A Natural Language Processing-Based Approach for Identifying Hospitalizations for Worsening Heart Failure Within an Integrated Health Care Delivery System

Key Points Question What are the overall burden and temporal trends in the rate of hospitalizations for worsening heart failure (WHF)? Findings This cohort study of 118 002 patients found that applying rigorous and prespecified diagnostic criteria to electronic health record data was associated with a more than 2-fold increase in the number of hospitalizations for WHF identified compared with estimates using a principal discharge diagnosis alone. There has been a gradual increase in the rate of hospitalizations for WHF over time, with a more noticeable increase observed among patients with heart failure with a preserved ejection fraction. Meaning These findings suggest that population temporal trends based on a principal hospital discharge diagnosis of heart failure may underreport the increasing burden of hospitalizations for WHF, particularly among those with heart failure with a preserved ejection fraction, compared with a comprehensive approach using structured and unstructured electronic health record data.
Importance The current understanding of epidemiological mechanisms and temporal trends in hospitalizations for worsening heart failure (WHF) is based on claims and national reporting databases. However, these data sources are inherently limited by the accuracy and completeness of diagnostic coding and/or voluntary reporting. Objective To assess the overall burden of and temporal trends in the rate of hospitalizations for WHF. Design, Setting, and Participants This cohort study, performed from January 1, 2010, to December 31, 2019, used electronic health record (EHR) data from a large integrated health care delivery system. Exposures Calendar year trends. Main Outcomes and Measures Hospitalizations for WHF (ie, excluding observation stays) were defined as 1 symptom or more, 2 objective findings or more including 1 sign or more, and 2 doses or more of intravenous loop diuretics and/or new hemodialysis or continuous kidney replacement therapy. Symptoms and signs were identified using natural language processing (NLP) algorithms applied to EHR data. Results The study population was composed of 118 002 eligible patients experiencing 287 992 unique hospitalizations (mean [SD] age, 75.6 [13.1] years; 147 203 [51.1%] male; 1655 [0.6%] American Indian or Alaska Native, 28 451 [9.9%] Asian or Pacific Islander, 34 903 [12.1%] Black, 23 452 [8.1%] multiracial, 175 840 [61.1%] White, and 23 691 [8.2%] unknown), including 65 357 with a principal discharge diagnosis and 222 635 with a secondary discharge diagnosis of HF. The study population included 59 868 patients (20.8%) with HF with a reduced ejection fraction (HFrEF) (
This cohort study uses rule-based natural language processing algorithms applied to electronic health record data to examine the overall burden of and temporal trends in the rate of hospitalizations for worsening heart failure overall and by the degree of systolic dysfunction.