Atrial fibrillation (AF) treatment often includes drug therapy with oral anticoagulants (OAC) to prevent stroke. Bleeding, however, is a common complication of these drugs, affecting up to one in four patients. The Center for Medicare and Medicaid Services recently prioritized OAC-related drug safety as a key quality measure. Currently, however, no method exists to accurately identify bleeding events and severity in large populations. Prior methods use diagnoses codes, which lack sensitivity and clinical detail, or manual chart review, which cannot be implemented in large populations. The proposed research aims address this knowledge gap by applying a natural language processing (NLP)-based approach to identify bleeding events and classify severity in a real-world AF population. The tools will be validated in patients treated at a different institution, to ensure reproducibility across provider settings. In addition, we will apply the bleeding classification tool to evaluate the association between bleeding severity and mortality. Dr. Shah is an emerging young investigator whose career development plan is focused on acquiring the biomedical informatics skills to needed to accurately identify and reduce patient harm. Her training plan focuses on learning core competencies in natural language processing, with the goal of turning the wealth of data in the electronic medical record into useable knowledge. She will combine mentorship from established experts and targeted coursework to acquire skills in biomedical informatics, data science, advanced analytic methods, and research leadership. Completion of these research and training aims will create a platform for future R01 proposals by: (i) enabling safety focused comparative effectiveness research in AF (ii) setting the stage to identify bleeding complications in other cardiovascular diseases and (iii) developing a skill set that allows leadership of a multidisciplinary research team. Through this career development plan, Dr. Shah will build upon her prior training in clinical cardiology and research methodology and lay a strong foundation for a high impact research career.

Public Health Relevance

Atrial fibrillation affects six million US adults, and the prevalence is expected to double by 2030. As new drug and device treatments for stroke prevention emerge, patients and providers increasingly need accurate methods to monitor and improve patient safety.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Clinical Investigator Award (CIA) (K08)
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Special Emphasis Panel (MCBS (JA))
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Huang, Li-Shin
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University of Utah
Internal Medicine/Medicine
Schools of Medicine
Salt Lake City
United States
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Shah, Rashmee U; Matheny, Michael E (2018) Data and Information in the Sea of Electronic Health Records. Circ Cardiovasc Qual Outcomes 11:e005247
Mathews, Robin; Wang, William; Kaltenbach, Lisa A et al. (2018) Hospital Variation in Adherence Rates to Secondary Prevention Medications and the Implications on Quality. Circulation 137:2128-2138
Cheng, Yan; Shao, Yijun; Weir, Charlene R et al. (2017) Predicting Adverse Outcomes in Heart Failure Patients Using Different Frailty Status Measures. Stud Health Technol Inform 245:327-331
Shah, Rashmee U; Rumsfeld, John S (2017) Big Data in Cardiology. Eur Heart J 38:1865-1867