Removal of race stratification or the addition of social determinants of health to pooled cohort equations did not improve ASCVD risk prediction model performance in any race-sex subgroup.
Cohort (n=11,638)
Yes
Does removing race stratification or adding social determinants of health improve the performance of ASCVD risk prediction models in adults without baseline ASCVD?
Removal of race stratification or the addition of social determinants of health did not improve the performance of ASCVD risk prediction models in a biracial cohort.
Importance: Use of race-specific risk prediction in clinical medicine is being questioned. Yet, the most commonly used prediction tool for atherosclerotic cardiovascular disease (ASCVD)-pooled cohort risk equations (PCEs)-uses race stratification. Objective: To quantify the incremental value of race-specific PCEs and determine whether adding social determinants of health (SDOH) instead of race improves model performance. Design, Setting, and Participants: Included in this analysis were participants from the biracial Reasons for Geographic and Racial Differences in Stroke (REGARDS) prospective cohort study. Participants were aged 45 to 79 years, without ASCVD, and with low-density lipoprotein cholesterol level of 70 to 189 mg/dL or non-high-density lipoprotein cholesterol level of 100 to 219 mg/dL at baseline during the period of 2003 to 2007. Participants were followed up to 10 years for incident ASCVD, including myocardial infarction, coronary heart disease death, and fatal and nonfatal stroke. Study data were analyzed from July 2022 to February 2023. Main outcome/measures: Discrimination (C statistic, Net Reclassification Index NRI), and calibration (plots, Nam D'Agostino test statistic comparing observed to predicted events) were assessed for the original PCE, then for a set of best-fit, race-stratified equations including the same variables as in the PCE (model C), best-fit equations without race stratification (model D), and best-fit equations without race stratification but including SDOH as covariates (model E). Results: This study included 11 638 participants (mean SD age, 61.8 8.3 years; 6764 female 58.1%) from the REGARDS cohort. Across all strata (Black female, Black male, White female, and White male participants), C statistics did not change substantively compared with model C (Black female, 0.71; 95% CI, 0.68-0.75; Black male, 0.68; 95% CI, 0.64-0.73; White female, 0.77; 95% CI, 0.74-0.81; White male, 0.68; 95% CI, 0.64-0.71), in model D (Black female, 0.71; 95% CI, 0.67-0.75; Black male, 0.68; 95% CI, 0.63-0.72; White female, 0.76; 95% CI, 0.73-0.80; White male, 0.68; 95% CI, 0.65-0.71), or in model E (Black female, 0.72; 95% CI, 0.68-0.76; Black male, 0.68; 95% CI, 0.64-0.72; White female, 0.77; 95% CI, 0.74-0.80; White male, 0.68; 95% CI, 0.65-0.71). Comparing model D with E using the NRI showed a net percentage decline in the correct assignment to higher risk for male but not female individuals. The Nam D'Agostino test was not significant for all race-sex strata in each model series, indicating good calibration in all groups. Conclusions: Results of this cohort study suggest that PCE performed well overall but had poorer performance in both BM and WM participants compared with female participants regardless of race in the REGARDS cohort. Removal of race or the addition of SDOH did not improve model performance in any subgroup.
Ghosh et al. (Wed,) conducted a cohort in Atherosclerotic cardiovascular disease (ASCVD) (n=11,638). Risk prediction models without race stratification or with social determinants of health (SDOH) vs. Race-stratified pooled cohort risk equations (PCEs) was evaluated on Discrimination (C statistic, Net Reclassification Index) and calibration for incident ASCVD. Removal of race stratification or the addition of social determinants of health to pooled cohort equations did not improve ASCVD risk prediction model performance in any race-sex subgroup.