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

• Published in: Journal of acquired immune deficiency syndromes (1999) Vol. 81; no. 5; pp. 562 - 571
• Main Authors: van Zoest, Rosan A, Law, Matthew, Sabin, Caroline A, Vaartjes, Ilonca, van der Valk, Marc, Arends, Joop E, Reiss, Peter, Wit, Ferdinand W
• Format: Journal Article
• Language: English
• Published: United States Copyright Wolters Kluwer Health, Inc. All rights reserved 15-08-2019; Lippincott Williams & Wilkins Ovid Technologies
• Subjects: Adult; AIDS/HIV; Algorithms; Anti-HIV Agents - therapeutic use; Anti-Retroviral Agents - therapeutic use; Antiretroviral agents; Antiretroviral therapy; Blood Pressure; Calibration; Cardiology; Cardiovascular disease; Cardiovascular diseases; Cardiovascular Diseases - epidemiology; Cardiovascular Diseases - etiology; CD4 antigen; CD4 Lymphocyte Count; Cholesterol; Cholesterol - blood; Data collection; Female; Goodness of fit; Health risk assessment; Health risks; Heart diseases; HIV; HIV Infections - complications; HIV Infections - drug therapy; Human immunodeficiency virus; Humans; Male; Middle Aged; Netherlands; Performance prediction; Propensity Score; Risk Assessment; Risk Factors; Risk groups; Smoking; Statistical analysis; Statistical tests; Netherlands
• ISSN: 1525-4135; 1944-7884
• DOI: 10.1097/QAI.0000000000002069

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 Harrellʼs C-statistic. Calibration was assessed using observed-versus-expected ratios, calibration plots, and Greenwood-Nam-DʼAgostino goodness-of-fit tests.
RESULTS:All algorithms showed acceptable discrimination (Harrellʼs 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-DʼAgostino 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).