HIV and Cardiovascular Disease: Epidemiology, Mechanisms, and Clinical Implications

Transcription

1 HIV and Cardiovascular Disease: Epidemiology, Mechanisms, and Clinical Implications Matthew J. Feinstein, MD, MSc Assistant Professor of Medicine Northwestern University Feinberg School of Medicine Chicago, Illinois Learning Objectives After attending this presentation, learners will be able to: Describe the changing epidemiology of HIV and cardiovascular disease (CVD) Define mechanisms underlying the associations of HIV with myocardial infarction and heart failure Outline clinical approaches to CVD prevention and lipid management for people living with HIV Slide 3 of 42 Outline What: Epidemiology of HIV & CVD Why: What we know, what we think, and what we don t know about - HIV, atherosclerosis, and thrombosis - HIV, cardiac dysfunction, and heart failure How can we prevent and treat CVD in HIV? - Risk assessment - Therapies: Old, New, and Future Slide 4 of 42

2 CVD Mortality (Proportion of Total), USA: GENERAL POPULATION HIV+ Slide 5 of 42 Feinstein MJ, et al. Am J Cardiol. 2016;115(12): HIV, Atherosclerosis, and Thrombosis: Myocardial Infarction and Stroke ARS Question: Myocardial Infarction in HIV After adjusting for demographics and traditional CVD risk factors, how does the risk for MI compare for HIV+ vs. uninfected people? 1. Same % Lower for HIV % Higher for HIV % Higher for HIV+ 5. >200% Higher for HIV+ Slide 7 of 42

3 HIV and Myocardial Infarction Slide 8 of 42 Triant V., et al. J Clin Endocrinol Metab 2007;92(7): Currier, J. S. Top HIV Med 2009;17(3): HIV and Myocardial Infarction Hazard Ratio of MI (vs. HIV-) HIV VL (Time-updated, copies/ml) <500: 1.39 ( ) 500: 1.75 ( ) CD4 (Time-updated, cells/mm 3 ) 200: 1.43 ( ) <200: 1.88 ( ) Slide 9 of 42 Freiberg MS, et al. JAMA Intern Med 2013;173: Figure Adapted from: Paisible AL, et al. J Acquir Immune Defic Syndr 2015;68: HIV and Ischemic Stroke PARTNERS - N= Mean f/u: 6 yrs - Adjusted for demographics and stroke risk factors Slide 10 of 42 Chow FC, et al. J Acquir Immune Defic Syndr. 2012;60(4):

4 HIV and Subclinical Atherosclerosis 759 men the Multicenter AIDS Cohort Study (MACS): 2/3 HIV+, 1/3 HIV- controls - Noncalcifiedplaque and coronary calcium (less so) significantly more common in HIV *Greater atherosclerotic burden in HIV vs. uninfected controls reproduced (C-IMT, CT) in ~20 other studies with consistent effect sizes Slide 11 of 42 Post, W.S., et al. Ann Intern Med 2014; 160(7): HIV and Subclinical Atherosclerosis CONTROL (HIV-) HIV+ FDG-PET CT - Images arterial inflammation - Macrophages uptake FDG during early atheroma formation - Aortic FDG PET uptake for HIV+ > FRS-matched controls Slide 12 of 42 Subramanian S, et al. Arterial inflammation in patients with HIV. JAMA 2012;308: Mechanisms of Athero-Thrombosis & MI Slide 13 of 42 Figure adapted from: (1) Hsue PY, et al. J infect dis. 2012;205 Suppl 3:S ; (2) Currier JS. Top HIV Med 2009;17(3): ; (3) Post WS, et al. Ann Intern Med. 2014;160:

5 HIV and Atherosclerosis: Independent of Meds HIV+ Elite Controllers (N=33) vs. Uninfected Controls (N=92): - Significantly greater C-IMT in general, when stratifying by CVD RFs (left), and in multivariable regression: C-IMT 0.16 mm greater for ECs (p=0.005) after adjustment for age, sex, glucose, HTN, smoking, LDL, TG, statin use, FHx CAD Slide 14 of 42 Hsue PY, et al. AIDS. 2009; 23: ART is Clearly Better than No Treatment MI Rate (over 2700 person-years follow-up): - Interrupted ART: 1.3 per 100 person-years - Uninterrupted ART: 0.8 per 100 person/years Slide 15 of 42 HIV and Myocardial Dysfunction: Heart Failure

