Development and Validation of the HIV-CARDIO-PREDICT Score to Estimate the Risk of Cardiovascular Events in HIV-Infected Patients

Importance: Commonly used risk assessment tools for cardiovascular disease might not be accurate for HIV-infected patients.

Objective: We aimed to develop a model to accurately predict the 10-year cardiovascular disease (CV) risk of HIV-infected patients.

Design: In this retrospective cohort study, adult HIV-infected patients seen at Boston Medical Center between March 2012 and January 2017 were divided into model development and validation cohorts.

Setting: Boston Medical Center, a tertiary, academic medical center.

Participants: Adult HIV-infected patients, seen in inpatient and outpatient setting.

Main Outcomes And Measures: We used logistic regression to create a prediction risk model for cardiovascular events using data from the development cohort. Using a point-based risk-scoring system, we summarized the relationship between risk factors and cardiovascular disease (CVD) risk. We then used the area under the receiver operating characteristics curve (AUC) to evaluate model discrimination. Finally, we tested the model using a validation cohort.

Results: 1914 individuals met the inclusion criteria. The model had excellent discrimination for CVD risk [AUC 0.989; (95% CI: 0.986-0.993)] and included the following 11 variables: male sex (95% CI: 2.53-3.99), African American race/ethnicity (95% CI: 1.50-3.13), current age (95% CI: 0.07-0.13), age at HIV diagnosis (95% CI: -0.10-(-0.02)), peak HIV viral load (95% CI: 9.89 × 10-3.00 × 10), nadir CD4 lymphocyte count (95% CI: -0.03-(-0.02)), hypertension (95% CI: 0.20-1.54), hyperlipidemia (95% CI: 3.03-4.60), diabetes (95% CI: 0.61-1.89), chronic kidney disease (95% CI: 1.26-2.62), and smoking (95% CI: 0.12-2.39). The eleven-parameter multiple logistic regression model had excellent discrimination [AUC 0.957; (95% CI: 0.938-0.975)] when applied to the validation cohort.

Conclusions And Relevance: Our novel HIV-CARDIO-PREDICT Score may provide a rapid and accurate evaluation of CV disease risk among HIV-infected patients and inform prevention measures.