1. Predictive Performance of Cardiovascular Disease Risk Prediction Algorithms in People Living With HIV
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Zoest, R.A. van, Law, M., Sabin, C.A., Vaartjes, I., Valk, M. van der, Arends, J.E., Reiss, P., Wit, F.W., Geerlings, S.E., Godfried, M.H., Goorhuis, A., Hovius, J.W., Kuijpers, T.W., Nellen, F.J.B., Poll, D.T. van der, Prins, J.M., Vugt, H.J.M. van, Wiersinga, W.J., Wit, F.W.M.N., Duinen, M. van, Eden, J. van, Hes, A.M.H. van, Pijnappel, F.J.J., Weijsenfeld, A.M., Jurriaans, S., Back, N.K.T., Zaaijer, H.L., Berkhout, B., Cornelissen, M.T.E., Schinkel, C.J., Wolthers, K.C., Peters, E.J.G., Agtmael, M.A. van, Bomers, M., Heitmuller, M., Laan, L.M., Ang, C.W., Houdt, R. van, Pettersson, A.M., Vandenbroucke-Grauls, C.M.J.E., Berge, M. van den, Stegeman, A., Baas, S., Looff, L.H. de, Wintermans, B., Veenemans, J., Pronk, M.J.H., Ammerlaan, H.S.M., Munnik, E.S. de, Jansz, A.R., Tjhie, J., Wegdam, M.C.A., Deiman, B., Scharnhorst, V., Eeden, A. van, Brokking, W., Groot, M., Elsenburg, L.J.M., Damen, M., Kwa, I.S., Kasteren, M.E.E. van, Brouwer, A.E., Erve, R. van, Kruijf-van de Wiel, B.A.F.M. de, Ven, B. van de, Buiting, A.G.M., Kabel, P.J., Versteeg, D., Ende, M.E. van der, Bax, H.I., Gorp, E.C.M. van, Nouwen, J.L., Rijnders, B.J.A., Schurink, C.A.M., Verbon, A., Vries-Sluijs, T.E.M.S. de, Jong-Peltenburg, N.C. de, Bassant, N., Beek, J.E.A. van, Vriesde, M., Zonneveld, L.M. van, Berg-Cameron, H.J. van den, Groot, J. de, Zeeuw-de Man, M. de, Boucher, C.A.B., Koopmans, M.P.G., Kampen, J.J.A. van, Pas, S.D., Branger, J., Douma, R.A., Duijf-van de Ven, C.J.H.M., Schippers, E.F., Nieuwkoop, C. van, IJperen, J.M. van, Geilings, J., Hut, G. van der, Burgel, N.D. van, Leyten, E.M.S., Gelinck, L.B.S., Davids-Veldhuis, S., Hartingsveld, A.Y. van, Meerkerk, C., Wildenbeest, G.S., Heikens, E., Groeneveld, P.H.P., Bouwhuis, J.W., Lammers, A.J.J., Kraan, S., Hulzen, A.G.W. van, Kruiper, M.S.M., Bliek, G.L. van der, Bor, P.C.J., Bloembergen, P., Wolfhagen, M.J.H.M., Ruijs, G.J.H.M., Kroon, F.P., Boer, M.G.J. de, Scheper, H., Jolink, H., Dorama, W., Holten, N. van, Claas, E.C.J., Wessels, E., Hollander, J.G. den, Pogany, K., Roukens, A., Kastelijns, M., Smit, J.V., Smit, E., Struik-Kalkman, D., Tearno, C., Niekerk, T. van, Pontesilli, O., Lowe, S.H., Lashof, A.M.L.O., Posthouwer, D., Ackens, R.P., Burgers, K., Schippers, J., Weijenberg-Maes, B., Loo, I.H.M. van, Havenith, T.R.A., Mulder, J.W., Vrouenraets, S.M.E., Lauw, F.N., Broekhuizen, M.C. van, Vlasblom, D.J., Smits, P.H.M., Weijer, S., Moussaoui, R. el, Bosma, A.S., Vonderen, M.G.A. van, Kampschreur, L.M., Dijkstra, K., Faber, S., Weel, J., Kootstra, G.J., Delsing, C.E., Burg-van de Plas, M. van der, Heins, H., Kortmann, W., Twillert, G. van, Renckens, R., Ruiter-Pronk, D., Truijen-Oud, F.A. van, Stuart, J.W.T.C., IJzerman, E.P., Jansen, R., Reijden, W.A. van der, Brinkman, K., Berk, G.E.L. van