Clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis
AIM: We aimed to identify subphenotypes among patients with out‐of‐hospital cardiac arrest (OHCA) with initial non‐shockable rhythm by applying machine learning latent class analysis and examining the associations between subphenotypes and neurological outcomes. METHODS: This study was a retrospecti...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9136939/ https://www.ncbi.nlm.nih.gov/pubmed/35664809 http://dx.doi.org/10.1002/ams2.760 |
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author | Okada, Yohei Komukai, Sho Kitamura, Tetsuhisa Kiguchi, Takeyuki Irisawa, Taro Yamada, Tomoki Yoshiya, Kazuhisa Park, Changhwi Nishimura, Tetsuro Ishibe, Takuya Yagi, Yoshiki Kishimoto, Masafumi Inoue, Toshiya Hayashi, Yasuyuki Sogabe, Taku Morooka, Takaya Sakamoto, Haruko Suzuki, Keitaro Nakamura, Fumiko Matsuyama, Tasuku Nishioka, Norihiro Kobayashi, Daisuke Matsui, Satoshi Hirayama, Atsushi Yoshimura, Satoshi Kimata, Shunsuke Shimazu, Takeshi Ohtsuru, Shigeru Iwami, Taku |
author_facet | Okada, Yohei Komukai, Sho Kitamura, Tetsuhisa Kiguchi, Takeyuki Irisawa, Taro Yamada, Tomoki Yoshiya, Kazuhisa Park, Changhwi Nishimura, Tetsuro Ishibe, Takuya Yagi, Yoshiki Kishimoto, Masafumi Inoue, Toshiya Hayashi, Yasuyuki Sogabe, Taku Morooka, Takaya Sakamoto, Haruko Suzuki, Keitaro Nakamura, Fumiko Matsuyama, Tasuku Nishioka, Norihiro Kobayashi, Daisuke Matsui, Satoshi Hirayama, Atsushi Yoshimura, Satoshi Kimata, Shunsuke Shimazu, Takeshi Ohtsuru, Shigeru Iwami, Taku |
author_sort | Okada, Yohei |
collection | PubMed |
description | AIM: We aimed to identify subphenotypes among patients with out‐of‐hospital cardiac arrest (OHCA) with initial non‐shockable rhythm by applying machine learning latent class analysis and examining the associations between subphenotypes and neurological outcomes. METHODS: This study was a retrospective analysis within a multi‐institutional prospective observational cohort study of OHCA patients in Osaka, Japan (the CRITICAL study). The data of adult OHCA patients with medical causes and initial non‐shockable rhythm presenting with OHCA between 2012 and 2016 were included in machine learning latent class analysis models, which identified subphenotypes, and patients who presented in 2017 were included in a dataset validating the subphenotypes. We investigated associations between subphenotypes and 30‐day neurological outcomes. RESULTS: Among the 12,594 patients in the CRITICAL study database, 4,849 were included in the dataset used to classify subphenotypes (median age: 75 years, 60.2% male), and 1,465 were included in the validation dataset (median age: 76 years, 59.0% male). Latent class analysis identified four subphenotypes. Odds ratios and 95% confidence intervals for a favorable 30‐day neurological outcome among patients with these subphenotypes, using group 4 for comparison, were as follows; group 1, 0.01 (0.001–0.046); group 2, 0.097 (0.051–0.171); and group 3, 0.175 (0.073–0.358). Associations between subphenotypes and 30‐day neurological outcomes were validated using the validation dataset. CONCLUSION: We identified four subphenotypes of OHCA patients with initial non‐shockable rhythm. These patient subgroups presented with different characteristics associated with 30‐day survival and neurological outcomes. |
format | Online Article Text |
id | pubmed-9136939 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91369392022-06-04 Clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis Okada, Yohei Komukai, Sho Kitamura, Tetsuhisa Kiguchi, Takeyuki Irisawa, Taro Yamada, Tomoki Yoshiya, Kazuhisa Park, Changhwi Nishimura, Tetsuro Ishibe, Takuya Yagi, Yoshiki Kishimoto, Masafumi Inoue, Toshiya Hayashi, Yasuyuki Sogabe, Taku Morooka, Takaya Sakamoto, Haruko Suzuki, Keitaro Nakamura, Fumiko Matsuyama, Tasuku Nishioka, Norihiro Kobayashi, Daisuke Matsui, Satoshi Hirayama, Atsushi Yoshimura, Satoshi Kimata, Shunsuke Shimazu, Takeshi Ohtsuru, Shigeru Iwami, Taku Acute Med Surg Original Articles AIM: We aimed to identify subphenotypes among patients with out‐of‐hospital cardiac arrest (OHCA) with initial non‐shockable rhythm by applying machine learning latent class analysis and examining the associations between subphenotypes and neurological outcomes. METHODS: This study was a retrospective analysis within a multi‐institutional prospective observational cohort study of OHCA patients in Osaka, Japan (the CRITICAL study). The data of adult OHCA patients with medical causes and initial non‐shockable rhythm presenting with OHCA between 2012 and 2016 were included in machine learning latent class analysis models, which identified subphenotypes, and patients who presented in 2017 were included in a dataset validating the subphenotypes. We investigated associations between subphenotypes and 30‐day neurological outcomes. RESULTS: Among the 12,594 patients in the CRITICAL study database, 4,849 were included in the dataset used to classify subphenotypes (median age: 75 years, 60.2% male), and 1,465 were included in the validation dataset (median age: 76 years, 59.0% male). Latent class analysis identified four subphenotypes. Odds ratios and 95% confidence intervals for a favorable 30‐day neurological outcome among patients with these subphenotypes, using group 4 for comparison, were as follows; group 1, 0.01 (0.001–0.046); group 2, 0.097 (0.051–0.171); and group 3, 0.175 (0.073–0.358). Associations between subphenotypes and 30‐day neurological outcomes were validated using the validation dataset. CONCLUSION: We identified four subphenotypes of OHCA patients with initial non‐shockable rhythm. These patient subgroups presented with different characteristics associated with 30‐day survival and neurological outcomes. John Wiley and Sons Inc. 2022-05-27 /pmc/articles/PMC9136939/ /pubmed/35664809 http://dx.doi.org/10.1002/ams2.760 Text en © 2022 The Authors. Acute Medicine & Surgery published by John Wiley & Sons Australia, Ltd on behalf of Japanese Association for Acute Medicine. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Articles Okada, Yohei Komukai, Sho Kitamura, Tetsuhisa Kiguchi, Takeyuki Irisawa, Taro Yamada, Tomoki Yoshiya, Kazuhisa Park, Changhwi Nishimura, Tetsuro Ishibe, Takuya Yagi, Yoshiki Kishimoto, Masafumi Inoue, Toshiya Hayashi, Yasuyuki Sogabe, Taku Morooka, Takaya Sakamoto, Haruko Suzuki, Keitaro Nakamura, Fumiko Matsuyama, Tasuku Nishioka, Norihiro Kobayashi, Daisuke Matsui, Satoshi Hirayama, Atsushi Yoshimura, Satoshi Kimata, Shunsuke Shimazu, Takeshi Ohtsuru, Shigeru Iwami, Taku Clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis |
title | Clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis |
title_full | Clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis |
title_fullStr | Clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis |
title_full_unstemmed | Clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis |
title_short | Clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis |
title_sort | clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9136939/ https://www.ncbi.nlm.nih.gov/pubmed/35664809 http://dx.doi.org/10.1002/ams2.760 |
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