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.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Clinical Investigator Award (CIA) (K08)
Project #
5K08HL136850-04
Application #
9899862
Study Section
NHLBI Mentored Clinical and Basic Science Review Committee (MCBS)
Program Officer
Huang, Li-Shin
Project Start
2017-04-01
Project End
2021-03-31
Budget Start
2020-04-01
Budget End
2021-03-31
Support Year
4
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Utah
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
009095365
City
Salt Lake City
State
UT
Country
United States
Zip Code
84112
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