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.
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.


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.
Department of Cardiology, Geisinger Medical Center, Danville, Pennsylvania; Center for Health Research, Geisinger Medical Center, Danville, Pennsylvania.
Rajakrishnan
R
Vijayakrishnan
Department of Cardiology, Geisinger Medical Center, Danville, Pennsylvania; Center for Health Research, Geisinger Medical Center, Danville, Pennsylvania; Scripps Translational Science Institute, La Jolla, California. Electronic address: steinhub@scripps.edu.
Steven R
SR
Steinhubl
T. J. Watson Research Center, IBM, Hawthorne, New York.
Kenney
K
Ng
T. J. Watson Research Center, IBM, Hawthorne, New York; Georgia Institute of Technology, Atlanta, Georgia.
Jimeng
J
Sun
T. J. Watson Research Center, IBM, Hawthorne, New York.
Roy J
RJ
Byrd
Department of Cardiology, Geisinger Medical Center, Danville, Pennsylvania; Center for Health Research, Geisinger Medical Center, Danville, Pennsylvania.
Zahra
Z
Daar
Department of Cardiology, Geisinger Medical Center, Danville, Pennsylvania; Center for Health Research, Geisinger Medical Center, Danville, Pennsylvania.
Brent A
BA
Williams
University of Maryland, Baltimore, Maryland.
Christopher
C
deFilippi
T. J. Watson Research Center, IBM, Hawthorne, New York.
Shahram
S
Ebadollahi
Sutter Health, Concord, California.
Walter F
WF
Stewart
D000368:Aged; D000369:Aged, 80 and over; D016022:Case-Control Studies; D015331:Cohort Studies; D057225:Data Mining / Q000379:methods / Q000706:statistics & numerical data*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D006333:Heart Failure / Q000175:diagnosis* / Q000453:epidemiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011159:Population Surveillance / Q000379:methods; D015995:Prevalence; D011320:Primary Health Care / Q000379:methods; D012189:Retrospective Studies
Heart failure; electronic health records; natural language processing