Predictive Performance of Cardiovascular Disease Risk Prediction Algorithms in People Living With HIV
Background: People living with HIV (PLWH) experience a higher cardiovascular disease (CVD) risk. Yet, traditional algorithms are often used to estimate CVD risk. We evaluated the performance of 4 commonly used algorithms. Setting: The Netherlands. Methods: We used data from 16,070 PLWH aged $18 years, who were in care between 2000 and 2016, had no pre-existing CVD, had initiated first combination antiretroviral therapy .1 year ago, and had available data on CD4 count, smoking status, cholesterol, and blood pressure. Predictive performance of 4 algorithms [Data Collection on Adverse Effects of Anti-HIV Drugs Study (D:A:D); Systematic COronary Risk Evaluation adjusted for national data (SCORE-NL); Framingham CVD Risk Score (FRS); and American College of Cardiology and American Heart Association Pooled Cohort Equations (PCE)] was evaluated using a Kaplan–Meier approach. Model discrimination was assessed using Harrell’s C-statistic. Calibration was assessed using observed-versusexpected ratios, calibration plots, and Greenwood-Nam-D’Agostino goodness-of-fit tests. Results: All algorithms showed acceptable discrimination (Harrell’s C-statistic 0.73–0.79). On a population level, D:A:D, SCORE-NL, and PCE slightly underestimated, whereas FRS slightly overestimated CVD risk (observed-versus-expected ratios 1.35, 1.38, 1.14, and 0.92, respectively). D:A:D, FRS, and PCE best fitted our data but still yielded a statistically significant lack of fit (Greenwood-Nam-D’Agostino x2 ranged from 24.57 to 34.22, P , 0.05). Underestimation of CVD risk was particularly observed in low-predicted CVD risk groups.
|Keywords||HIV, cardiovascular disease, risk prediction algorithms|
|Persistent URL||dx.doi.org/10.1097/qai.0000000000002069, hdl.handle.net/1765/119454|
|Journal||J A I D S|
van Zoest, R.A., Law, M, Sabin, C.A., Vaartjes, I, van der Valk, M, & Arends, J.E. (2019). Predictive Performance of Cardiovascular Disease Risk Prediction Algorithms in People Living With HIV. J A I D S, 81(5), 562–571. doi:10.1097/qai.0000000000002069