Use of electronic health record data mining for heart failure subtyping

To assess whether electronic health record (EHR) data text mining can be used to improve register-based heart failure (HF) subtyping. EHR data of 43,405 individuals from two Finnish hospital biobanks were mined for unstructured text mentions of ejection fraction (EF) and validated against clinical a...

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Bibliographic Details
Published in:BMC research notes 2023-09, Vol.16 (1), p.1-6, Article 208
Main Authors: Vuori, Matti A, Kiiskinen, Tuomo, Pitkänen, Niina, Kurki, Samu, Laivuori, Hannele, Laitinen, Tarja, Mäntylahti, Sampo, Palotie, Aarno, Niiranen, Teemu J
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Biobanks
Cardiovascular disease
Care and treatment
Congestive heart failure
Creatinine
Data mining
Diabetes
Diagnosis
Ejection fraction
Electronic health records
Electronic medical records
Electronic records
Finland
Health
Heart failure
HFrEF
Hospitals
Hypertension
Laboratories
Medical records
Medical research
Medicine, Experimental
Methods
Mortality
Research Note
Risk factors
Text mining
Type 2 diabetes
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Summary:To assess whether electronic health record (EHR) data text mining can be used to improve register-based heart failure (HF) subtyping. EHR data of 43,405 individuals from two Finnish hospital biobanks were mined for unstructured text mentions of ejection fraction (EF) and validated against clinical assessment in two sets of 100 randomly selected individuals. Structured laboratory data was then incorporated for a categorization by HF subtype (HF with mildly reduced EF, HFmrEF; HF with preserved EF, HFpEF; HF with reduced EF, HFrEF; and no HF). In 86% of the cases, the algorithm-identified EF belonged to the correct HF subtype range. Sensitivity, specificity, PPV and NPV of the algorithm were 94-100% for HFrEF, 85-100% for HFmrEF, and 96%, 67%, 53% and 98% for HFpEF. Survival analyses using the traditional diagnosis of HF were in concordance with the algorithm-based ones. Compared to healthy individuals, mortality increased from HFmrEF (hazard ratio [HR], 1.91; 95% confidence interval [CI], 1.24-2.95) to HFpEF (2.28; 1.80-2.88) to HFrEF group (2.63; 1.97-3.50) over a follow-up of 1.5 years. We conclude that quantitative EF data can be efficiently extracted from EHRs and used with laboratory data to subtype HF with reasonable accuracy, especially for HFrEF.
ISSN:1756-0500
1756-0500
DOI:10.1186/s13104-023-06469-x