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Improved risk stratification of patients with atrial fibrillation: an integrated GARFIELD-AF tool for the prediction of mortality, stroke and bleed in patients with and without anticoagulation

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  • 05/14/2025
Type of Material
Authors
    Keith A. A. Fox, University of EdinburghJoseph E. Lucas, Duke UniversityKaren S. Pieper, Duke Clinical Research InstituteJean-Pierre Bassand, Thrombosis Research InstituteA. John Camm, St George’s University LondonDavid A. Fitzmaurice, University of WarwickSamuel Z. Goldhaber, Brigham and Women’s HospitalShinya Goto, Tokai UniversitySylvia Haas, Technical University of MunichWerner Hacke, University of HeidelbergGloria Kayani, Thrombosis Research InstituteAli Oto, Hacettepe UniversityLorenzo G. Mantovani, University of Milano-BicoccaFrank Misselwitz, Bayer AGJonathan P. Piccini, Duke Clinical Research InstituteAlexander G. G. Turpie, McMaster UniversityFreek W. A. Verheught, Onze Lieve Vrouwe GasthuisAjay K. Kakakr, Thrombosis Research InstitutePeter Wilson, Emory University
Language
  • English
Date
  • 2017-12-21
Publisher
  • BMJ Journals
Publication Version
Copyright Statement
  • © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017
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Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 7
Issue
  • 12
Grant/Funding Information
  • This work was supported by an unrestricted research grant from Bayer AG, Berlin, Germany, to TRI, London, UK, which sponsors the GARFIELD-AF registry.
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Abstract
  • Objectives To provide an accurate, web-based tool for stratifying patients with atrial fibrillation to facilitate decisions on the potential benefits/risks of anticoagulation, based on mortality, stroke and bleeding risks. Design The new tool was developed, using stepwise regression, for all and then applied to lower risk patients. C-statistics were compared with CHA2DS2-VASc using 30-fold cross-validation to control for overfitting. External validation was undertaken in an independent dataset, Outcome Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF). Participants Data from 39 898 patients enrolled in the prospective GARFIELD-AF registry provided the basis for deriving and validating an integrated risk tool to predict stroke risk, mortality and bleeding risk. Results The discriminatory value of the GARFIELD-AF risk model was superior to CHA2DS2-VASc for patients with or without anticoagulation. C-statistics (95% CI) for all-cause mortality, ischaemic stroke/systemic embolism and haemorrhagic stroke/major bleeding (treated patients) were: 0.77 (0.76 to 0.78), 0.69 (0.67 to 0.71) and 0.66 (0.62 to 0.69), respectively, for the GARFIELD-AF risk models, and 0.66 (0.64–0.67), 0.64 (0.61–0.66) and 0.64 (0.61–0.68), respectively, for CHA2DS2-VASc (or HAS-BLED for bleeding). In very low to low risk patients (CHA2DS2-VASc 0 or 1 (men) and 1 or 2 (women)), the CHA2DS2-VASc and HAS-BLED (for bleeding) scores offered weak discriminatory value for mortality, stroke/systemic embolism and major bleeding. C-statistics for the GARFIELD-AF risk tool were 0.69 (0.64 to 0.75), 0.65 (0.56 to 0.73) and 0.60 (0.47 to 0.73) for each end point, respectively, versus 0.50 (0.45 to 0.55), 0.59 (0.50 to 0.67) and 0.55 (0.53 to 0.56) for CHA2DS2-VASc (or HAS-BLED for bleeding). Upon validation in the ORBIT-AF population, C-statistics showed that the GARFIELD-AF risk tool was effective for predicting 1-year all-cause mortality using the full and simplified model for all-cause mortality: C-statistics 0.75 (0.73 to 0.77) and 0.75 (0.73 to 0.77), respectively, and for predicting for any stroke or systemic embolism over 1 year, C-statistics 0.68 (0.62 to 0.74). Conclusions Performance of the GARFIELD-AF risk tool was superior to CHA2DS2-VASc in predicting stroke and mortality and superior to HAS-BLED for bleeding, overall and in lower risk patients. The GARFIELD-AF tool has the potential for incorporation in routine electronic systems, and for the first time, permits simultaneous evaluation of ischaemic stroke, mortality and bleeding risks.
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Research Categories
  • Health Sciences, Medicine and Surgery

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