RT Journal Article
SR Electronic
T1 Ascertaining Framingham heart failure phenotype from inpatient electronic health record data using natural language processing: a multicentre Atherosclerosis Risk in Communities (ARIC) validation study
JF BMJ Open
JO BMJ Open
FD British Medical Journal Publishing Group
SP e047356
DO 10.1136/bmjopen-2020-047356
VO 11
IS 6
A1 Moore, Carlton R
A1 Jain, Saumya
A1 Haas, Stephanie
A1 Yadav, Harish
A1 Whitsel, Eric
A1 Rosamand, Wayne
A1 Heiss, Gerardo
A1 Kucharska-Newton, Anna M
YR 2021
UL http://bmjopen.bmj.com/content/11/6/e047356.abstract
AB Objectives Using free-text clinical notes and reports from hospitalised patients, determine the performance of natural language processing (NLP) ascertainment of Framingham heart failure (HF) criteria and phenotype.Study design A retrospective observational study design of patients hospitalised in 2015 from four hospitals participating in the Atherosclerosis Risk in Communities (ARIC) study was used to determine NLP performance in the ascertainment of Framingham HF criteria and phenotype.Setting Four ARIC study hospitals, each representing an ARIC study region in the USA.Participants A stratified random sample of hospitalisations identified using a broad range of International Classification of Disease, ninth revision, diagnostic codes indicative of an HF event and occurring during 2015 was drawn for this study. A randomly selected set of 394 hospitalisations was used as the derivation dataset and 406 hospitalisations was used as the validation dataset.Intervention Use of NLP on free-text clinical notes and reports to ascertain Framingham HF criteria and phenotype.Primary and secondary outcome measures NLP performance as measured by sensitivity, specificity, positive-predictive value (PPV) and agreement in ascertainment of Framingham HF criteria and phenotype. Manual medical record review by trained ARIC abstractors was used as the reference standard.Results Overall, performance of NLP ascertainment of Framingham HF phenotype in the validation dataset was good, with 78.8%, 81.7%, 84.4% and 80.0% for sensitivity, specificity, PPV and agreement, respectively.Conclusions By decreasing the need for manual chart review, our results on the use of NLP to ascertain Framingham HF phenotype from free-text electronic health record data suggest that validated NLP technology holds the potential for significantly improving the feasibility and efficiency of conducting large-scale epidemiologic surveillance of HF prevalence and incidence.Data are available upon reasonable request. All raw data for the study will be available upon request.