Risk Prediction for Atherosclerotic Cardiovascular Disease With and Without Race Stratification

Author:

Ghosh Arnab K.1,Venkatraman Sara12,Nanna Michael G.3,Safford Monika M.1,Colantonio Lisandro D.4,Brown Todd M.5,Pinheiro Laura C.1,Peterson Eric D.6,Navar Ann Marie6,Sterling Madeline R.1,Soroka Orysya1,Nahid Musarrat1,Banerjee Samprit7,Goyal Parag1

Affiliation:

1. Department of Medicine, Weill Cornell Medical College, Cornell University, New York, New York

2. Department of Statistics and Data Science, Cornell University, New York, New York

3. Department of Internal Medicine, Section of Cardiovascular Medicine, Yale University School of Medicine, New Haven, Connecticut

4. Department of Epidemiology, University of Alabama at Birmingham, Birmingham

5. Division of Cardiovascular Disease, University of Alabama at Birmingham, Birmingham

6. Division of Cardiology, UT Southwestern Medical Center, Dallas, Texas

7. Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, New York

Abstract

ImportanceUse 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.ObjectiveTo 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 ParticipantsIncluded 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/measuresDiscrimination (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).ResultsThis 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.ConclusionsResults 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.

Publisher

American Medical Association (AMA)

Subject

Cardiology and Cardiovascular Medicine

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