Literature DB >> 34732587

Clinical Phenotyping of Out-of-Hospital Cardiac Arrest Patients With Shockable Rhythm - Machine Learning-Based Unsupervised Cluster Analysis.

Yohei Okada1,2, Sho Komukai3, Tetsuhisa Kitamura4, Takeyuki Kiguchi5, Taro Irisawa6, Tomoki Yamada7, Kazuhisa Yoshiya8, Changhwi Park9, Tetsuro Nishimura10, Takuya Ishibe11, Yoshiki Yagi12, Masafumi Kishimoto13, Toshiya Inoue14, Yasuyuki Hayashi15, Taku Sogabe16, Takaya Morooka17, Haruko Sakamoto18, Keitaro Suzuki19, Fumiko Nakamura20, Tasuku Matsuyama21, Norihiro Nishioka1, Daisuke Kobayashi1, Satoshi Matsui4, Atsushi Hirayama22, Satoshi Yoshimura1, Shunsuke Kimata1, Takeshi Shimazu6, Shigeru Ohtsuru2, Taku Iwami1.   

Abstract

BACKGROUND: The hypothesis of this study is that latent class analysis could identify the subphenotypes of out-of-hospital cardiac arrest (OHCA) patients associated with the outcomes and allow us to explore heterogeneity in the effects of extracorporeal cardiopulmonary resuscitation (ECPR).Methods and 
Results: This study was a retrospective analysis of a multicenter prospective observational study (CRITICAL study) of OHCA patients. It included adult OHCA patients with initial shockable rhythm. Patients from 2012 to 2016 (development dataset) were included in the latent class analysis, and those from 2017 (validation dataset) were included for evaluation. The association between subphenotypes and outcomes was investigated. Further, the heterogeneity of the association between ECPR implementation and outcomes was explored. In the study results, a total of 920 patients were included for latent class analysis. Three subphenotypes (Groups 1, 2, and 3) were identified, mainly characterized by the distribution of partial pressure of O2(PO2), partial pressure of CO2(PCO2) value of blood gas assessment, cardiac rhythm on hospital arrival, and estimated glomerular filtration rate. The 30-day survival outcomes were varied across the groups: 15.7% in Group 1; 30.7% in Group 2; and 85.9% in Group 3. Further, the association between ECPR and 30-day survival outcomes by subphenotype groups in the development dataset was as varied. These results were validated using the validation dataset.
CONCLUSIONS: The latent class analysis identified 3 subphenotypes with different survival outcomes and potential heterogeneity in the effects of ECPR.

Entities:  

Keywords:  Cardiac arrest; Clustering; Latent class analysis; Subphenotype; Ventricular fibrillation

Mesh:

Year:  2021        PMID: 34732587     DOI: 10.1253/circj.CJ-21-0675

Source DB:  PubMed          Journal:  Circ J        ISSN: 1346-9843            Impact factor:   2.993


  1 in total

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

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
Journal:  Acute Med Surg       Date:  2022-05-27
  1 in total

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