Predicting Thromboembolic and Bleeding Event Risk in Patients with Non-Valvular Atrial Fibrillation: A Systematic

Ethan D Borre1, Adam Goode1,2, Giselle Raitz1

  • 1Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States.

Insights

For atrial fibrillation (AF) patients, CHADS₂, CHA₂DS₂-VASc, and ABC scores best predict stroke risk, while HAS-BLED best predicts bleeding risk. These tools aid in balancing stroke prevention against bleeding complications.

Area of Science:

  • Cardiology
  • Clinical Risk Assessment
  • Medical Informatics

Background:

  • Atrial fibrillation (AF) is a prevalent arrhythmia increasing stroke risk.
  • Anticoagulant therapy for stroke prevention in AF carries a potential bleeding risk.
  • Clinicians utilize risk stratification tools to balance stroke and bleeding risks for optimal patient management.

Purpose of the Study:

  • To review and summarize existing literature on risk stratification tools for predicting stroke and bleeding in AF patients.
  • To identify the most effective tools for assessing thromboembolic and bleeding risks in the context of AF management.

Main Methods:

  • Systematic literature search of PubMed, Embase, and Cochrane Database of Systematic Reviews (2000-2018).
  • Inclusion of English-language studies evaluating risk prediction tools for AF.
  • Extraction of data on study design, patient characteristics, interventions, outcomes, quality, and applicability by two independent reviewers.

Main Results:

  • Sixty-one studies assessed thromboembolic risk, and 38 assessed bleeding risk.
  • CHADS₂, CHA₂DS₂-VASc, and ABC risk scores demonstrate the strongest evidence for predicting thromboembolic events (moderate strength).
  • The HAS-BLED score shows the strongest evidence for predicting bleeding risk (moderate strength).

Conclusions:

  • CHADS₂, CHA₂DS₂-VASc, and ABC scores are the most effective for predicting stroke events in AF.
  • HAS-BLED is the most effective score for predicting bleeding risk in AF patients.
  • Future research should explore the role of imaging and biomarkers in refining these risk prediction tools.
Abstract

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