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<p>Traditional stroke risk assessment in atrial fibrillation relies on clinical scoring tools such as CHA₂DS₂-VASc, which do not capture the full spectrum of individual thromboembolic risk. Contemporary trials have further challenged this traditional approach, highlighting the importance of atrial fibrillation burden and patient-specific factors in guiding therapy. Advanced echocardiographic markers, including left atrial volume index, strain, and appendage morphology, correlate with atrial remodeling, while biomarkers reflecting cardiac stress and inflammation may further refine risk stratification. These multimodal tools offer a path toward more precise “de-risking” strategies and may help identify patients in whom the net benefit of anticoagulation warrants further individualized assessment. Integrating structural, functional, biomarker, and AI-enabled data into stroke prevention models represents a meaningful step toward personalized care in atrial fibrillation management.</p>

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De-risking Atrial Fibrillation: Refining Anticoagulation Decision-Making

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月23日

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  • Navid Radfar,
  • Dhir Gala,
  • Harsh Singh,
  • Juan Bello,
  • Joseph Allencherril

摘要

Traditional stroke risk assessment in atrial fibrillation relies on clinical scoring tools such as CHA₂DS₂-VASc, which do not capture the full spectrum of individual thromboembolic risk. Contemporary trials have further challenged this traditional approach, highlighting the importance of atrial fibrillation burden and patient-specific factors in guiding therapy. Advanced echocardiographic markers, including left atrial volume index, strain, and appendage morphology, correlate with atrial remodeling, while biomarkers reflecting cardiac stress and inflammation may further refine risk stratification. These multimodal tools offer a path toward more precise “de-risking” strategies and may help identify patients in whom the net benefit of anticoagulation warrants further individualized assessment. Integrating structural, functional, biomarker, and AI-enabled data into stroke prevention models represents a meaningful step toward personalized care in atrial fibrillation management.