Cardiovascular Disease Risk Prediction in the HIV Outpatient Study

Area of Science:

• Cardiology
• Infectious Diseases
• Epidemiology

Background:

• Cardiovascular disease (CVD) risk prediction tools are frequently applied to populations for which they were not originally developed, especially when validated tools for specific subpopulations are scarce.
• This practice is common in human immunodeficiency virus (HIV)-infected populations, where CVD is a significant concern.

Purpose of the Study:

• To evaluate the performance of commonly used CVD risk prediction models in a cohort of HIV-infected adults.
• To compare the accuracy of general population CVD risk scores (Framingham Risk Score [FRS], Pooled Cohort Equations [PCEs], Systematic COronary Risk Evaluation [SCORE]) against a model developed in HIV-infected individuals (Data Collection on Adverse Effects of Anti-HIV Drugs [D:A:D] study equation).

Main Methods:

• The study utilized data from 2283 HIV-infected adults in the HIV Outpatient Study (HOPS).
• Performance was assessed using C-statistics for discrimination and the ratio of expected to observed events (E/O) with Hosmer-Lemeshow P values for calibration.
• Three general CVD risk models (FRS, PCEs, SCORE) and one HIV-specific model (D:A:D) were evaluated.

Main Results:

• Over 15,056 person-years, 195 (8.5%) participants experienced a CVD event.
• The FRS demonstrated moderate discrimination and good calibration (C-statistic: 0.66, E/O: 1.01).
• PCE and D:A:D showed good discrimination but underestimated risk (C-statistics: 0.71-0.72, E/O: 0.80-0.88), while SCORE performed poorly (C-statistic: 0.59).

Conclusions:

• Among the evaluated models, only the FRS accurately estimated CVD risk in this HIV-infected cohort.
• General population CVD risk scores like PCE and D:A:D tended to underestimate risk in HIV-infected individuals.
• While these models can help stratify risk, they may miss a significant number of HIV-infected persons who could benefit from intensified CVD management.