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Cardiovascular Risk Estimation Is Suboptimal in People With HIV
Virginia A Triant1,2,3,4, Asya Lyass5, Leo B Hurley6
1Division of General Internal Medicine Massachusetts General Hospital Boston MA.
Insights
Established cardiovascular disease (CVD) risk prediction tools often underestimate risk in people with HIV. New HIV-specific and sex-specific models are needed to improve CVD risk assessment and care for aging individuals with HIV.
Area of Science:
- Cardiology
- Infectious Diseases
- Epidemiology
Background:
- Established cardiovascular disease (CVD) risk prediction functions may not accurately assess risk in people with HIV.
- This study evaluated the performance of three common CVD risk prediction functions in two distinct HIV cohorts.
Purpose of the Study:
- To assess the accuracy of existing CVD risk prediction functions in people with HIV.
- To identify potential sex-based differences in the performance of these functions.
Main Methods:
- Utilized data from the Mass General Brigham and Kaiser Permanente Northern California HIV cohorts.
- Calculated CVD risk scores using the American College of Cardiology/American Heart Association (ACC/AHA) atherosclerotic CVD function, Framingham Heart Study (FHS) hard coronary heart disease function, and FHS hard CVD function.
- Assessed model performance through discrimination (c-statistics) and calibration, comparing predicted to observed risks stratified by sex.
Main Results:
- Across 9412 participants, 1.7% experienced a coronary heart disease event and 3.3% experienced a CVD event.
- All three functions generally underestimated CVD risk in women.
- In men, two functions underestimated risk, while one overestimated it. Calibration was poor for women with one function and for men with all functions.
- Discrimination was good for women (c-statistics 0.78-0.90) and moderate for men (c-statistics 0.71-0.72).
Conclusions:
- Existing CVD risk prediction functions tend to underestimate risk in people with HIV.
- Significant differences in model performance by sex highlight the necessity for both HIV-specific and sex-specific risk prediction tools.
- Developing tailored CVD risk prediction models for people with HIV is crucial for enhancing the care of aging individuals living with HIV.
Background:
Established cardiovascular disease (CVD) risk prediction functions may not accurately predict CVD risk in people with HIV. We assessed the performance of 3 CVD risk prediction functions in 2 HIV cohorts.
Methods And Results:
CVD risk scores were calculated in the Mass General Brigham and Kaiser Permanente Northern California HIV cohorts, using the American College of Cardiology/American Heart Association atherosclerotic CVD function, the FHS (Framingham Heart Study) hard coronary heart disease function and the Framingham Heart Study hard CVD function. Outcomes were myocardial infarction or coronary death for FHS hard coronary heart disease function; and myocardial infarction, stroke, or coronary death for American College of Cardiology/American Heart Association and FHS hard CVD function. We calculated regression coefficients and assessed discrimination and calibration by sex; predicted to observed risk of outcome was also compared. In the combined cohort of 9412, 158 (1.7%) had a coronary heart disease event, and 309 (3.3%) had a CVD event. Among women, CVD risk was generally underestimated by all 3 risk functions. Among men, CVD risk was underestimated by the American College of Cardiology/American Heart Association and FHS hard CVD function, but overestimated by the FHS hard coronary heart disease function. Calibration was poor for women using the FHS hard CVD function and for men using all functions. Discrimination in all functions was good for women (c-statistics ranging from 0.78 to 0.90) and moderate for men (c-statistics ranging from 0.71 to 0.72).
Conclusions:
Established CVD risk prediction functions generally underestimate risk in people with HIV. Differences in model performance by sex underscore the need for both HIV-specific and sex-specific functions. Development of CVD risk prediction models tailored to HIV will enhance care for aging people with HIV.
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