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American Indians (AI) have a high prevalence of diabetes, obesity, cardiovascular disease (CVD), and chronic kidney disease. Inclusion of kidney function and other population-specific characteristics in equations used to predict atherosclerotic CVD (ASCVD) risk may help define risk more accurately in populations with these chronic diseases. We used data from the Strong Heart Study (SHS), a population-based longitudinal cohort study of AI, to modify the American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort ASCVD risk equations and then explored the performance of the new equations in predicting ASCVD in AI. The study included baseline SHS exam data from 4213 individuals between 45 and 75 years of age, collected in 13 communities from 3 geographic areas in the United States and spanning a wide range of tribal backgrounds, with continuous follow-up data from 1989 to 2015. Using SHS data for blood pressure, diabetes, cholesterol, smoking, and renal function, Cox proportional hazard models were developed to predict ASCVD-free time for AI men and women. ASCVD risk in AI calculated using the SHS-modified equations were compared to risk calculated using the ACC/AHA pooled cohort equations for African Americans (AAs) and Whites. Goodness-of-fit measures for ASCVD risk prediction showed that the SHS-modified equations fit the data from the SHS better than the ACC/AHA equations for AAs and Whites. Adjusting risk prediction equations using population data from the SHS and including measures of renal function significantly improved ASCVD risk prediction in our AI cohort. |
