Prevalence of heart failure signs and symptoms in a large primary care population identified through the use of text and data mining of the electronic health record. J Card Fail 2014 Jul;20(7):459-64
Date
04/09/2014Pubmed ID
24709663Pubmed Central ID
PMC4083004DOI
10.1016/j.cardfail.2014.03.008Scopus ID
2-s2.0-84903893026 (requires institutional sign-in at Scopus site) 62 CitationsAbstract
BACKGROUND: The electronic health record (EHR) contains a tremendous amount of data that if appropriately detected can lead to earlier identification of disease states such as heart failure (HF). Using a novel text and data analytic tool we explored the longitudinal EHR of over 50,000 primary care patients to identify the documentation of the signs and symptoms of HF in the years preceding its diagnosis.
METHODS AND RESULTS: Retrospective analysis consisted of 4,644 incident HF cases and 45,981 group-matched control subjects. Documentation of Framingham HF signs and symptoms within encounter notes were carried out with the use of a previously validated natural language processing procedure. A total of 892,805 affirmed criteria were documented over an average observation period of 3.4 years. Among eventual HF cases, 85% had ≥1 criterion within 1 year before their HF diagnosis, as did 55% of control subjects. Substantial variability in the prevalence of individual signs and symptoms were found in both case and control subjects.
CONCLUSIONS: HF signs and symptoms are frequently documented in a primary care population as identified through automated text and data mining of EHRs. Their frequent identification demonstrates the rich data available within EHRs that will allow for future work on automated criterion identification to help develop predictive models for HF.
Author List
Vijayakrishnan R, Steinhubl SR, Ng K, Sun J, Byrd RJ, Daar Z, Williams BA, deFilippi C, Ebadollahi S, Stewart WFAuthor
Rajakrishnan Vijayakrishnan MD Associate Professor in the Medicine department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AgedAged, 80 and over
Case-Control Studies
Cohort Studies
Data Mining
Electronic Health Records
Female
Heart Failure
Humans
Male
Middle Aged
Population Surveillance
Prevalence
Primary Health Care
Retrospective Studies
