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J Acquir Immune Defic Syndr. 2019 Aug 15;81(5):562-571. doi: 10.1097/QAI.0000000000002069.

Predictive Performance of Cardiovascular Disease Risk Prediction Algorithms in People Living With HIV.

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Department of Global Health, Amsterdam Public Health Research Institute, Amsterdam Universitair Medische Centra (Amsterdam UMC), University of Amsterdam, Amsterdam, the Netherlands.
Amsterdam Institute for Global Health and Development, Amsterdam, the Netherlands.
Kirby Institute, University of New South Wales, Sydney, Australia.
Institute for Global Health, University College London, London, United Kingdom.
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht (UMCU), University Utrecht, Utrecht, the Netherlands.
Department of Internal Medicine, Division of Infectious Diseases, Amsterdam UMC, Amsterdam Infection and Immunity Institute, University of Amsterdam, Amsterdam, the Netherlands.
Department of Internal Medicine, Section Infectious Diseases, UMCU, University Utrecht, Utrecht, the Netherlands.
HIV Monitoring Foundation, Amsterdam, the Netherlands.



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.


The Netherlands.


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.


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 ranged from 24.57 to 34.22, P < 0.05). Underestimation of CVD risk was particularly observed in low-predicted CVD risk groups.


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).

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