Use of electronic health record data mining for heart failure subtyping

BMC Research Notes

Research Note

Tarja Laitinen¹  Niina Pitkänen²  Teemu J. Niiranen³  Matti A. Vuori⁴  Sampo Mäntylahti⁵  Tuomo Kiiskinen⁶  Aarno Palotie⁶  Samu Kurki⁷  Hannele Laivuori⁸ 

[1] Administration Center, Tampere University Hospital and University of Tampere, P.O. Box 2000, 33521, Tampere, Finland;Auria Biobank, PO Box 30, Kiinamyllynkatu 10, FI-20520, Turku, Finland;Division of Medicine, University of Turku, Kiinamyllynkatu 10, FI-20520, Turku, Finland;Turku University Hospital, Box 52, Kiinamyllynkatu 4-8, FI-20521, Turku, Finland;Department of Public Health Solutions, Finnish Institute for Health and Welfare, PO Box 30, FI-00271, Helsinki, Finland;Division of Medicine, University of Turku, Kiinamyllynkatu 10, FI-20520, Turku, Finland;Turku University Hospital, Box 52, Kiinamyllynkatu 4-8, FI-20521, Turku, Finland;Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Tukholmankatu 8, Helsinki, Finland;Helsinki Biobank, Haartmaninkatu 3, 00290, Helsinki, Finland;Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Tukholmankatu 8, Helsinki, Finland;Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Tukholmankatu 8, Helsinki, Finland;Auria Biobank, PO Box 30, Kiinamyllynkatu 10, FI-20520, Turku, Finland;Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Tukholmankatu 8, Helsinki, Finland;Centre for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland;Department of Obstetrics and Gynecology, Tampere University Hospital, Tampere, Finland;

关键词: Heart failure;    Ejection fraction;    Data mining;    Text mining;    Electronic health records;    HFrEF;    HFpEF;    HFmrEF;   

DOI  :  10.1186/s13104-023-06469-x

 received in 2022-10-14, accepted in 2023-08-22,  发布年份 2023

来源: Springer

【 摘 要 】

ObjectiveTo 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).ResultsIn 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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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