The ORBIT bleeding score: a simple bedside score to assess bleeding risk in atrial fibrillation.
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2015-12-07
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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.
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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.
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Emily O'Brien
Dr. Emily O’Brien is Associate Professor in Population Health Sciences, Associate Professor in Neurology, Core Faculty Member at Duke-Margolis Center for Health Policy, and Co-Director of Population Health Sciences at the Duke Clinical Research Institute. Her research focuses on comparative effectiveness, patient-centered outcomes, and pragmatic health systems research in cardiovascular and pulmonary disease. Her areas of expertise include: Epidemiology, Pragmatic Clinical Trials, and Clinical Decision Sciences. Dr. O’Brien received her PhD in Epidemiology from the University of North Carolina in Chapel Hill. As principal investigator for projects funded by the FDA, NIH, and PCORI, she has extensive experience working with diverse data sources including registries, epidemiologic cohorts, electronic health records, and administrative claims data. Dr. O’Brien teaches Analytic Methods in the Department of Population Health Sciences PhD program and has co-authored over 160 manuscripts in peer-reviewed journals on topics ranging from epidemiologic methods, comparative effectiveness, and pragmatic clinical trials. She is an associate editor for Circulation: Cardiovascular Quality and Outcomes, Chair of the AHA QCOR Scientific & Clinical Education Lifelong Learning Committee, social media editor for the Journal of the American Heart Association, and a fellow of the American Heart Association.
Laine Elliott Thomas
Laine Thomas, PhD is a Professor and Vice Chair of 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.
Michael J Pencina
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, contributing to Duke’s national leadership in trustworthy health 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-leads the national Coalition for Health AI (CHAI), a multi-stakeholder effort 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. He also spearheaded the establishment 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).
Dr. Pencina is an internationally recognized authority in the evaluation of AI algorithms. Guideline groups rely on his work to advance best practices for the application of clinical decision support tools in health delivery. He interacts frequently with investigators from academic and industry institutions as well as government officials. Since 2014, he has been acknowledged annually by Thomson Reuters/Clarivate Analytics as one of the world’s "highly cited researchers" in clinical medicine and social sciences, with over 400 publications cited over 100,000 times. He serves as a deputy editor for statistics at JAMA-Cardiology.
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
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