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

van Zoest, Rosan A. MDa,b; Law, Matthew PhDc; Sabin, Caroline A. PhDd; Vaartjes, Ilonca PhDe; van der Valk, Marc MD, PhDf; Arends, Joop E. MD, PhDg; Reiss, Peter MD, PhDa,b,f,h; Wit, Ferdinand W. MD, PhDa,b,f,h

JAIDS Journal of Acquired Immune Deficiency Syndromes: August 15, 2019 - Volume 81 - Issue 5 - p 562–571
doi: 10.1097/QAI.0000000000002069
Clinical Science
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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 χ2 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).

aDepartment of Global Health, Amsterdam Public Health Research Institute, Amsterdam Universitair Medische Centra (Amsterdam UMC), University of Amsterdam, Amsterdam, the Netherlands;

bAmsterdam Institute for Global Health and Development, Amsterdam, the Netherlands;

cKirby Institute, University of New South Wales, Sydney, Australia;

dInstitute for Global Health, University College London, London, United Kingdom;

eJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht (UMCU), University Utrecht, Utrecht, the Netherlands;

fDepartment of Internal Medicine, Division of Infectious Diseases, Amsterdam UMC, Amsterdam Infection and Immunity Institute, University of Amsterdam, Amsterdam, the Netherlands;

gDepartment of Internal Medicine, Section Infectious Diseases, UMCU, University Utrecht, Utrecht, the Netherlands; and

hHIV Monitoring Foundation, Amsterdam, the Netherlands.

Correspondence to: Rosan A. van Zoest, MD, Department of Global Health, Academic Medical Center, Amsterdam Institute for Global Health and Development, Amsterdam Health Technology Center, Tower C4, Paasheuvelweg 25, 1105 BP Amsterdam, the Netherlands (e-mail: r.a.vanzoest@amsterdamumc.nl).

The ATHENA cohort is managed by Stichting HIV Monitoring and supported by a grant from the Dutch Ministry of Health, Welfare and Sport through the Centre for Infectious Disease Control of the National Institute for Public Health and the Environment.

Presented in part at the 20th International Workshop on Comorbidities and Adverse Drug Reactions in HIV; October 13–14, 2018; New York, NY.

R.A.v.Z. has received travel grants from Gilead Sciences and speaker fees from Gilead Sciences and Janssen-Cilag for which her institution received remuneration. M.L. reports unrestricted grants from Boehringer Ingelheim, Gilead Sciences, Merck Sharp & Dohme, Bristol-Myers Squibb, Janssen-Cilag, ViiV HealthCare, and consultancy and presentation fees from Gilead Sciences. C.A.S. has received funding for participation in Advisory Boards, for membership of Data Safety and Monitoring Committees, and for the preparation of educational materials from Gilead Sciences, ViiV Healthcare and Janssen-Cilag. M.v.d.V. reports personal fees from Abbvie, BMS, Gilead Sciences, ViiV Healthcare, Merck, and Janssen outside the submitted work. J.E.A. reports institutional fees from Gilead Sciences, ViiV Healthcare, Abbvie, Janssen, and Merck; institutional (research) grants from Merck, Abbvie, BMS, and Jansen. P.R. reports independent scientific grant support outside the submitted work from Gilead Sciences, Janssen Pharmaceuticals Inc, Merck & Co, Bristol-Myers Squibb and ViiV Healthcare (through his institution); he has served on scientific advisory board for Gilead Sciences, ViiV Healthcare, Merck & Co and Teva Pharmaceutical Industries and on a Data Safety Monitoring Committee for Janssen Pharmaceuticals Inc (all honoraria paid to institution). F.W.W. has received consultation and speaker fees from Gilead Sciences and ViiV Healthcare. The remaining author has no conflicts of interest to disclose.

R.A.v.Z. and F.W.W. conceived the idea of the study. R.A.v.Z., M.L., C.A.S., I.V., and F.W.W. contributed to the design of the study. R.A.v.Z. analyzed the data, interpreted the results, and wrote all major drafts of the manuscript. M.L., C.A.S., I.V., M.v.d.V., J.E.A., P.R., and F.W.W. contributed to the interpretation of the results and the critical review of the manuscript.

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Received December 18, 2018

Accepted April 01, 2019

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