The Power of Propensity Score Matching to Remove Confounding by Indication:  Statins and Acute Myocardial Infarction among HIV Infected and Uninfected Veterans Elyn H. Wang, Matthew S. Freiberg, Janet P. Tate, Joyce Chang, Melissa Skanderson, Amy Justice for the Veterans Aging Cohort Study (VACS)

Veterans Aging Cohort Study (VACS) Required: Optional based on project funding:

Virtual Cohort (VC) SUBJECTS: 47,805 HIV infected; 99,061 uninfected All individuals with HIV diagnoses Age, race/ethnicity, region 2:1 matched controls Last updated: September 2012 Next update: September 2014 SITES: All VA sites BASELINE: 1998 (15 years of follow up) HIV infected veterans at initiation of HIV care Controls selected and followed in same calendar year

HIV and Acute Myocardial Infarction (AMI) Prior studies show an increased risk of AMI in HIV+ - Abnormal lipids - Chronic Inflammation Statins have been shown to decrease CHD events - LDL reduction - Cholesterol-independent effects Do HIV+ individuals experience similar effects from statin therapy compared to HIV- individuals?

Methods Study population Veterans Aging Cohort Study Virtual Cohort (VACS-VC) from April 1, 2003 to December 31, 2009 Exclusions: - Prior or Existing CVD - Statin Use Within 365 Days Data Sources - Pharmacy Fill/Refill (Time-Updated) Patient had to be on statins for at least 30 days - Medical Charts and Lab Results

Study Populationa HIV – HIV+ Non-User Statin User AMI Events (%) 233 (0.8) 275 (1.1) 217 (1.1) 146 (1.7) Mean Age (SD) 47.3 (9.3) 49.4 (8.5) 47.2 (9.4) 49.9 (9.0) Male (%) 96.8 97.7 97.3 97.1 Race (%) White 35.4 38.3 34.2 44.2 Black 50.2 47.6 51.7 41.6 Other 14.4 14.1 Alcohol (%) 15.3 12.5 15.8 11.0 Cocaine (%) 8.5 6.9 12.9 8.4 CD4 <200 31.6 21.8 VL ≥500 60.8 44.4 aStatistically significant difference between Non-user and Statin user for all characteristics

Other Covariates HCV Hgb <14 TG ≥150 LDL ≥130 BMI ≥30 Smoking HTN eGFR <60 Diabetes HDL <40

Results AMI Risk and Statin Use Stratified Model HR (95% CI) p-value HIV (-) Statin Use 0.81 (0.64-1.02) 0.07 HIV (+) Statin Use 1.12 (0.86 -1.46) 0.40 Interaction Between Statin Use and HIV Status (p =0.16)

The Next Step Propensity Score Analysis Step 1: Estimate each patient’s propensity score (probability of receiving treatment) based on multiple variables Step 2: Match each treated patient with a non- treated patient who has a similar propensity score Treated Patients Not Treated

HIV Age Sex Race Hypertension Diabetes LDL HDL Triglycerides BMI eGFR Hgb HCV Smoking Alcohol Cocaine Propensity Score Modeling Patient characteristics and statin initiation Sex and Cocaine were not significant predictors

c = 0.804

Results After Matching AMI Risk and Statin Use Matched Model HR (95% CI) p-value HIV (-) Statin Use 0.75 (0.60 - 0.95) 0.02 HIV (+) Statin Use 0.82 (0.62 - 1.09) 0.18 Interaction Between Statin Use and HIV Status (p =0.64)

National VACS Project Team 2011

Acknowledgements Consortium PI : AC Justice* Scientific Collaborator (NIAAA): K Bryant Affiliated PIs: S Braithwaite, K Crothers*, R Dubrow *, DA Fiellin*, M Freiberg*, V LoRe* Participating VA Medical Centers: Atlanta (D. Rimland*, V Marconi), Baltimore (M Sajadi, R Titanji), Bronx (S Brown, Y Ponomarenko), Dallas (R Bedimo), Houston (M Rodriguez-Barradas, N Masozera), Los Angeles (M Goetz, D Leaf), Manhattan-Brooklyn (M Simberkoff, D Blumenthal, H Leaf, J Leung), Pittsburgh (A Butt, K Kraemer, M Freiberg, E Hoffman), and Washington DC (C Gibert, R Peck) Core and Workgroup Chairs: C Brandt, J Edelman, N Gandhi, J Lim, K McGinnis, KA Oursler, C Parikh, J Tate, E Wang, J Womack Staff: H Bathulapalli, T Bohan, J Ciarleglio, A Consorte, P Cunningham, L Erickson, C Frank, K Gordon, J Huston, F Kidwai-Khan, G Koerbel, F Levin, L Piscitelli, C Rogina, S Shahrir, M Skanderson Major Collaborators: VA Public Health Strategic Healthcare Group, VA Pharmacy Benefits Management, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Yale Center for Interdisciplinary Research on AIDS (CIRA), Center for Health Equity Research and Promotion (CHERP), ART-CC, NA-ACCORD, HIV-Causal Cross Cohort Collaborators: Richard Moore (NA-ACCORD), Jonathan Sterne (ART-CC), Brian Agan (DoD) Major Funding by: National Institutes of Health: AHRQ (R01-HS018372), NIAAA (U24-AA020794, U01-AA020790, U01-AA020795, U01-AA020799, U24-AA022001, U24 AA022007), NHLBI (R01-HL095136; R01-HL090342) , NIAID (U01-A1069918), NIMH (P30-MH062294), NIDA (R01DA035616), NCI (R01 CA173754) and the Veterans Health Administration Office of Research and Development (VA REA 08-266, VA IRR Merit Award) and Office of Academic Affiliations (Medical Informatics Fellowship) *Indicates individual is also the Chair of a Core or Workgroup

Acknowledgements Continued COMpAAAS/Veterans Aging Cohort Study, a CHAART Cooperative Agreement, supported by the National Institutes of Health: National Institute on Alcohol Abuse and Alcoholism (U24-AA020794, U01-AA020790, U01-AA020795, U01-AA020799) and in kind by the US Department of Veterans Affairs.  In addition to grant support from NIAAA, we gratefully acknowledge the scientific contributions of Dr. Kendall Bryant, our scientific collaborator. QR Codes QR Code for VACS QR Code for VACS QR Code for VACS Homepage INDEX CALCULATOR- INDEX CALCULATOR- MOBILE APP