Cargando…

Use of electronic health record data mining for heart failure subtyping

OBJECTIVE: 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...

Descripción completa

Detalles Bibliográficos
Autores principales: Vuori, Matti A., Kiiskinen, Tuomo, Pitkänen, Niina, Kurki, Samu, Laivuori, Hannele, Laitinen, Tarja, Mäntylahti, Sampo, Palotie, Aarno, FinnGen, Niiranen, Teemu J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496250/
https://www.ncbi.nlm.nih.gov/pubmed/37697398
http://dx.doi.org/10.1186/s13104-023-06469-x
_version_ 1785105070040285184
author Vuori, Matti A.
Kiiskinen, Tuomo
Pitkänen, Niina
Kurki, Samu
Laivuori, Hannele
Laitinen, Tarja
Mäntylahti, Sampo
Palotie, Aarno
FinnGen
Niiranen, Teemu J.
author_facet Vuori, Matti A.
Kiiskinen, Tuomo
Pitkänen, Niina
Kurki, Samu
Laivuori, Hannele
Laitinen, Tarja
Mäntylahti, Sampo
Palotie, Aarno
FinnGen
Niiranen, Teemu J.
author_sort Vuori, Matti A.
collection PubMed
description OBJECTIVE: 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). RESULTS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13104-023-06469-x.
format Online
Article
Text
id pubmed-10496250
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-104962502023-09-13 Use of electronic health record data mining for heart failure subtyping Vuori, Matti A. Kiiskinen, Tuomo Pitkänen, Niina Kurki, Samu Laivuori, Hannele Laitinen, Tarja Mäntylahti, Sampo Palotie, Aarno FinnGen Niiranen, Teemu J. BMC Res Notes Research Note OBJECTIVE: 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). RESULTS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13104-023-06469-x. BioMed Central 2023-09-11 /pmc/articles/PMC10496250/ /pubmed/37697398 http://dx.doi.org/10.1186/s13104-023-06469-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Note
Vuori, Matti A.
Kiiskinen, Tuomo
Pitkänen, Niina
Kurki, Samu
Laivuori, Hannele
Laitinen, Tarja
Mäntylahti, Sampo
Palotie, Aarno
FinnGen
Niiranen, Teemu J.
Use of electronic health record data mining for heart failure subtyping
title Use of electronic health record data mining for heart failure subtyping
title_full Use of electronic health record data mining for heart failure subtyping
title_fullStr Use of electronic health record data mining for heart failure subtyping
title_full_unstemmed Use of electronic health record data mining for heart failure subtyping
title_short Use of electronic health record data mining for heart failure subtyping
title_sort use of electronic health record data mining for heart failure subtyping
topic Research Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496250/
https://www.ncbi.nlm.nih.gov/pubmed/37697398
http://dx.doi.org/10.1186/s13104-023-06469-x
work_keys_str_mv AT vuorimattia useofelectronichealthrecorddataminingforheartfailuresubtyping
AT kiiskinentuomo useofelectronichealthrecorddataminingforheartfailuresubtyping
AT pitkanenniina useofelectronichealthrecorddataminingforheartfailuresubtyping
AT kurkisamu useofelectronichealthrecorddataminingforheartfailuresubtyping
AT laivuorihannele useofelectronichealthrecorddataminingforheartfailuresubtyping
AT laitinentarja useofelectronichealthrecorddataminingforheartfailuresubtyping
AT mantylahtisampo useofelectronichealthrecorddataminingforheartfailuresubtyping
AT palotieaarno useofelectronichealthrecorddataminingforheartfailuresubtyping
AT finngen useofelectronichealthrecorddataminingforheartfailuresubtyping
AT niiranenteemuj useofelectronichealthrecorddataminingforheartfailuresubtyping