Current use of statins reduces risk of HIV rebound on suppressive HAART

dataset

posted on 2017-03-01, 18:38 authored by Henning Drechsler, Colby Ayers, James Cutrell, Naim Maalouf, Pablo Tebas, Roger Bedimo

Background

Despite compelling evidence for activity against HIV-1 in vitro, a virologic effect of statins has not been shown in clinical studies. Given their short plasma half-lives, such an effect may be transient and only apparent during ongoing exposure.

Methods

We studied all HIV infected US-Veterans who started HAART 1995–2011, had a documented HIV viral load (VL) >1000 copies/mL, reached an undetectable VL on HAART, and had ≥1 follow-up VL within 13 months. We defined virologic failure (VF) as the first VL >1,000 copies/mL or the first of 2 consecutive VL >200 copies/mL. We built a time-updated drug exposure model for antiretrovirals (ARVs), statins, and other cardiovascular drugs (CVMs), investigating current use (yes/no), recent use (proportion of days used), and categorical use (ever/never). We used both multiply adjusted and inverse-probability-weighted (IPW) Cox models to explore the association between statin and CVM use and VF.

Results

19,324 veterans met inclusion criteria. Median follow-up was 13 months (IQR: 5–32 months); 63% experienced VF after a median time of 9 months (IQR 4–21 months). Almost 1/3 patients ever used statins but exposure comprised only 41% of follow-up time covered after initial prescription. Unadjusted, current statin use was associated with a hazard ratio (HR) for VF of 0.60 (CI: 0.56–0.65). This remained statistically significant after multivariate adjustment (MVA) for demographics, HIV and HAART parameters [HR 0.81 (CI: 0.75–0.88), p<0.001] and IPW (truncation <1%/>99%) HR: 0.83 (CI: 0.75–0.92), p<0.001]. No independent association was observed for other CVMs. The association between categorical-statin use and VF after MVA was much weaker: HR 0.94 (CI: 0.88–1.00, p = 0.04).

Conclusion

Current statin exposure was associated with reduced risk of VF in univariate, multivariate, and inverse-probability-weighted models. Our results highlight the importance of time-updated medication exposure models for observational studies.