CVD risk assessment using risk scores in primary and secondary prevention Raul D. Santos MD, PhD Heart Institute-InCor University of Sao Paulo Brazil
Disclosure Honoraria for consulting and speaker activities on the last year from Amgen, Astra Zeneca, Akcea Biolab, Merck, Novo-Nordisk Pfizer, Kowa Sanofi/Regeneron 3
Atherosclerotic cardiovascular disease risk stratification Why do we need to stratify ASCVD risk? What is high risk? Thresholds based on cost/effectiveness How scores are made? How to validate a risk biomarker External validity (calibration) Limitations and new biomarkers 4
Why do we need to stratify ASCVD risk?
ASCVD First cause of death in the world Multifactorial disease Heterogeneity in risk Individuals with the same risk factors may or not have events Pharmacological treatments Cost/effectiveness Risk/Benefits 6
Blood cholesterol and vascular mortality by age, sex and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55 000 vascular deaths Lancet 2007; 370: 1829-39
N=900,000 Lancet 2007; 370: 1829 39
Impact of 1mmol/L reduction in LDL-C upon major cardiovascular events and mortality CTT 2010 Relative Risk (95% CI) All cause mortality CHD mortality Other cardiac deaths Stroke deaths Major vascular events Non-fatal MI Myocardial revascularization Ischemic stroke Cancer incidence Hemorrhagic stroke 0.90 (0.87-0.93), p<0.0001** 0.80 (0.74 0.87); p<0.0001** 0.89 (0.81 0.98); p=0.002** 0.96 (0.84 1.09); p=0.5 0.78 (0 76 0 80); p<0.0001 0.73 (0.70 0.77); p<0.0001 0.75 (0.72 0.78); p<0.0001 0.79 (0.74 0.85); p<0.0001 1.00 (0.96 1.04); p=0.9 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 Mean LDL-C 148 (118-190) mg/dl Relative risk reduction Primary Prevention 20% 20% Secondary Prevention Absolute risk reduction Events avoided per 1,000 (CI 95%) 2% 5% 25 (19-31) 48 (39-57) NNT 50 20 Adapted from CTT Lancet 2005;366:1267-78
Risks in Medicine nrelative Risk : proportion comparison between groups nabsolute Risk : real rate of events in a given group nattributable risk : percentage of events in a given population that is caused by a given group of individuals
How to create risk scores? And validate risk biomarkers?
How to create risk scores? Cross sectional or retrospective analyses Identify possible risk biomarkers Prospective studies with multivariate adjustments Develop a mathematical risk model Internal and external validation Validation cohorts Discrimination Calibration 13
ASCVD Risk Increases With Addition Of Risk Factors: Framingham 57.5 56.4 38.0 36.8 Estimated 10 year risk % 23.4 2.,8 27.7 8.7 5.5 13.7 9.2 16.5 11.3 17.0 SBP Cholesterol HDL-C Diabetes Smoking LVH (EKG) 120 160 160 160 160 160 160 mm Hg 220 50 - - - 220 50 - - - 259 50 - - - 259 35 - - - 259 35 + - - 259 35 + + -+ 259 35 + + + mg/dl mm Hg Kannel WB. JAMA 1996;275:1571-6
How to validate a risk marker? Measures or relative risk Calibration Discrimination Reclassification (for new markers) Wilson. JAMA 2009;302:2369-70
Hazard Ratios and 95% CI for hard cardiovascular events in 30 years Parameter Estimated 30 year risk Observed 30 year risk Male sex 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) Diabetes mellitus 2.42 (1.77, 3.31) 2.30 (1.89, 2.81) Total Cholesterol 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?
Examples of good and bad calibration Lloyd-Jones et al. Circulation. 2010;121:1768-1777
Discrimination How well the model separates who and who will not have an event Measured by ROC curves (C statistics )
ROC curves, their under the curve areas and corresponding odds ratios 1.0 OR=105; AUC=0.95 OR=38; AUC=0.9 0.8 OR=11; AUC=0.8 True positive rate 0.6 0.4 OR=4; AUC=0.7 OR=2; AUC=0.6 OR=1; AUC=0.5 Age, LDL, HDL, Blood pressure, Smoking Diabetes Risk Factors or Biomarkers 0.2 0 0 0.2 0.4 0.6 0.8 1.0 False positive rate Based on the paper by Pepe e. al. Am J Epidemiol 2004; 159:882-890.
Reclassification How many subjects change risk category?