den, Blok, W.L., Frissen, P.H.J., Lettinga, K.D., Schouten, W.E.M., Veenstra, J., Brouwer, C.J., Geerders, G.F., Hoeksema, K., Kleene, M.J., Meche, I.B. van der, Spelbrink, M., Toonen, A.J.M., Wijnands, S., Kwa, D., Regez, R., Crevel, R. van, Keuter, M., Ven, A.J.A.M. van der, Hofstede, H.J.M. ter, Dofferhoff, A.S.M., Hoogerwerf, J., Grintjes-Huisman, K.J.T., Haan, M. de, Marneef, M., Rahamat-Langendoen, J., Stelma, F.F., Burger, D., Gisolf, E.H., Hassing, R.J., Claassen, M., Beest, G. ter, Bentum, P.H.M. van, Langebeek, N., Tiemessen, R., Swanink, C.M.A., Lelyveld, S.F.L. van, Soetekouw, R., Prijt, L.M.M. van der, Swaluw, J. van der, Bermon, N., Herpers, B.L., Veenendaal, D., Verhagen, D.W.M., Wijk, M. van, Bierman, W.F.W., Bakker, M., Kleinnijenhuis, J., Kloeze, E., Stienstra, Y., Wilting, K.R., Wouthuyzen-Bakker, M., Boonstra, A., Meulen, P.A. van der, Weerd, D.A. de, Niesters, H.G.M., Leer-Buter, C.C. van, Knoester, M., Hoepelman, A.I.M., Barth, R.E., Bruns, A.H.W., Ellerbroek, P.M., Mudrikova, T., Oosterheert, J.J., Schadd, E.M., Wassenberg, M.W.M., Zoelen, M.A.D. van, Aarsman, K., Elst-Laurijssen, D.H., Rozemeijer, Graduate School, AII - Infectious diseases, APH - Aging & Later Life, Infectious diseases, Global Health, APH - Digital Health, APH - Personalized Medicine, APH - Global Health, Microbes in Health and Disease (MHD), Neurochirurgie, RS: MHeNs - R3 - Neuroscience, Med Microbiol, Infect Dis & Infect Prev, MUMC+: DA MMI Staf (9), RS: CAPHRI - R4 - Health Inequities and Societal Participation, Internal medicine, Pediatric surgery, Amsterdam Movement Sciences - Rehabilitation & Development, ACS - Diabetes & metabolism, AGEM - Digestive immunity, Medical Microbiology and Infection Prevention, Amsterdam Reproduction & Development (AR&D), Internal Medicine, Virology, and Medical Microbiology & Infectious Diseases
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Male ,Heart disease ,Human immunodeficiency virus (HIV) ,risk prediction algorithms ,Blood Pressure ,HIV Infections ,Disease ,030312 virology ,medicine.disease_cause ,SUBCLINICAL ATHEROSCLEROSIS ,Risk Factors ,cardiovascular disease ,FRAMINGHAM ,Pharmacology (medical) ,Netherlands ,0303 health sciences ,INFECTED PATIENTS ,Framingham Risk Score ,Middle Aged ,Prediction algorithms ,Cholesterol ,Infectious Diseases ,Anti-Retroviral Agents ,Cardiovascular Diseases ,HUMAN-IMMUNODEFICIENCY-VIRUS ,Female ,Risk assessment ,Algorithms ,Adult ,Anti-HIV Agents ,CARDIOLOGY/AMERICAN HEART ASSOCIATION ,HEART-DISEASE ,AMERICAN-COLLEGE ,DATA-COLLECTION ,Risk Assessment ,EVENTS ,03 medical and health sciences ,SDG 3 - Good Health and Well-being ,INFLAMMATION ,Environmental health ,SCORE ,medicine ,Humans ,Propensity Score ,business.industry ,HIV ,medicine.disease ,CD4 Lymphocyte Count ,MYOCARDIAL-INFARCTION ,Propensity score matching ,Disease risk ,business - 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-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 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).
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- 2019