Predicting stroke in Asian patients with atrial fibrillation using machine learning: A report from the KERALA-AF registry, with external validation in the APHRS-AF registry
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Yang Chen1, Ying Gue1, Peter Calvert1
1Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom.
Insights
Machine learning models predict stroke risk in atrial fibrillation (AF) patients. While effective in an Indian cohort, external validation suggests a need for ethnic-specific models for better accuracy in diverse Asian populations.
Area of Science:
- Cardiology
- Medical Informatics
- Data Science
Background:
- Atrial fibrillation (AF) is a major stroke risk factor, particularly in South Asians.
- Existing stroke prediction models may not fully capture ethnic-specific risks.
Purpose of the Study:
- To develop and validate a one-year stroke prediction model using machine learning (ML) in a South Asian cohort.
- To assess the generalizability of ML models across different Asian populations.
Main Methods:
- Utilized the KERALA-AF South Asian cohort (2101 patients, 83 strokes) for internal validation.
- Employed Random Forest and Light Gradient Boosting Machine algorithms.
- External validation was conducted using the prospective APHRS-AF multinational Asian registry.
Main Results:
- Random Forest achieved an AUC of 0.821 and G-mean of 0.427 in internal validation.
- Light Gradient Boosting Machine showed the best performance in external validation with AUC of 0.670 and G-mean of 0.083.
- The study is the first to demonstrate ML applicability in an Indian prospective AF cohort.
Conclusions:
- Machine learning shows promise for stroke risk prediction in AF patients.
- Performance variations on external validation highlight the need for ethnic-specific ML models.
- Further research into tailored ML approaches is crucial for improving stroke prevention in diverse Asian populations.
Abstract:
Atrial fibrillation (AF) is a significant risk factor for stroke. Based on the higher stroke associated with AF in the South Asian population, we constructed a one-year stroke prediction model using machine learning (ML) methods in KERALA-AF South Asian cohort. External validation was performed in the prospective APHRS-AF registry. We studied 2101 patients and 83 were to patients with stroke in KERALA-AF registry. The random forest showed the best predictive performance in the internal validation with receiver operator characteristic curve (AUC) and G-mean of 0.821 and 0.427, respectively. In the external validation, the light gradient boosting machine showed the best predictive performance with AUC and G-mean of 0.670 and 0.083, respectively. We report the first demonstration of ML's applicability in an Indian prospective cohort, although the more modest prediction on external validation in a separate multinational Asian registry suggests the need for ethnic-specific ML models.
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