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

Rosan A. van Zoest*, Matthew Law, Caroline A. Sabin, Ilonca Vaartjes, Marc Van der Valk, Joop E. Arends, Peter Reiss, Ferdinand W. Wit, S. E. Geerlings, M. H. Godfried, A. Goorhuis, J. W. Hovius, T. W. Kuijpers, F. J. B. Nellen, D. T. van der Poll, J. M. Prins, H. J. M. van Vugt, W. J. Wiersinga, F. W. M. N. Wit, M. van DuinenM. T. E. Cornelissen, E. J. G. Peters, M. Groot, A. Verbon, J. E. A. van Beek, J. de Groot, M. G. J. de Boer, J. V. Smit, E. Smit, S. H. Lowe, A. M. L. Oude Lashof, D. Posthouwer, R. P. Ackens, K. Burgers, J. Schippers, I. H. M. van Loo, T. R. A. Havenith, P. H. M. Smits, S. Weijer, G. J. Kootstra, R. Jansen, C. J. Brouwer, A. J. A. M. van der Ven, M. de Haan, M. van Wijk, M. Bakker, A. de Jong, M. van den Akker, R. van der Meer, ATHENA National Observational HIV Cohort

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

22 Citations (Web of Science)


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 chi(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).

Original languageEnglish
Pages (from-to)562-571
Number of pages10
JournalJaids-journal of Acquired Immune Deficiency Syndromes
Issue number5
Publication statusPublished - 15 Aug 2019


  • HIV
  • cardiovascular disease
  • risk prediction algorithms

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