6 HIV and Heart Failure: VACS *Adjustment for age, race/ethnicity, HTN, lipid levels, smoking, diabetes, statin use, depression, Afib, HCV, substance use **HF definition based on administrative codes Freiberg MS, et al. JAMA Cardiology. 2017;2: Slide 17 of 42 HIV and Adjudicated Heart Failure Free from heart failure at baseline. All receiving care at large urban healthcare system from Frequencymatched on demographics, ZIP code, clinical site HIV+ (N=4640) versus Uninfected Controls (N=4250) (mean time to HF = 6.7 years) Physician-Adjudication of HF Endpoints from 30 days after baseline thru 7/12/2016 Adjustment for: Age, sex, race, bodymass index, diabetes, hypertension, hepatitis C virus, baseline year, and coronary heart disease N = 97 HIV+ with Incident HF N = 55 Uninfected Controls with Incident HF Multivariable-adjusted HR for HF = 2.10 (95% CI ) Slide 18 of 42 Feinstein MJ, et al. Under Review HIV and Adjudicated Heart Failure Slide 19 of 42 Feinstein MJ, et al. Under Review

7 Evolution of HIV and Heart Failure Pre-ART: LV dilation, systolic dysfunction, HFrEF Modern ART era: Some LV systolic dysfunction and increasingly diastolic dysfunction Slide 20 of 42 Multivariable-adjusted OR of DD for HIV+ persons vs. controls: 2.4 ( , p=0.019) Healthy HIV+ vs. controls (cardiac MRI): 6% lower LVEF 7% higher myocardial mass 29% lower diast. strain rate Myocardial fibrosis 4x more common Feinstein MJ et al. Curr Cardiol Rep. 2016;18:113. Fisher SD et al. Am J Cardiol. 2016;117: Hsue PY, et al. Circ Heart Fail. 2010;3: Ntusi N, et al. Circ Cardiovasc Imaging. 2016;9:e HIV and Heart Failure: Why? Slide 21 of 42 HIV and Heart Failure: Why? Slide 22 of 42

8 HIV, MI, Scar, and Heart Failure MI: Inflammatory cascade, then repair Clearance/resolution of inflammatory infiltrate less myocyte injury, infarct expansion T REG cells: Reduce Infarct size, attenuate adverse remodeling by: Peri-infarct neovascularization Slide 23 of 42 CD4+ T REG Pro-inflammatory cytokine expression Pro-inflammatory immune cell infiltration Excessive matrix degradation Tang TT, et al. Bas Res Cardiol. 2012;107:332. Meng X, et al. Nat Rev Cardiol. 2016;13: Hofmann U, et al. Circulation Research. 2015;116: HIV, MI, Scar, and Heart Failure Hypothesis: Persons with HIV have more scar following MI than uninfected persons Population Studied: - HIV+ vs. Uninfected Persons with CAD/MI who underwent Cardiac MRI to evaluate LVEF, scar - Matched on demographics, CVD risk factors, indication for testing Slide 24 of 42 Feinstein MJ, et al. J Am Coll Cardiol. 2016;68(18): HIV, MI, Scar, and Heart Failure HIV+ (N=12) HIV- (N=22) P value Arteries with 50% Stenosis 2.25± ± Number (%) of participants with angiographic characteristics LAD Stenosis 50% 92% 91% 0.94 LCx Stenosis 50% 58% 68% 0.57 RCA Stenosis 50% 58% 59% 0.97 Feinstein MJ, et al. J Am Coll Cardiol. 2016;68(18): Slide 25 of 42

9 Mean % Myocardial Scar HIV+: More Scar Lower LVEF (36% vs. 49%) Despite similar demographics, CVD risk factors, CAD burden Slide 26 of 42 Feinstein MJ, et al. J Am Coll Cardiol. 2016;68(18): HIV and Heart Failure: Why? Slide 27 of 42 Biomarkers & Diastolic Dysfunction Biomarker clusters in 332 HIV+ (N=138 with Diastolic Dysfcn) Cluster 1: Low inflammatory, Low fibrotic/cardiac Cluster 2: Low inflammatory, High fibrotic/cardiac Cluster 3: High inflammatory, Low fibrotic/cardiac Slide 28 of 42 Scherzer R, et al. Circ Heart Fail. 2018;11:e

10 Future Investigations MI HEART: Myocardial Imaging in HIV to Evaluate Arterial and Related Triggers of Dysfunction N=60 (40 HIV+, 20 controls) HIV+ CD4<350 HIV+ CD4>700 Uninfected Goal: Identify the extent to which HIVassociated pathologies contribute to subclinical myocardial dysfunction ( pre-heart failure ) Coronary CT Angiography: Evaluating Epicardial Coronary Artery Disease Cardiac MR with vasodilator perfusion Resting MR: Fibrosis, myocardial lipid, function Stress MR Perfusion: Quant myocardial blood flow (microvascular dz) Slide 29 of 42 So There s More Myocardial Infarction, Stroke, and Heart Failure in HIV. What Can We Do About It? ARS Question: Cardiovascular Risk Prediction When Assessing Risk for Cardiovascular Disease in a Patient with HIV, which of these Strategies Do You Most Commonly Use? 1. Framingham Risk Equations (CHD or Total CVD) 2. ACC/AHA 2013 ASCVD Risk Estimator 3. D:A:D Risk Equations 4. My Clinical Judgment Based on Risk Factors 5. None: I Don t Actively Assess CVD Risk Slide 31 of 42