Reclassification NRI: net reclassification improvement IDI: integrated discrimination improvement
Helfand et al. Ann Intern Med. 2009;151:496-507
Who is at high risk for ASCVD already? And does not need a score! 24
What threshold for high risk? ATP-III High risk = 2% per year total cardiovascular events ACC/AHA 2013 High risk= 1.5% per year of hard cardiovascular events ESC/EAS 2016 High risk is 1-2% per year of CVD death Very high risk 2% year of CVD death 25
ACC/AHA 2013 4 high risk groups= statins 1. Clinical ASCVD < 75 years of age * 2. LDL 190 mg/dl (primary cause) > 21 years of age (FH) * 3. Individuals age 40-75 years with diabetes and LDL-C 70-189 mg/dl 4. Individuals without clinical ASCVD or diabetes aged 40-75 years with LDL-C 70-189 mg/dl and estimated risk ASCVD 7.5% * Stone NJ, et al. JACC 2013 * High dose high potency statins = Atorva 40-80mg and Rosuva 20-40 mg
Table 4 Risk categories Risk Classification ESC/EAS Very high-risk High-risk Moderate-risk Low-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 m 2 ). A calculated SCORE 10% for 10-year risk of fatal CVD. 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 m 2 ). A calculated SCORE 5% and <10% for 10-year risk of fatal CVD. SCORE is 1% and <5% for 10-year risk of fatal CVD. SCORE <1% for 10-year risk of fatal CVD. Catapano et al. European Heart Journal (2016) 37, 2999 3058 27
Example of SCORE Fatal CVD Risk Calculator Figure 6 Risk function without high-density lipoprotein-cholesterol (HDL-C) for women in populations at high cardiovascular disease risk, with examples of the corresponding estimated risk when different levels of HDL-C are included. Catapano et al. European Heart Journal (2016) 37, 2999 3058 28
ACC/AHA Risk Estimator http://tools.acc.org/ascvd-risk-estimator-plus/#!/calculate/estimate/ 29
Family Matters! http://www.reynoldsriskscore.org 30
What are the limitations of risk scores? Chronological age dependent Young high risk individuals not detected Do not consider individual susceptibility Biological vs. chronological age Usually calculate short term risk 5 or 10 years Not measure impact of extreme risk factor values Need to be calibrated for different populations E.g.- Oman, Brazil etc 31
Adapted from Furberg C. Basis of Atherosclerosis Prevention
Comparison of Novel Risk Markers for Improvement in Cardiovascular Risk Assessment in Intermediate-Risk Individuals FRS + Carotid IMT Events Non Events FRS + CAC Events Non Events FRS +ABI Events Non Events FRS + CRP Events Non Events % net correct reclassification 3.3 2.7 10.6 36 4.1 2.7 1.6 2.1 NRI 0.06 0.466 0.068 0.037 FRS + Family History Events Non Events 0.8 3.2 Adapted from Yeboah et al. JAMA. 2012;308:788-795 0.040 33
Why use scores for secondary prevention? Cost-effectiveness Risk/Benefit
FOURIER: Primary Outcome Sabatine et al. N Engl J Med. 2017;376(18):1713-1722 Sabatine 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
Table 2. Clinical and Economic Outcomes of Treatment Strategies in ASCVD a Statin + Ezetimibe Relative to Statin Alone, Difference (80% Uncertainty Interval) Statin + PCSK9 Inhibitor Relative to Statin + Ezetimibe, Difference (80% Uncertainty Interval) Total MACE averted b 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, $ millions e 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 care f 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, major c No. of patients that would need to be treated for 5 years to avert 1 MACE.
Benefit of EvoMab Based on Time from Qualifying MI Qualifying MI <2 yrs ago Qualifying MI 2 yrs ago 24% RRR 10.8% 13% RRR CV Death, MI, or Stroke HR 0.76 D 2.9% (95% CI 0.64-0.89) NNT 35 P<0.001 7.9% Placebo Evolocumab HR 0.87 (95% CI 0.76-0.99) P=0.04 9.3% 8.3% D 1.0% NNT 101 P interaction =0.18 0 6 12 18 24 30 36 0 6 12 18 24 30 36 Months after Randomization Sabatine MS AHA 2017
The TIMI Risk Score For Secondary Prevention: IMPROVE-IT Study FIGURE 1 Risk Stratification of CV Death, MI, or Ischemic Stroke in thecontrolarm(placebo/simvastatin) 80% Cumulative Incidence of CV Death, MI or Ischemic Stroke at 7 Yr 70% 60% 50% 40% 30% 20% 10% 8.6% TRS 2 P Risk Indicators CHF HTN Age 75 DM Prior Stroke Prior CABG PAD egfr <60 Current Smoking 14.7% p trend < 0.0001 21.5% 33.1% 48.7% 68.4% 0% # Risk Indicators 0 1 2 3 4 5 At Risk 1070 2957 2642 1418 534 248 % Population 12 33 30 16 6 2 Simva Events 79 381 471 377 200 128 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 In Myocardial Infarction) Risk Score for Secondary Prevention. Bohula EA et al. J Am Coll Cardiol. 2017;69(8):911-921 38
Atherosclerosis is a multifactorial disease Risk varies from person to person Risk scores help identify higher risk individuals Risk scores are not perfect Conclusions Other biomarkers can help identify risk ASCVD risk must be estimated to implement cost/effective pharmacological therapy 39