M Krikke1, R C Hoogeveen1, A I M Hoepelman1, F L J Visseren2, J E Arends1. 1. Department of Internal Medicine and Infectious Diseases, University Medical Centre Utrecht (UMCU), Utrecht, The Netherlands. 2. Department of Vascular Medicine, University Medical Centre Utrecht (UMCU), Utrecht, The Netherlands.
Abstract
OBJECTIVES: The aim of the study was to compare the predictions of five popular cardiovascular disease (CVD) risk prediction models, namely the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) model, the Framingham Heart Study (FHS) coronary heart disease (FHS-CHD) and general CVD (FHS-CVD) models, the American Heart Association (AHA) atherosclerotic cardiovascular disease risk score (ASCVD) model and the Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) model. METHODS: A cross-sectional design was used to compare the cumulative CVD risk predictions of the models. Furthermore, the predictions of the general CVD models were compared with those of the HIV-specific D:A:D model using three categories (< 10%, 10-20% and > 20%) to categorize the risk and to determine the degree to which patients were categorized similarly or in a higher/lower category. RESULTS: A total of 997 HIV-infected patients were included in the study: 81% were male and they had a median age of 46 [interquartile range (IQR) 40-52] years, a known duration of HIV infection of 6.8 (IQR 3.7-10.9) years, and a median time on ART of 6.4 (IQR 3.0-11.5) years. The D:A:D, ASCVD and SCORE-NL models gave a lower cumulative CVD risk, compared with that of the FHS-CVD and FHS-CHD models. Comparing the general CVD models with the D:A:D model, the FHS-CVD and FHS-CHD models only classified 65% and 79% of patients, respectively, in the same category as did the D:A:D model. However, for the ASCVD and SCORE-NL models, this percentage was 89% and 87%, respectively. Furthermore, FHS-CVD and FHS-CHD attributed a higher CVD risk to 33% and 16% of patients, respectively, while this percentage was < 6% for ASCVD and SCORE-NL. CONCLUSIONS: When using FHS-CVD and FHS-CHD, a higher overall CVD risk was attributed to the HIV-infected patients than when using the D:A:D, ASCVD and SCORE-NL models. This could have consequences regarding overtreatment, drug-related adverse events and drug-drug interactions.
OBJECTIVES: The aim of the study was to compare the predictions of five popular cardiovascular disease (CVD) risk prediction models, namely the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) model, the Framingham Heart Study (FHS) coronary heart disease (FHS-CHD) and general CVD (FHS-CVD) models, the American Heart Association (AHA) atherosclerotic cardiovascular disease risk score (ASCVD) model and the Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) model. METHODS: A cross-sectional design was used to compare the cumulative CVD risk predictions of the models. Furthermore, the predictions of the general CVD models were compared with those of the HIV-specific D:A:D model using three categories (< 10%, 10-20% and > 20%) to categorize the risk and to determine the degree to which patients were categorized similarly or in a higher/lower category. RESULTS: A total of 997 HIV-infectedpatients were included in the study: 81% were male and they had a median age of 46 [interquartile range (IQR) 40-52] years, a known duration of HIV infection of 6.8 (IQR 3.7-10.9) years, and a median time on ART of 6.4 (IQR 3.0-11.5) years. The D:A:D, ASCVD and SCORE-NL models gave a lower cumulative CVD risk, compared with that of the FHS-CVD and FHS-CHD models. Comparing the general CVD models with the D:A:D model, the FHS-CVD and FHS-CHD models only classified 65% and 79% of patients, respectively, in the same category as did the D:A:D model. However, for the ASCVD and SCORE-NL models, this percentage was 89% and 87%, respectively. Furthermore, FHS-CVD and FHS-CHD attributed a higher CVD risk to 33% and 16% of patients, respectively, while this percentage was < 6% for ASCVD and SCORE-NL. CONCLUSIONS: When using FHS-CVD and FHS-CHD, a higher overall CVD risk was attributed to the HIV-infectedpatients than when using the D:A:D, ASCVD and SCORE-NL models. This could have consequences regarding overtreatment, drug-related adverse events and drug-drug interactions.
Authors: Matthew J Feinstein; Milana Bogorodskaya; Gerald S Bloomfield; Rajesh Vedanthan; Mark J Siedner; Gene F Kwan; Christopher T Longenecker Journal: Curr Cardiol Rep Date: 2016-11 Impact factor: 2.931
Authors: Mark H Kuniholm; Elizabeth Vásquez; Allison A Appleton; Lawrence Kingsley; Frank J Palella; Matthew Budoff; Erin D Michos; Ervin Fox; Deborah Jones; Adaora A Adimora; Igho Ofotokun; Gypsyamber D'souza; Kathleen M Weber; Phyllis C Tien; Michael Plankey; Anjali Sharma; Deborah R Gustafson Journal: AIDS Date: 2022-02-01 Impact factor: 4.632
Authors: Angela M Thompson-Paul; Kenneth A Lichtenstein; Carl Armon; Frank J Palella; Jacek Skarbinski; Joan S Chmiel; Rachel Hart; Stanley C Wei; Fleetwood Loustalot; John T Brooks; Kate Buchacz Journal: Clin Infect Dis Date: 2016-09-09 Impact factor: 9.079
Authors: Felicia C Chow; Asya Lyass; Taylor F Mahoney; Joseph M Massaro; Virginia A Triant; Kunling Wu; Baiba Berzins; Kevin Robertson; Ronald J Ellis; Katherine Tassiopoulos; Babafemi Taiwo; Ralph B D'Agostino Journal: Clin Infect Dis Date: 2020-12-15 Impact factor: 9.079
Authors: Mosepele Mosepele; Linda C Hemphill; Tommy Palai; Isaac Nkele; Kara Bennett; Shahin Lockman; Virginia A Triant Journal: PLoS One Date: 2017-02-24 Impact factor: 3.240