Clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis

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 retrospective...

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Bibliographic Details
Main Authors: Yohei Okada, Sho Komukai, Tetsuhisa Kitamura, Takeyuki Kiguchi, Taro Irisawa, Tomoki Yamada, Kazuhisa Yoshiya, Changhwi Park, Tetsuro Nishimura, Takuya Ishibe, Yoshiki Yagi, Masafumi Kishimoto, Toshiya Inoue, Yasuyuki Hayashi, Taku Sogabe, Takaya Morooka, Haruko Sakamoto, Keitaro Suzuki, Fumiko Nakamura, Tasuku Matsuyama, Norihiro Nishioka, Daisuke Kobayashi, Satoshi Matsui, Atsushi Hirayama, Satoshi Yoshimura, Shunsuke Kimata, Takeshi Shimazu, Shigeru Ohtsuru, Taku Iwami, the CRITICAL Research Group Investigators
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Acute Medicine & Surgery
Subjects:
Asystole
cardiac arrest
clustering
latent class analysis
pulseless electrical activity
subphenotype
Online Access:https://doi.org/10.1002/ams2.760

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