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Jeffrey P. Anderson Combination Antiretroviral Therapy and Hepatic Decompensation in HIV/HCV Coinfected Veterans JEFFREY P. ANDERSON | ERIC J. TCHETGEN TCHETGEN GEORGE R. SEAGE III | VINCENT LO RE III | JANET P. TATE JOSEPH K. LIM | PAIGE L. WILLIAMS C. ROBERT HORSBURGH | MATTHEW B. GOETZ DAVID RIMLAND | MARIA C. RODRIGUEZ-BARRADAS ADEEL A. BUTT | MARINA B. KLEIN | AMY C. JUSTICE 16th International Workshop on HIV Observational Databases March 29, 2012
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Background ~30% of HIV+ also infected with HCV1
HCV liver disease progression is accelerated among HIV coinfected Graham 2001 meta-analysis2: HIV/HCV vs. HCV mono, hepatic decompensation, HR=6.14 (2.86, 13.20) Effect of cART on hepatic decompensation unclear Protection conferred by immune restoration? Exacerbation due to hepatotoxicity, IRIS? 1 [ALTER J HEPATOL 44:S6-9.] 2 [GRAHAM CLIN INFECT DIS 33:562-9.]
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Objective To estimate the effect of initiation of cART on hepatic decompensation in a large HIV/HCV coinfected population Intent-to-treat approach Using marginal structural model (MSM) methods to account for time-varying confounding by indication
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Study Population Veterans Aging Cohort Study Virtual Cohort3 (VACS-VC), 3 [FULTZ MED CARE 44:S25-30.]
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Variables of Interest Outcome: Hepatic Decompensation
First occurrence of a hospital discharge diagnosis *or* ≥2 outpatient diagnoses of ascites, spontaneous bacterial peritonitis, or esophageal variceal hemorrhage Exposure: Initiation of cART ≥3 ARVs from ≥2 classes, *or* triple NRTI regimen of zidovudine / lamivudine / abacavir
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Covariates Considered
VARIABLE BASELINE % MISSING TIME-DEPENDENT Age X Year Race/ethnicity Diabetes HBV sAg 2 HCV treatment HCV genotype 61 HCV RNA 60 FIB-4 22 9 Alcohol abuse Drug abuse AIDS-defining Dx CD4 8 HIV RNA 1
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Marginal Structural Models (1)
Analytical issues Confounding by indication Sicker individuals more likely to be treated Informative censoring Death due to competing risks Standard adjusted Cox models generally biased in this context4 Inverse probability weighted (IPW) estimation of a marginal structural model (MSM) preferred 4 [ROBINS EPIDEMIOLOGY 11: ]
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Marginal Structural Models (2)
STEP 1. At each time point, use treatment & covariate history to estimate stabilized IP weights for likelihood of: Treatment initiation Censoring Death
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Adjusted IPW Models PREDICTOR cART CENSORING DEATH Age X Year
Race/ethnicity Diabetes HBV sAg HCV treatment FIB-4 Alcohol abuse Drug abuse AIDS-defining Dx CD4 HIV RNA
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Marginal Structural Models (3)
STEP 2. Apply stabilized IPW to marginal structural (weighted Cox) model to estimate hazard ratio of hepatic decompensation for cART initiators vs. non-initiators5 Unbiased effect under specific assumptions6 5 [HERNAN EPIDEMIOLOGY 11: ] 6 [COLE AM J EPIDEMIOL 168: ]
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Results
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TABLE 1. Covariate distributions and unadjusted associations with hepatic decompensation. (1)
BASELINE VARIABLES N (%) EVENTS UNADJUSTED HR (95% CI) P FOR TREND Total 10,090 (100) 645 - Age: 20-39 40-49 50-59 60-89 1459 (14) 5364 (53) 2718 (27) 549 (5) 68 370 180 27 Referent 1.55 (1.19, 2.00) 1.72 (1.30, 2.27) 1.35 (0.86, 2.10) 0.007 Year >2000 3310 (33) 135 0.82 (0.68, 1.00) 0.048 Race: White NH Black NH Hispanic Other 2703 (27) 6135 (61) 945 (9) 307 (3) 220 308 104 13 0.69 (0.58, 0.83) 1.40 (1.10, 1.76) 0.86 (0.49, 1.50) <0.001* HBV sAg 820 (8) 80 1.57 (1.24, 1.98) <0.001 Alcohol abuse 2495 (25) 188 1.41 (1.19, 1.67) Drug abuse 3208 (32) 169 0.85 (0.72, 1.02) 0.076
