Comparison of ASCVD Risk Prediction Models in STEMI: Insights From a South Asian Cohort.

BACKGROUND

Cardiovascular (CV) risk prediction models guide primary prevention, yet most were derived in Western populations and may not generalize to the South Asian population.

OBJECTIVES

To compare risk classification, agreement, and discriminative patterns of major atherosclerotic cardiovascular disease (ASCVD) risk prediction models in first acute myocardial infarction (AMI).

METHODS

In this retrospective analysis, 4,975 consecutive patients aged 40 to 79 years presenting with first AMI were evaluated. Pre-event demographics, clinical characteristics, and laboratory data were extracted from outpatient records. Ten-year pre-event CV risk was calculated using 5 prediction models: Framingham Risk Score (FRS), American College of Cardiology/American Heart Association ASCVD 2013, World Health Organization (WHO) CVD charts, Joint British Societies' cardiovascular risk score version 3 (JBS-3), and PREVENT equations. Patients were categorized as low (<7.5%), intermediate (7.5% to <20%), or high (≥20%) risk. Statistical analyses included Pearson correlation, intraclass correlation, weighted Cohen's kappa, and repeated-measures generalized linear modeling with Bonferroni-adjusted pairwise comparisons.

RESULTS

PREVENT classified 19.8% of patients as high risk, compared with 20.2% using FRS, 15.0% using WHO charts, 11.7% using JBS-3, and 12.3% using ASCVD 2013. PREVENT demonstrated the widest risk distribution (0.2%-91%). Agreement between PREVENT and other models was poor (κ = 0.228; 95% CI: 0.204-0.252 with ASCVD, κ = 0.311; 95% CI: 0.288-0.33 with FRS, and κ = 0.063; 95% CI: 0.038-0.088 with WHO), despite moderate positive correlations.

CONCLUSIONS

Major CV risk algorithms classify Indian AMI patients very differently, with substantial potential for misclassification. Although PREVENT and FRS showed nearly similar classification of high risk, 80% of patients were still not identified as high risk before their event, underscoring the need for South Asia-specific risk score development and validation and alternative preventive strategies.