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

Ethan D. Borre; Adam Goode; Giselle Raitz; Bimal Shah; Angela Lowenstern; Ranee Chatterjee; Lauren Sharan; Nancy M. Allen LaPointe; Roshini Yapa; J. Kelly Davis; Kathryn Lallinger; Robyn Schmidt; Andrzej Kosinski; Sana M. Al-Khatib; Gillian D. Sanders

doi:10.1055/s-0038-1675400

Thrombosis and Haemostasis, Table of Contents

Thromb Haemost 2018; 118(12): 2171-2187
DOI: 10.1055/s-0038-1675400

Stroke, Systemic or Venous Thromboembolism

Georg Thieme Verlag KG Stuttgart · New York

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

Authors

Ethan D. Borre

¹Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States
Adam Goode

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

²Department of Orthopedic Surgery, Duke University School of Medicine, Durham, North Carolina, United States
Giselle Raitz

¹Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States
Bimal Shah

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

³Livongo, Mountain View, California, United States
Angela Lowenstern

⁴Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, United States

⁵Division of Cardiology, Duke University School of Medicine, Durham, North Carolina
Ranee Chatterjee

¹Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States
Lauren Sharan

¹Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States
Nancy M. Allen LaPointe

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

⁶Premier Inc., Charlotte, North Carolina, United States
Roshini Yapa

⁷Department of Medicine, University of Colorado, Aurora, Colorado, United States
J. Kelly Davis

⁸Duke-Margolis Center for Health Policy, Duke University, Durham, North Carolina, United States
Kathryn Lallinger

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

⁴Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, United States

⁹Evidence-Based Practice Center, Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, United States
Robyn Schmidt

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

⁴Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, United States

⁹Evidence-Based Practice Center, Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, United States
Andrzej Kosinski

¹⁰Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, United States
Sana M. Al-Khatib

⁴Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, United States

⁵Division of Cardiology, Duke University School of Medicine, Durham, North Carolina
Gillian D. Sanders

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

⁴Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, United States

⁸Duke-Margolis Center for Health Policy, Duke University, Durham, North Carolina, United States

⁹Evidence-Based Practice Center, Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, United States

Abstract

Background Atrial fibrillation (AF) is a common cardiac arrhythmia that increases the risk of stroke. Medical therapy for decreasing stroke risk involves anticoagulation, which may increase bleeding risk for certain patients. In determining the optimal therapy for stroke prevention for patients with AF, clinicians use tools with various clinical, imaging and patient characteristics to weigh stroke risk against therapy-associated bleeding risk.

Aim This article reviews published literature and summarizes available risk stratification tools for stroke and bleeding prediction in patients with AF.

Methods We searched for English-language studies in PubMed, Embase and the Cochrane Database of Systematic Reviews published between 1 January 2000 and 14 February 2018. Two reviewers screened citations for studies that examined tools for predicting thromboembolic and bleeding risks in patients with AF. Data regarding study design, patient characteristics, interventions, outcomes, quality, and applicability were extracted.

Results Sixty-one studies were relevant to predicting thromboembolic risk and 38 to predicting bleeding risk. Data suggest that CHADS₂, CHA₂DS₂-VASc and the age, biomarkers, and clinical history (ABC) risk scores have the best evidence for predicting thromboembolic risk (moderate strength of evidence for limited prediction ability of each score) and that HAS-BLED has the best evidence for predicting bleeding risk (moderate strength of evidence).

Limitations Studies were heterogeneous in methodology and populations of interest, setting, interventions and outcomes analysed.

Conclusion CHADS₂, CHA₂DS₂-VASc and ABC scores have the best prediction for stroke events, and HAS-BLED provides the best prediction for bleeding risk. Future studies should define the role of imaging tools and biomarkers in enhancing the accuracy of risk prediction tools.

Primary Funding Source Patient-Centered Outcomes Research Institute (PROSPERO #CRD42017069999)

Keywords

non-valvular atrial fibrillation - stroke risk - bleeding risk

Full Text

References

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