Van Zoest, RA;
Law, M;
Sabin, CA;
Vaartjes, I;
Van Der Valk, M;
Arends, JE;
Reiss, P;
(2019)
Predictive Performance of Cardiovascular Disease Risk Prediction Algorithms in People Living with HIV.
Journal of Acquired Immune Deficiency Syndromes
, 81
(5)
pp. 562-571.
10.1097/QAI.0000000000002069.
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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 four commonly used algorithms. SETTING: The Netherlands. METHODS: We used data from 16,070 PLWH aged ≥18 years, who were in care between 2000-2016, 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 four 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]; 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, 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 24.57-34.22, 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 (i.e., lack of fit andslight underestimation of CVD risk in low risk groups).
| Type: | Article |
|---|---|
| Title: | Predictive Performance of Cardiovascular Disease Risk Prediction Algorithms in People Living with HIV |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1097/QAI.0000000000002069 |
| Publisher version: | https://doi.org/10.1097/QAI.0000000000002069 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health > Infection and Population Health |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10074048 |
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