TY - JOUR
T1 - Predictive Performance of Cardiovascular Disease Risk Prediction Algorithms in People Living With HIV
AU - Van Zoest, Rosan A.
AU - Law, Matthew
AU - Sabin, Caroline A.
AU - Vaartjes, Ilonca
AU - Van Der Valk, Marc
AU - Arends, Joop E.
AU - Reiss, Peter
AU - Wit, Ferdinand W.
AU - on behalf of the ATHENA National Observational HIV Cohort
AU - Geerlings, S.E.
AU - Hovius, JWR
AU - Kuijpers, TW
AU - Nellen, FJB
AU - Prins, JM
AU - Wiersinga de Vreede, WJ
AU - Peters, EJ
AU - van Agtmael, MA
AU - Bomers, MK
AU - Ang, CW
AU - van Houdt, R
AU - Pettersson, AM
AU - Vandenbroucke-Grauls, CMJE
AU - Wintermans, BB
PY - 2019/8/15
Y1 - 2019/8/15
N2 - 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).
AB - 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).
KW - HIV
KW - cardiovascular disease
KW - risk prediction algorithms
UR - http://www.scopus.com/inward/record.url?scp=85069888409&partnerID=8YFLogxK
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85069888409&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/31045648
U2 - https://doi.org/10.1097/QAI.0000000000002069
DO - https://doi.org/10.1097/QAI.0000000000002069
M3 - Article
C2 - 31045648
SN - 1525-4135
VL - 81
SP - 562
EP - 571
JO - JOURNAL OF ACQUIRED IMMUNE DEFICIENCY SYNDROMES
JF - JOURNAL OF ACQUIRED IMMUNE DEFICIENCY SYNDROMES
IS - 5
ER -