Cardiovascular Risk Prediction Functions Underestimate Risk in HIV Infection.

Abstract

Cardiovascular disease (CVD) risk is elevated in HIV-infected individuals, with contributions from both traditional and nontraditional risk factors. The accuracy of established CVD risk prediction functions in HIV is uncertain. We sought to assess the performance of 3 established CVD risk prediction functions in a longitudinal cohort of HIV-infected men. The FHS (Framingham Heart Study) functions for hard coronary heart disease (FHS CHD) and atherosclerotic CVD (FHS ASCVD) and the American College of Cardiology/American Heart Association ASCVD function were applied to the Partners HIV cohort. Risk scores were calculated between January 1, 2006, and December 31, 2008. Outcomes included CHD (myocardial infarction or coronary death) for the FHS CHD function and ASCVD (myocardial infarction, stroke, or coronary death) for the FHS ASCVD and American College of Cardiology/American Heart Association ASCVD functions. We investigated the accuracy of CVD risk prediction for each function when applied to the HIV cohort using comparison of Cox regression coefficients, discrimination, and calibration. The HIV cohort was comprised of 1272 men followed for a median of 4.4 years. There were 78 (6.1%) ASCVD events; the 5-year incidence rate was 16.4 per 1000 person-years. Discrimination was moderate to poor as indicated by the low c statistic (0.68 for FHS CHD, 0.65 for American College of Cardiology/American Heart Association ASCVD, and 0.67 for FHS ASCVD). Observed CVD risk exceeded the predicted risk for each of the functions in most deciles of predicted risk. Calibration, or goodness of fit of the models, was consistently poor, with significant χ2P values for all functions. Recalibration did not significantly improve model fit. Cardiovascular risk prediction functions developed for use in the general population are inaccurate in HIV infection and systematically underestimate risk in a cohort of HIV-infected men. Development of tailored CVD risk prediction functions incorporating traditional CVD risk factors and HIV-specific factors is likely to result in more accurate risk estimation to guide preventative CVD care.