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Comparison of ASCVD Risk Prediction Models in STEMI: Insights From a South Asian Cohort
Mohit Dayal Gupta1, Shekhar Kunal2, M P Girish1
1Department of Cardiology, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research, Delhi, India.
Insights
Cardiovascular risk models misclassify South Asian patients, highlighting the need for region-specific tools. Current algorithms show poor agreement and may miss high-risk individuals, necessitating new preventive strategies.
Area of Science:
- Cardiology
- Public Health
- Epidemiology
Background:
- Cardiovascular (CV) risk prediction models are crucial for primary prevention but often lack generalizability to South Asian populations due to derivation in Western cohorts.
- Existing models may not accurately reflect the CV risk profile of diverse ethnic groups, potentially leading to suboptimal preventive strategies.
Purpose of the Study:
- To compare the risk classification, agreement, and discriminative performance of major atherosclerotic cardiovascular disease (ASCVD) risk prediction models in a South Asian population presenting with first acute myocardial infarction (AMI).
Main Methods:
- Retrospective analysis of 4,975 patients (aged 40-79) with first AMI, extracting pre-event data.
- Calculated 10-year CV risk using Framingham Risk Score (FRS), ASCVD 2013, WHO CVD charts, JBS-3, and PREVENT equations.
- Assessed risk categories (low, intermediate, high) and statistical agreement using correlation, intraclass correlation, and Cohen's kappa.
Main Results:
- Models showed varied high-risk classifications, with PREVENT (19.8%) and FRS (20.2%) classifying the highest proportions.
- PREVENT exhibited the widest risk distribution (0.2%-91%), indicating diverse risk levels within the cohort.
- Poor agreement was observed between PREVENT and other models (e.g., Cohen's kappa with ASCVD = 0.228), despite moderate correlations.
Conclusions:
- Major CV risk algorithms demonstrate significant differences in classifying Indian AMI patients, leading to potential misclassification.
- A substantial majority (80%) of patients were not identified as high risk prior to their event, emphasizing limitations of current models.
- There is a critical need for the development and validation of South Asia-specific risk scores and alternative preventive strategies for cardiovascular disease.
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.
