-treatedpatients from TRA 2"P and the control arm (placeboand simvastatin) from IMPROVE-IT, thereby indi-cating adequate calibration of the integer-basedapproach (Online Figure 2). The c-statistic for the9-component multivariable model for CV death, MI,or ischemic stroke was 0.67 (95% CI: 0.65 to 0.68) inthe patients randomized to placebo and simvastatin,consistent with the derivation data set (c-statistic:0.67; 95% CI: 0.65 to 0.69) (11).

TREATMENT EFFECT OF EZETIMIBE ON RECURRENT

CARDIOVASCULAR EVENTS BY ATHEROTHROMBOTIC

RISK CATEGORY. Risk categories, defined as low (0 to 1risk indicators), intermediate (2 indicators), and high($3 indicators), represented 45% (n ¼ 8,032),

30% (n ¼ 5,292), and 25% (n ¼ 4,393) of the overallpopulation, respectively (Table 1). The index ACSeventwas non–ST-segment elevationMI in 47% to 48%of patients in all risk categories, with higher rates ofST-segment elevation and lower rates of unstableangina in lower-risk patients. There was a higher rateof previous lipid-lowering therapy with increasing riskand correspondingly lower baseline LDL-C at the timeof the index ACS event. Adherence to guideline-basedtherapies was high across all risk categories, with 95%to 98% of randomized patients receiving aspirin, 86%to 88% receiving a beta-blocker, and 82% to 90% athienopyridine at the time of randomization, whichwas a median of 5.0 days after the index ACS event inall risk categories. Baseline characteristics were wellmatched (p > 0.05) by randomization in patientsallocated to placebo in addition to simvastatin andezetimibe in addition to simvastatin, with the excep-tion of clinically minimal differences in previous MI(placebo and simvastatin 13% vs. ezetimibe and

FIGURE 1 Risk Stratification of CV Death, MI, or Ischemic Stroke in the Control Arm (Placebo/Simvastatin)

01070

1279

Cum

ulat

ive In

ciden

ce o

f CV

Deat

h, M

I or I

sche

mic

Stro

ke at

7 Yr

p trend < 0.0001

# Risk IndicatorsAt Risk

% PopulationSimva Events

80%

70%

60%

50%

40%

30%

20%

10%

0%1

295733381

22642

30471

31418

16377

4534

6200

≥5248

2128

8.6%

14.7%

21.5%

33.1%

48.7%

68.4%CHF

HTN

Age ≥75

DM

Prior Stroke

Prior CABG

PAD

eGFR <60

Current Smoking

TRS 2°P RiskIndicators

The 7-year Kaplan-Meier estimates are shown. The basis of the p value is the chi-square test for trend. CABG ¼ coronary artery bypass graft;CHF ¼ congestive heart failure; CV ¼ cardiovascular; DM ¼ diabetes mellitus; eGFR ¼ estimated glomerular filtration rate;HTN ¼ hypertension; MI ¼ myocardial infarction; PAD ¼ peripheral artery disease; Simva ¼ simvastatin; TRS 2"P ¼ TIMI (Thrombolysis InMyocardial Infarction) Risk Score for Secondary Prevention.

Bohula et al. J A C C V O L . 6 9 , N O . 8 , 2 0 1 7

Risk Stratification and Ezetimibe in the IMPROVE-IT Trial F E B R U A R Y 2 8 , 2 0 1 7 : 9 1 1 – 2 1

916

BohulaEAetal.JAmCollCardiol.2017;69(8):911-921

• Atherosclerosisisamultifactorialdisease• Riskvariesfrompersontoperson• Riskscoreshelpidentifyhigherriskindividuals• Riskscoresarenotperfect• Otherbiomarkerscanhelpidentifyrisk• ASCVDriskmustbeestimatedtoimplementcost/effectivepharmacologicaltherapy

Conclusions

39