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The COOL-AF 3-year prediction models for death, ischemic stroke, and major bleeding: a report from the COOL-AF
Satchana Pumprueg1, Gregory Y H Lip2,3,4, Chulaluk Komoltri5
1Division of Cardiology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand.
Insights
New 3-year prediction models for Asian atrial fibrillation (AF) patients accurately estimate risks for death, stroke, and bleeding. These COOL-AF models outperform existing scores, aiding long-term risk stratification.
Area of Science:
- Cardiology
- Epidemiology
- Health Outcomes Research
Background:
- Atrial fibrillation (AF) patients face elevated risks of mortality, ischemic stroke/systemic embolism (SSE), and major bleeding.
- Existing risk prediction models often focus on short-term outcomes and are primarily based on Western populations.
Purpose of the Study:
- To develop and validate 3-year prediction models for death, SSE, and major bleeding in an Asian AF cohort.
- To compare the performance of these new models against established risk scores like GARFIELD, CHA₂DS₂-VASc, and HAS-BLED.
Main Methods:
- Analysis of 3-year data from the prospective, nationwide COOL-AF registry in Thailand, including 3,405 patients with non-valvular AF.
- Development of multivariable Cox proportional hazards models for predicting death, SSE, and major bleeding.
- Assessment of model performance using discrimination (C-statistics), calibration, and reclassification metrics.
Main Results:
- The 3-year COOL-AF models demonstrated good discrimination (C-statistics: 0.728 for death, 0.703 for SSE, 0.700 for bleeding) and calibration.
- COOL-AF models outperformed CHA₂DS₂-VASc for death prediction and HAS-BLED for major bleeding prediction.
- Performance was comparable or superior to GARFIELD models, especially for bleeding risk, with reclassification indices supporting incremental value.
Conclusions:
- The 3-year COOL-AF prediction models offer reliable and well-calibrated risk estimates for death, SSE, and major bleeding in Asian AF patients.
- These models outperform commonly used clinical risk scores, supporting their utility in long-term risk stratification and clinical decision-making for Asian populations.
Abstract:
Patients with atrial fibrillation (AF) are at increased risk of death, ischemic stroke/systemic embolism (SSE), and major bleeding. Most existing risk models focus on short-term outcomes and are derived mainly from Western populations. We aimed to develop and validate 3-year prediction models for these outcomes in an Asian AF cohort and to compare their performance with established risk scores. We analyzed the 3-year data from the prospective, nationwide COOL-AF registry, enrolling patients with non-valvular AF from 27 hospitals in Thailand. Multivariable Cox proportional hazards models were used to derive prediction models for death, SSE, and major bleeding. Model performance was assessed using discrimination (C-statistics), calibration, and reclassification metrics, and compared with GARFIELD, CHA₂DS₂-VASc, and HAS-BLED models. Among 3,405 patients, 380 deaths, 134 SSE events, and 199 major bleeding events occurred over a median follow-up of 35.9 months. The 3-year COOL-AF models showed good discrimination, with C-statistics of 0.728 (0.712-0.743) for death, 0.703 (0.687-0.718) for SSE, and 0.700 (0.685-0.716) for major bleeding, and demonstrated good calibration. Compared with existing scores, the COOL-AF models performed better than CHA₂DS₂-VASc for death and better than HAS-BLED for major bleeding, while showing comparable or superior performance to the GARFIELD models, particularly for bleeding risk. Reclassification indices further supported the incremental value of the COOL-AF models. In conclusion, the 3-year COOL-AF prediction models provide an acceptable levels of prediction, well-calibrated estimates of death, SSE, and major bleeding in Asian patients with AF and outperform commonly used clinical risk scores, supporting their use for long-term risk stratification and clinical decision-making.
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