Literature DB >> 24264434

Bleeding risk prediction models in atrial fibrillation.

Isac C Thomas1, Matthew J Sorrentino.   

Abstract

Novel, nonvitamin K antagonist oral anticoagulants (OACs) have demonstrated similar or superior efficacy to warfarin for ischemic stroke prevention in patients with atrial fibrillation (AF). As the prevalence of AF rises in a growing elderly population, these agents are becoming central to the routine practice of clinicians caring for these patients. Though the benefits are clear, the decision to treat the elderly patient with AF with long-term oral OACs is often a dilemma for the clinician mindful of the risk of major bleeding. Several bleeding risk prediction models have been created to help the clinician identify patients for whom the risk of bleeding is high, and would potentially outweigh the benefits of OAC therapy. In this review, we discuss the features of 8 bleeding risk prediction models, including the recently described HEMORR2HAGES, HAS-BLED, and ATRIA models, and approaches to assessing bleeding risk in clinical practice.

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Year:  2014        PMID: 24264434     DOI: 10.1007/s11886-013-0432-9

Source DB:  PubMed          Journal:  Curr Cardiol Rep        ISSN: 1523-3782            Impact factor:   2.931


  36 in total

1.  Major bleeding in outpatients treated with warfarin: incidence and prediction by factors known at the start of outpatient therapy.

Authors:  C S Landefeld; L Goldman
Journal:  Am J Med       Date:  1989-08       Impact factor: 4.965

2.  Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation.

Authors:  B F Gage; A D Waterman; W Shannon; M Boechler; M W Rich; M J Radford
Journal:  JAMA       Date:  2001-06-13       Impact factor: 56.272

3.  Clinical classification schemes for predicting hemorrhage: results from the National Registry of Atrial Fibrillation (NRAF).

Authors:  Brian F Gage; Yan Yan; Paul E Milligan; Amy D Waterman; Robert Culverhouse; Michael W Rich; Martha J Radford
Journal:  Am Heart J       Date:  2006-03       Impact factor: 4.749

4.  Incidence of intracranial hemorrhage in patients with atrial fibrillation who are prone to fall.

Authors:  Brian F Gage; Elena Birman-Deych; Roger Kerzner; Martha J Radford; David S Nilasena; Michael W Rich
Journal:  Am J Med       Date:  2005-06       Impact factor: 4.965

5.  Bleeding risk assessment and management in atrial fibrillation patients. Executive Summary of a Position Document from the European Heart Rhythm Association [EHRA], endorsed by the European Society of Cardiology [ESC] Working Group on Thrombosis.

Authors:  Gregory Y H Lip; Felicita Andreotti; Laurent Fauchier; Kurt Huber; Elaine Hylek; Eve Knight; Deirdre Lane; Marcel Levi; Francisco Marín; Gualtiero Palareti; Paulus Kirchhof
Journal:  Thromb Haemost       Date:  2011-11-02       Impact factor: 5.249

6.  Comparative validation of a novel risk score for predicting bleeding risk in anticoagulated patients with atrial fibrillation: the HAS-BLED (Hypertension, Abnormal Renal/Liver Function, Stroke, Bleeding History or Predisposition, Labile INR, Elderly, Drugs/Alcohol Concomitantly) score.

Authors:  Gregory Y H Lip; Lars Frison; Jonathan L Halperin; Deirdre A Lane
Journal:  J Am Coll Cardiol       Date:  2010-11-24       Impact factor: 24.094

7.  Prediction of the risk of bleeding during anticoagulant treatment for venous thromboembolism.

Authors:  P M Kuijer; B A Hutten; M H Prins; H R Büller
Journal:  Arch Intern Med       Date:  1999-03-08

8.  Prospective evaluation of an index for predicting the risk of major bleeding in outpatients treated with warfarin.

Authors:  R J Beyth; L M Quinn; C S Landefeld
Journal:  Am J Med       Date:  1998-08       Impact factor: 4.965

9.  Antithrombotic therapy in atrial fibrillation: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines (8th Edition).

Authors:  Daniel E Singer; Gregory W Albers; James E Dalen; Margaret C Fang; Alan S Go; Jonathan L Halperin; Gregory Y H Lip; Warren J Manning
Journal:  Chest       Date:  2008-06       Impact factor: 9.410

10.  Bleeding and thromboembolism during anticoagulant therapy: a population-based study in Rochester, Minnesota.

Authors:  M J Gitter; T M Jaeger; T M Petterson; B J Gersh; M D Silverstein
Journal:  Mayo Clin Proc       Date:  1995-08       Impact factor: 7.616

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  7 in total

1.  Predicting Prolonged Stay in the ICU Attributable to Bleeding in Patients Offered Plasma Transfusion.

Authors:  Che Ngufor; Dennis Murphree; Sudhi Upadhyaya; Nageswar Madde; Jyotishman Pathak; Rickey Carter; Daryl Kor
Journal:  AMIA Annu Symp Proc       Date:  2017-02-10

2.  Body mass index predicts major bleeding risks in patients on warfarin.

Authors:  Adedotun A Ogunsua; Sunkaru Touray; Justin K Lui; Tiffany Ip; Jorge V Escobar; Joel Gore
Journal:  J Thromb Thrombolysis       Date:  2015-11       Impact factor: 2.300

3.  Long non-coding RNA expression profile in atrial fibrillation.

Authors:  Zhongbao Ruan; Xiaohua Sun; Haihui Sheng; Li Zhu
Journal:  Int J Clin Exp Pathol       Date:  2015-07-01

4.  Association between activated partial thromboplastin time, age and bleeding events in NVAF patients receiving dabigatran.

Authors:  Qiuyi Ji; Qing Xu; Zi Wang; Xiaoye Li; Qianzhou Lv
Journal:  Eur J Clin Pharmacol       Date:  2018-10-30       Impact factor: 2.953

5.  Importance of time in therapeutic range on bleeding risk prediction using clinical risk scores in patients with atrial fibrillation.

Authors:  José Miguel Rivera-Caravaca; Vanessa Roldán; María Asunción Esteve-Pastor; Mariano Valdés; Vicente Vicente; Gregory Y H Lip; Francisco Marín
Journal:  Sci Rep       Date:  2017-09-21       Impact factor: 4.379

6.  The expression profile analysis of atrial mRNA in rats with atrial fibrillation: the role of IGF1 in atrial fibrosis.

Authors:  Jiangrong Wang; Zhan Li; Juanjuan Du; Jianhua Li; Yong Zhang; Jing Liu; Yinglong Hou
Journal:  BMC Cardiovasc Disord       Date:  2019-02-15       Impact factor: 2.298

Review 7.  Atrial fibrillation and microRNAs.

Authors:  Gaetano Santulli; Guido Iaccarino; Nicola De Luca; Bruno Trimarco; Gianluigi Condorelli
Journal:  Front Physiol       Date:  2014-01-24       Impact factor: 4.566

  7 in total

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