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

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.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1093/eurheartj/ehv476

Publication Info

O'Brien, Emily C, DaJuanicia N Simon, Laine E Thomas, Elaine M Hylek, Bernard J Gersh, Jack E Ansell, Peter R Kowey, Kenneth W Mahaffey, et al. (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.

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

Thomas

Laine Elliott Thomas

Professor of Biostatistics & Bioinformatics

Laine Thomas, PhD, joined the Department of Biostatistics and Bioinformatics and DCRI in 2009.  She serves as Associate Chair for Equity, Diversity and Inclusion within the Department of Biostatistics and Bioinformatics and Deputy Director of Data Science and Biostatistics at the Duke Clinical Research Institute.  She is a leader in study design and development of methods for observational and pragmatic studies, with over 240 peer reviewed clinical and methodological publications arising from scientific collaboration in the therapeutic areas of cardiovascular disease, diabetes, uterine fibroids and SARS-CoV-2 virus. She led the statistical teams on the HERO COVID-19, ORBIT-AF I & II, ACTION-CMS, CHAMP-HF, and COMPARE-UF clinical registries and secondary analyses of the NAVIGATOR and ARISTOTLE clinical trials. She has served as a primary investigator and co-investigator on numerous methodological studies with funding from NIH, AHRQ, PCORI and Burroughs Wellcome Fund, addressing observational treatment comparisons, time-varying treatments, heterogeneity of treatment effects, and randomized trials augmented by synthetic controls from real world data.      

Pencina

Michael J Pencina

Professor of Biostatistics & Bioinformatics

Michael J. Pencina, PhD
Chief Data Scientist, Duke Health
Vice Dean for Data Science
Director, Duke AI Health
Professor, Biostatistics & Bioinformatics
Duke University School of Medicine

Michael J. Pencina, PhD, is Duke Health's chief data scientist and serves as vice dean for data science, director of Duke AI Health, and professor of biostatistics and bioinformatics at the Duke University School of Medicine. His work bridges the fields of data science, health care, and AI, and builds upon Duke’s national leadership in trustworthy AI.

Dr. Pencina partners with key leaders to develop data science strategies for Duke Health that span and connect academic research and clinical care. As vice dean for data science, he develops and implements quantitative science strategies to support the School of Medicine’s missions in education and training, laboratory and clinical science, and data science.

He co-founded and co-chairs Duke Health’s Algorithm-Based Clinical Decision Support (ABCDS) Oversight Committee and serves as co-director of Duke’s Collaborative to Advance Clinical Health Equity (CACHE). He spearheads Duke’s role as a founding partner of the Coalition for Health AI (CHAI) whose mission is to increase trustworthiness of AI by developing guidelines to drive high-quality health care through the adoption of credible, fair, and transparent health AI systems. 

Dr. Pencina is an internationally recognized authority in the evaluation of AI tools and algorithms. Guideline groups rely on his work to advance best practices for the application of algorithms in clinical medicine. He is actively involved in the design, conduct, and analysis of clinical studies with a focus on novel and efficient designs and applications of machine learning for medical decision support. He interacts frequently with investigators from academic and industry institutions as well as regulatory officials from the U.S. Food and Drug Administration.

Widely noted as an expert on risk prediction models, Dr. Pencina has authored or co-authored 400 peer-reviewed publications that have been cited over 111,000 times. Thomson Reuters/Clarivate Analytics has recognized him as a “highly cited researcher” in clinical medicine from 2014-2021 and social sciences from 2014-2022. He serves as deputy editor for statistics at JAMA-Cardiology and associate editor for Statistics in Medicine.

Dr. Pencina joined the Duke University faculty in 2013, and served as director of biostatistics for the Duke Clinical Research Institute until 2018. Previously, he was an associate professor in the Department of Biostatistics at Boston University and the Framingham Heart Study, and director of statistical consulting at the Harvard Clinical Research Institute. He received his PhD in Mathematics and Statistics from Boston University in 2003 and holds master’s degrees from the University of Warsaw in actuarial mathematics and business culture.

Email: michael.pencina@duke.edu

Web Sites:  medschool.duke.edu; aihealth.duke.edu; https://scholars.duke.edu/person/michael.pencina

Phone:  919.613.9066

Address:  Duke University School of Medicine; 2424 Erwin Road, Suite 903; Durham, NC 27705

 

Piccini

Jonathan Paul Piccini

Professor of Medicine

Jonathan P. Piccini, MD, MHS, FACC, FAHA, FHRS is a clinical cardiac electrophysiologist and Professor of Medicine 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 and lead extraction. His research interests include the development and evaluation of innovative cardiovascular interventions for the treatment heart rhythm disorders. He has served as the chairman for several national and international clinical trials and registries, including the American Heart Association-Get with the Guidelines Atrial Fibrillation program. He is an Associate Editor at JACC: Clinical Electrophysiology and is an elected member of the American Society for Clinical Investigation. Dr. Piccini has more than 550 publications in the field of heart rhythm medicine and has been the recipient of several teaching and mentorship awards.


Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.