11 ACC/AHA 2013 ASCVD Estimator in CNICS Guide to Interpretation: Harrell s C = 0.76 GND = 6.4 (P=.50) Slope = Intercept = Harrell s C = 0.74 GND = 10.3 (.24) Slope = Intercept = C-statistic (Discrimination): How well does the estimator rank-order higher vs. lower risk Harrell s C = 0.64 GND = 12.9 (<0.01) Slope = Intercept = Harrell s C = 0.74 GND = 8.0 (.24) Slope = Intercept = GND or Chi-Square (Calibration): How good is the fit? Greater difference (higher number, lower P value) means worse calibration Slide 32 of 42 Feinstein MJ, et al. JAMA Cardiology. 2017;2: Framingham ASCVD Estimator: PARTNERS C-Statistic: 0.67 Calibration Chi-Square = 24.6 (p=0.0004) Conclusion: Inadequate discrimination and calibration; consistent under-prediction of ASCVD risk. Similar findings when ACC/AHA model applied in PARTNERS Slide 33 of 42 Triant VA, et al. Circulation 2018;137(19):[ePub]. D:A:D, Framingham Total CVD (really ASCVD) Notes: D:A:D somewhat better calibrated but not externally validated - Full D:A:D model: Chi-square = 13.3 (p=0.11) - Reduced D:A:D model: Chi-square = 12.7 (p=0.21) - Framingham total CVD: Chi-square = 37.8 (p<0.001) Limitation of D:A:D, Framingham: Race not incorporated Endpoints were almost all ASCVD (MI, stroke, invasive coronary procedure, and CHD death accounted for 997 out of 1010 total CVD events) D:A:D reduced model adds CD4 count and family history; full model adds abacavir, PI, and NRTI exposure Slide 34 of 42 Friis-Moller N, et al. Eur J Prev Cardiol 2016;23(2):

12 HIV and Statins HIGH-QUALITY STATIN TRIAL EVIDENCE (RANDOMIZED CONTROLLED TRIALS) IN THE GENERAL POPULATION: >220,000 PEOPLE >1,000,000 PERSON-YEARS STATIN TRIALS FOR HIV (ON ART): 317 PPTS 213 PERSON-YRS NO HARD OUTCOMES Slide 35 of 42 Feinstein MJ, et al. Am J Cardiol 2015;115(2): Approach: CVD Prevention in HIV The risk estimators probably aren t that different. Not surprising - they use similar covariates and differ mainly in their derivation cohorts. Pick one that works for you (I use the 2013 ACC/AHA one) and consider it the low-end estimate of ASCVD risk. Probably 1.5-2x higher in HIV. Estimating risk is impt: Net clinical benefit = Absolute benefit harm = ARR adverse events Net benefit of statins most apparent by ~5% 10y risk and up. Consider this in determining Rx, plus lifestyle modification, DDIs, pt preferences Slide 36 of 42 What About Secondary Prevention? No data on highest intensity statin safety in HIV PROVE-IT TIMI 22 (general population) N=4162 with ACS Pravastatin 40 vs. Atorvastatin 80 16% relative risk reduction (4% ARR) with atorvastatin For highest risk people in the general population, we do the highest intensity tolerated, so why not in HIV? Consider DDIs and context, then start low, go slow to max tolerated dose. Slide 37 of 42

13 Role of Aspirin ASA did not impact markers of immune activation or endothelial function in HIV in a recent trial Young study population (mean age 48-50), 12 week f/u, endpoint of questionable utility/reproducibility (FMD) Therefore, despite pro-thrombotic risk in HIV, no compelling evidence to have different approach than in the general population for CVD prevention USPSTF: ASA for age if 10%+ 10y ASCVD risk Slide 38 of 42 O Brien MP, et al. Open Forum Infect Dis 2017;4(1):ofw278. Heart Failure Prevention and Treatment Studies ongoing to understand pathogenesis of heart failure (particularly with preserved ejection fraction) in HIV More investigation is needed to determine which screening tests and therapies unique to HIV-related heart failure prevention & treatment will be most useful In the meantime, diagnosis/treatment as in general population but with high index of suspicion Slide 39 of 42 Conclusions People living with HIV have elevated risks for atherosclerotic disease, thrombosis, and cardiac dysfunction / heart failure Chronic inflammation and immune activation persist despite effective ART and appear to play a role in CVD pathogenesis High indices of suspicion are needed to recognize and treat these CVDs in the aging HIV population. More research is needed regarding optimal screening and treatment strategies Slide 40 of 42