Equations for Predicting the 10-Year Risk of Atherosclerotic Cardiovascular Disease in Population With Obesity.

Atherosclerotic cardiovascular disease (ASCVD) is a significant public health issue; however, current risk prediction models have limitations in populations with obesity. The aim of this study was to develop risk models for predicting ASCVD risk in patients with obesity. Gender-specific equations to predict ASCVD were developed and validated using a cohort of 3,058 participants with obesity (body mass index ≥30 kg/m2) in the ARIC (Atherosclerosis Risk In Communities) study and 1,953 participants in MESA (Multi-Ethnic Study of Atherosclerosis). The new models were called "ASCVD Risk estimation with Obesity-Specific Equations" or AROSE. We also conducted a comparative analysis of our algorithms against the 2008 Framingham risk score (FRS) and pooled cohort equations (PCEs) in both derivation and validation cohorts. In the derivation cohort (ARIC), C-statistics were 0.758 for men and 0.817 for women, demonstrating good discrimination and calibration (calibration chi-square for men = 1.71, P = 0.634; calibration χ2 for women = 0.77, P = 0.857). In the validation cohort (MESA), AROSE outperformed both PCEs and FRS, with C-statistics of 0.707 for men and 0.734 for women, compared to PCEs (0.706 for men, 0.705 for women) and FRS (0.693 for men, 0.703 for women). AROSE also showed significant net reclassification improvements (P < 0.05) and a higher net clinical benefit in decision curve analysis compared to PCEs and FRS. Our equations provided better prediction of 10-year ASCVD risk in individuals with obesity in the MESA cohort, outperforming both PCEs and FRS.