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The ORBIT bleeding score: a simple bedside score to assess bleeding risk in atrial fibrillation.

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Date
2015-12-07
Authors
O'Brien, Emily C
Simon, DaJuanicia N
Thomas, Laine E
Hylek, Elaine M
Gersh, Bernard J
Ansell, Jack E
Kowey, Peter R
Mahaffey, Kenneth W
Chang, Paul
Fonarow, Gregg C
Pencina, Michael J
Piccini, Jonathan P
Peterson, Eric D
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(13 total)
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Abstract
BACKGROUND: Therapeutic decisions in atrial fibrillation (AF) are often influenced by assessment of bleeding risk. However, existing bleeding risk scores have limitations. OBJECTIVES: We sought to develop and validate a novel bleeding risk score using routinely available clinical information to predict major bleeding in a large, community-based AF population. METHODS: We analysed data from Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF), a prospective registry that enrolled incident and prevalent AF patients at 176 US sites. Using Cox proportional hazards regression, we identified factors independently associated with major bleeding among patients taking oral anticoagulation (OAC) over a median follow-up of 2 years (interquartile range = 1.6-2.5). We also created a numerical bedside risk score that included the five most predictive risk factors weighted according to their strength of association with major bleeding. The predictive performance of the full model, the simple five-item score, and two existing risk scores (hypertension, abnormal renal/liver function, stroke, bleeding history or predisposition, labile INR, elderly, drugs/alcohol concomitantly, HAS-BLED, and anticoagulation and risk factors in atrial fibrillation, ATRIA) were then assessed in both the ORBIT-AF cohort and a separate clinical trial population, Rivaroxaban Once-daily oral direct factor Xa inhibition compared with vitamin K antagonism for prevention of stroke and embolism trial in atrial fibrillation (ROCKET-AF). RESULTS: Among 7411 ORBIT-AF patients taking OAC, the rate of major bleeding was 4.0/100 person-years. The full continuous model (12 variables) and five-factor ORBIT risk score (older age [75+ years], reduced haemoglobin/haematocrit/history of anaemia, bleeding history, insufficient kidney function, and treatment with antiplatelet) both had good ability to identify those who bled vs. not (C-index 0.69 and 0.67, respectively). These scores both had similar discrimination, but markedly better calibration when compared with the HAS-BLED and ATRIA scores in an external validation population from the ROCKET-AF trial. CONCLUSIONS: The five-element ORBIT bleeding risk score had better ability to predict major bleeding in AF patients when compared with HAS-BLED and ATRIA risk scores. The ORBIT risk score can provide a simple, easily remembered tool to support clinical decision making.
Type
Journal article
Subject
Anticoagulants
Atrial fibrillation
Major bleeding
Risk prediction
Aged
Aged, 80 and over
Anticoagulants
Atrial Fibrillation
Female
Hemorrhage
Humans
Male
Middle Aged
Point-of-Care Systems
Prospective Studies
Registries
Risk Assessment
Risk Factors
Stroke
Permalink
https://hdl.handle.net/10161/15004
Published Version (Please cite this version)
10.1093/eurheartj/ehv476
Publication Info
O'Brien, Emily C; Simon, DaJuanicia N; Thomas, Laine E; Hylek, Elaine M; Gersh, Bernard J; Ansell, Jack E; ... Peterson, Eric D (2015). The ORBIT bleeding score: a simple bedside score to assess bleeding risk in atrial fibrillation. Eur Heart J, 36(46). pp. 3258-3264. 10.1093/eurheartj/ehv476. Retrieved from https://hdl.handle.net/10161/15004.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

O'Brien

Emily O'Brien

Associate Professor in Population Health Sciences
I am an epidemiologist and health services researcher at the Duke Clinical Research Institute. My research focuses on comparative effectiveness, patient-centered outcomes, and pragmatic health services research in cardiovascular and pulmonary disease.Areas of expertise: Epidemiology, Health Services Research, and Clinical Decision Sciences
Pencina

Michael J Pencina

Professor of Biostatistics & Bioinformatics
As Vice Dean for Data Science, Dr. Pencina is responsible for developing and implementing quantitative science strategies as they pertain to the education and training, and laboratory, clinical science, and data science missions of the School of Medicine. Dr. Pencina is a Professor of Biostatistics and Bioinformatics at Duke University and Director of Duke AI Health. Previously, he served as Director of Biostatistics at the Duke Clinical Research Institute. Dr. Pencina is an internati
Peterson

Eric David Peterson

Fred Cobb, M.D. Distinguished Professor of Medicine
Dr Peterson is the Fred Cobb Distinguished Professor of Medicine in the Division of Cardiology, a DukeMed Scholar, and the Past Executive Director of the Duke Clinical Research Institute (DCRI), Durham, NC, USA. Dr Peterson is the Principal Investigator of the National Institute of Health, Lung and Blood Institute (NHLBI) Spironolactone Initiation Registry Randomized Interventional Trial in Heart Failure With Preserved Ejection Fraction (SPIRRIT) Trial  He is also the Principal I
This author no longer has a Scholars@Duke profile, so the information shown here reflects their Duke status at the time this item was deposited.
Piccini

Jonathan Paul Piccini Sr.

Associate Professor of Medicine
Jonathan P. Piccini, MD, MHS, FACC, FAHA, FHRS is a clinical cardiac electrophysiologist and Associate Professor of Medicine with Tenure at Duke University Medical Center and the Duke Clinical Research Institute. He is the Director of the Cardiac Electrophysiology section at the Duke Heart Center. His focus is on the care of patients with atrial fibrillation and complex arrhythmias, with particular emphasis on catheter ablation, left atrial appendage occlusion, and lead extraction. His resear
Thomas

Laine Elliott Thomas

Associate Professor of Biostatistics & Bioinformatics
Causal Inference, Heterogeneity of Treatment Effects, Observational Data, Time-varying Treatments, Real World Evidence
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