Predictive Performance of Cardiovascular Disease Risk Prediction Algorithms in People Living With HIV. (15th August 2019)

• Record Type: Journal Article
• Authors: van Zoest, Rosan A.; Law, Matthew; Sabin, Caroline A.; Vaartjes, Ilonca; van der Valk, Marc; Arends, Joop E.; Reiss, Peter; Wit, Ferdinand W.
• Is Part Of: Journal of acquired immune deficiency syndromes. Volume 81:Number 5(2019)
• Publication Date: 2019-08-15
• Subjects: HIV -- cardiovascular disease -- risk prediction algorithms; AIDS (Disease) -- Periodicals; Acquired Immunodeficiency Syndrome -- Periodicals; AIDS (Disease); Periodicals; 616.9792005
• DOI: 10.1097/QAI.0000000000002069
• Languages: English
• ISSNs: 1525-4135

Abstract

Abstract : 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-versus-expected 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 χ 2 ranged from 24.57 to 34.22, P < 0.05). Underestimation of CVD risk was particularly observed in low-predicted CVD risk groups. Conclusions: All algorithms perform reasonably well in PLWH, with SCORE-NL performing poorest. Prediction algorithms are useful for clinical practice, but clinicians should be aware of their limitations (ie, lack of fit and slight underestimation of CVD risk in low-risk groups). Abstract : Supplemental Digital Content is Available in the Text.