Cardiovascular disease (CVD) risk prediction tools are often applied to populations beyond those in which they were designed when validated tools for specific subpopulations are unavailable.
Using data from 2,283 HIV-infected adults aged ≥18 years, who were active in the HIV Outpatient Study (HOPS), we assessed performance of three commonly used CVD prediction models developed for general populations: Framingham general cardiovascular Risk Score (FRS), American College of Cardiology/American Heart Association Pooled Cohort equations (PCE), and Systematic COronary Risk Evaluation (SCORE) high-risk equation, and one model developed in HIV-infected persons: the Data Collection on Adverse Effects of Anti-HIV Drugs (D:A:D) study equation. C-statistics assessed model discrimination and the ratio of expected to observed events (E/O) and Hosmer-Lemeshow χ(2) P-value assessed calibration.
From January 2002 through September 2013, 195 (8.5%) HOPS participants experienced an incident CVD event in 15,056 person-years. The FRS demonstrated moderate discrimination and was well calibrated (C-statistic: 0.66, E/O: 1.01, P=0.89). The PCE and D:A:D risk equations demonstrated good discrimination but were less well calibrated (C-statistics: 0.71, 0.72 and E/O: 0.88, 0.80, respectively; P<0.001 for both), while SCORE performed poorly (C-statistic: 0.59, E/O: 1.72, P =0.48). CONCLUSION
Only the FRS accurately estimated risk of CVD events, while PCE and D:A:D underestimated risk. Although these models could potentially be used to rank U.S. HIV-infected individuals at higher or lower risk for CVD, the models may fail to identify substantial numbers of HIV-infected persons with elevated CVD risk who could potentially benefit from additional medical treatment.