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TABLE 1. Covariate distributions and unadjusted associations with hepatic decompensation. (2)
TIME-DEPENDENT VARIABLES N (%) EVENTS UNADJUSTED HR (95% CI) P FOR TREND Diabetes 1378 (14) 128 1.88 (1.54, 2.29) <0.001 PegIFN (≥28 days) 355 (4) 30 1.80 (1.23, 2.62) 0.002 FIB-4: <1.45 >3.25 3178 (34) 3705 (39) 2513 (27) 48 86 495 Referent 1.82 (1.28, 2.59) 25.80 (19.18, 34.70) AIDS-defining Dx 5346 (53) 372 1.73 (1.47, 2.04) CD4: <50 50-199 ≥500 981 (11) 1913 (21) 1961 (21) 1715 (18) 2743 (29) 63 205 143 104 115 2.87 (2.10, 3.91) 3.52 (2.79, 4.44) 1.82 (1.42, 2.33) 1.43 (1.10, 1.87) HIV RNA: ≤400 401-10,000 10, ,000 >100,000 4917 (49) 2023 (20) 1996 (20) 1029 (10) 300 134 78 0.92 (0.75, 1.14) 1.25 (1.01, 1.55) 1.81 (1.39, 2.35)
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TABLE 2. Hazard ratios from marginal structural models for the association between cART initiation and hepatic decompensation. TIME-DEPENDENT TREATMENT VARIABLE PERSON-YEARS EVENTS HR (95% CI) Total 46,454 645 - No initiation cART initiation 10,873 35,581 187 458 Referent 0.72 (0.54, 0.95) <2 years since initiation 2 to <4 years since initiation ≥4 years since initiation 10,740 8,562 16,279 155 108 195 0.75 (0.56, 1.00) 0.70 (0.46, 1.04) 0.57 (0.36, 0.89)
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FIGURE 1. Estimated effect of cART on hepatic decompensation from MSM and unweighted (standard) models. 0.72 (0.54, 0.95) 0.99 (0.81, 1.21) 0.82 (0.64, 1.05) 0.86 (0.67, 1.10)
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Limitations Observational data Outcomes not confirmed
Validation study: 91% positive predictive value7 Missing data: HCV RNA, genotype Detailed alcohol/drug use not available MSM subject to specific assumptions 7 [LO RE PHARMACOEPIDEMIOL DRUG SAF 20: ]
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Conclusions We observed a 28% average reduction in the rate of hepatic decompensation due to initiation of cART in HIV/HCV coinfected veterans. These data suggest a clinical benefit to initiating cART in coinfected patients, in support of current guidelines.
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Collaborators Harvard School of Public Health (Boston, MA)
IWHOD #16_164 Jeffrey P. Anderson Collaborators Harvard School of Public Health (Boston, MA) George R. Seage III Eric J. Tchetgen Tchetgen Paige L. Williams Boston University (Boston, MA) C. Robert Horsburgh Yale / VA Connecticut (New Haven, CT) Amy C. Justice (P.I., VACS) Joseph K. Lim Janet P. Tate VA Healthcare System Affiliates Vincent Lo Re III (Philadelphia, PA) Matthew B. Goetz (Los Angeles, CA) David Rimland (Atlanta, GA) Maria C. Rodriguez-Barradas (Houston, TX) Adeel A. Butt (Pittsburgh, PA) Marina B. Klein (Montreal, Quebec)
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References Alter MJ. Epidemiology of viral hepatitis and HIV co-infection. J Hepatol 2006; 44:S6-9. Graham CS, Baden LR, Yu E et al. Influence of human immunodeficiency virus infection on the course of hepatitis C virus infection: a meta-analysis. Clin Infect Dis 2001; 33:562-9. Fultz SL, Skanderson M, Mole LA et al. Development and verification of a “virtual” cohort using the National VA Health Information System. Med Care 2006; 44:S25-30. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000; 11: Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 2000; 11: Cole SR, Hernan MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol 2008; 168: Lo Re V, Lim JK, Goetz MB et al. Validity of diagnostic codes and liver-related laboratory abnormalities to identify hepatic decompensation events in the Veterans Aging Cohort Study. Pharmacoepidemiol Drug Saf 2011; 20:
