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

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 volun...

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Bibliographic Details
Published in:JAMA network open 2021-11, Vol.4 (11), p.e2135152-e2135152
Main Authors: Ambrosy, Andrew P, Parikh, Rishi V, Sung, Sue Hee, Narayanan, Anand, Masson, Rajeev, Lam, Phuong-Quang, Kheder, Kevin, Iwahashi, Alan, Hardwick, Alexander B, Fitzpatrick, Jesse K, Avula, Harshith R, Selby, Van N, Shen, Xian, Sanghera, Navneet, Cristino, Joaquim, Go, Alan S
Format: Article
Language:English
Subjects:
Aged
Algorithms
Cardiology
Cohort Studies
Delivery of Health Care, Integrated - statistics & numerical data
Disease Progression
Electronic health records
Female
Forecasting - methods
Health care delivery
Heart failure
Heart Failure - diagnosis
Heart Failure - physiopathology
Hospitalization
Hospitalization - statistics & numerical data
Humans
Integrated delivery systems
Male
Middle Aged
Natural Language Processing
Online Only
Original Investigation
Prognosis
Trends
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Summary: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. To assess the overall burden of and temporal trends in the rate of hospitalizations for WHF. 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. Calendar year trends. 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. 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) (
ISSN:2574-3805
2574-3805
DOI:10.1001/jamanetworkopen.2021.35152