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...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Original Articles
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

Internet

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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