Format

Send to

Choose Destination
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.

Author information

1
Department of Global Health, Amsterdam Public Health Research Institute, Amsterdam Universitair Medische Centra (Amsterdam UMC), University of Amsterdam, Amsterdam, the Netherlands.
2
Amsterdam Institute for Global Health and Development, Amsterdam, the Netherlands.
3
Kirby Institute, University of New South Wales, Sydney, Australia.
4
Institute for Global Health, University College London, London, United Kingdom.
5
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht (UMCU), University Utrecht, Utrecht, the Netherlands.
6
Department of Internal Medicine, Division of Infectious Diseases, Amsterdam UMC, Amsterdam Infection and Immunity Institute, University of Amsterdam, Amsterdam, the Netherlands.
7
Department of Internal Medicine, Section Infectious Diseases, UMCU, University Utrecht, Utrecht, the Netherlands.
8
HIV Monitoring Foundation, Amsterdam, the Netherlands.

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 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).

Supplemental Content

Full text links

Icon for Wolters Kluwer
Loading ...
Support Center