Key result
Machine learning identifies phenotypic clusters of patients with atrial fibrillation to help determine those at greatest risk for morbidity and mortality.
Why the study?
AF is associated with significant morbidity and mortality, requiring better efforts to identify patients at greatest risk who would benefit most from appropriate management.
Key points are not available for this paper at this time.
Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide and is associated with significant morbidity and mortality, from stroke, heart failure, dementia and hospitalisations[1]. As a result, better efforts to identify patients at greatest risk, who would benefit most from appropriate management are needed.
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Essa et al. (2024) studied this question. Machine learning identifies phenotypic clusters of patients with atrial fibrillation to help determine those at greatest risk for morbidity and mortality.
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