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Supplemental Slides
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ART & Liver Outcomes
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Background: Confounding by Indication (CBI)
Methodologically… Bias due to a time-varying confounder that predicts treatment and outcome status, but is also itself an intermediate affected by treatment on the causal pathway Intuitively… Sicker individuals are more likely to be treated – these individuals may also be more likely to develop adverse outcomes Results in a distorted estimate of the true effect of a treatment intervention When present, standard regression methods will be biased whether these factors are accounted for in adjusted models or not1 [1 Robins JM et al. Epidemiology 2000;11: ]
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CBI Illustration ART HepDec
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CBI Illustration ART HepDec CD4 (etc)
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CBI Illustration ARTt1 ARTt2 HepDec CD4t0 CD4t1
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MSM Assumptions Consistency Positivity Exchangeability
An individual’s outcome matches his/her potential outcome corresponding to their observed treatment history Positivity At any time point an untreated patient with a given covariate history has a non-negligible probability of initiating cART Exchangeability No unmeasured confounding No model misspecification
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Summary of Supplemental Analyses
Model (cART initiation vs. non-initiation) HR (95% CI) Primary MSM w/ categorical IPW predictors using PHREG 0.72 (0.55, 0.95) Pooled logistic regression method, structured data w/ 1-month intervals 0.68 (0.51, 0.91) Alternative time scale (time on treatment) 0.67 (0.52, 0.85) Continuous variables modeled as linear 0.73 (0.55, 0.96) CD4 & HIVRNA modeled using spline function w/ 3 knots 0.72 (0.55, 0.96) CD4 & HIVRNA modeled using spline function w/ 4 knots 0.70 (0.52, 0.93) CD4 & HIVRNA lagged 1 additional interval, irregular data 0.74 (0.57, 0.98) CD4 & HIVRNA lagged 1 additional interval, structured data 0.67 (0.50, 0.90) Truncated weights (1st, 99th percentiles) 7 knots for intercept spline function (vs. 5) 0.71 (0.54, 0.94) IPW component for visit process 0.80 (0.58, 1.11)
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TABLE. Distributions of selected patient characteristics and associations with cART initiation (predictors of treatment). BASELINE VARIABLE UNADJUSTED HR (95% CI) ADJUSTED P FOR TREND Age: 20-39 40-49 50-59 60-89 Referent 0.98 (0.92, 1.05) 0.97 (0.90, 1.05) 0.98 (0.87, 1.10) - Year >2000 1.06 (1.01, 1.12) 1.21 (1.15, 1.28) <0.001 Race: White NH Black NH Hispanic Other 0.91 (0.86, 0.96) 0.93 (0.85, 1.01) 0.96 (0.82, 1.12) 0.90 (0.85, 0.95) 0.89 (0.81, 0.97) 0.95 (0.81, 1.11) 0.001* HBV sAg 1.12 (1.03, 1.22) Alcohol abuse 0.86 (0.81, 0.91) Drug abuse 0.82 (0.78, 0.86) 0.86 (0.81, 0.90) 28
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TABLE (cont). Distributions of selected patient characteristics and associations with cART initiation (predictors of treatment). TIME-DEPENDENT VARIABLE UNADJUSTED HR (95% CI) ADJUSTED HR (95% CI) P FOR TREND Diabetes 0.86 (0.77, 0.97) - HCV treatment (PegIFN) 0.70 (0.46, 1.07) FIB-4: <1.45 >3.25 Referent 1.14 (1.08, 1.21) 1.19 (1.11, 1.28) AIDS-defining condition 1.40 (1.33, 1.47) 1.21 (1.12, 1.30) <0.001 CD4 count: <50 50-199 ≥500 5.17 (4.72, 5.66) 4.09 (3.78, 4.44) 3.12 (2.88, 3.38) 1.80 (1.65, 1.97) 3.76 (3.14, 4.50) 3.82 (3.32, 4.40) 2.84 (2.52, 3.20) 1.64 (1.47, 1.84) HIV RNA: ≤400 401-10,000 10, ,000 >100,000 0.91 (0.84, 0.98) 1.56 (1.45, 1.67) 2.47 (2.29, 2.67) 0.93 (0.85, 1.03) 1.25 (1.13, 1.38) 1.51 (1.34, 1.69) 29
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