CVD risk assessment using risk scores in primary and secondary prevention

Transcription

1 CVD risk assessment using risk scores in primary and secondary prevention Raul D. Santos MD, PhD Heart Institute-InCor University of Sao Paulo Brazil

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3 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

4 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

5 Why do we need to stratify ASCVD risk?

6 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

7 Blood cholesterol and vascular mortality by age, sex and blood pressure: a meta-analysis of individual data from 61 prospective studies with vascular deaths Lancet 2007; 370:

8 N=900,000 Lancet 2007; 370:

9 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 ( ), p<0.0001** 0.80 ( ); p<0.0001** 0.89 ( ); p=0.002** 0.96 ( ); p= ( ); p< ( ); p< ( ); p< ( ); p< ( ); p= ( ); p=0.2 Adapted from The Lancet 2010.; 376: **- CI 99%

10 Cardiovascular events per 39 mg/dl (1 mmol/l) reduction in LDL-C in 5 years: CTT Mean LDL-C 148 ( ) 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 Adapted from CTT Lancet 2005;366:

11 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

12 How to create risk scores? And validate risk biomarkers?

13 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

14 ASCVD Risk Increases With Addition Of Risk Factors: Framingham Estimated 10 year risk % , SBP Cholesterol HDL-C Diabetes Smoking LVH (EKG) mm Hg mg/dl mm Hg Kannel WB. JAMA 1996;275:1571-6

15 How to validate a risk marker? Measures or relative risk Calibration Discrimination Reclassification (for new markers) Wilson. JAMA 2009;302:

16 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;

17 Model calibration How the calculated risk corresponds to the real risk?

18 Examples of good and bad calibration Lloyd-Jones et al. Circulation. 2010;121:

19 Discrimination How well the model separates who and who will not have an event Measured by ROC curves (C statistics )

20 ROC curves, their under the curve areas and corresponding odds ratios 1.0 OR=105; AUC=0.95 OR=38; AUC= OR=11; AUC=0.8 True positive rate 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 False positive rate Based on the paper by Pepe e. al. Am J Epidemiol 2004; 159:

21 Reclassification How many subjects change risk category?

22 Reclassification NRI: net reclassification improvement IDI: integrated discrimination improvement

23 Helfand et al. Ann Intern Med. 2009;151:

24 Who is at high risk for ASCVD already? And does not need a score! 24

25 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

26 ACC/AHA 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 years with diabetes and LDL-C mg/dl 4. Individuals without clinical ASCVD or diabetes aged years with LDL-C mg/dl and estimated risk ASCVD 7.5% * Stone NJ, et al. JACC 2013 * High dose high potency statins = Atorva 40-80mg and Rosuva mg

27 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 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,

28 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,

29 ACC/AHA Risk Estimator 29

30 Family Matters! 30

31 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

32 Adapted from Furberg C. Basis of Atherosclerosis Prevention

33 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 NRI FRS + Family History Events Non Events Adapted from Yeboah et al. JAMA. 2012;308:

34 Why use scores for secondary prevention? Cost-effectiveness Risk/Benefit

35 FOURIER: Primary Outcome Sabatine et al. N Engl J Med. 2017;376(18): Sabatine MS et al. Am Heart J 2016;173: Primary efficacy endpoint: Cardiovascular death, Myocardial infarction, stroke, hospitalization for unstable angina, or coronary revascularization

36 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 ( to ) ( to ) NNT, No. (80% uncertainty interval) c 41 (30 to 67) 37 (25 to 65) d Life-years gained ( to ) ( to ) QALYs gained ( to ) ( to ) Incremental costs, $ millions e Drugs ( to ) ( to ) Cardiovascular care ( to ) ( to ) Noncardiovascular care f ( to ) ( to ) Incremental cost-effectiveness ratio Per life-year gained ( to ) ( to ) Per QALY gained (primary outcome) ( to ) ( ) 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.

37 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 ) NNT 35 P< % Placebo Evolocumab HR 0.87 (95% CI ) P= % 8.3% D 1.0% NNT 101 P interaction = Months after Randomization Sabatine MS AHA 2017

38 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 < % 33.1% 48.7% 68.4% 0% # Risk Indicators At Risk % Population Simva Events 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):

39 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