Evidence map›Paper›PMID 40386702›Full record

ArticleJournal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures)2025

Latent class analysis for identification of sub-phenotypes predicting prognosis in hospitalized out-of-hospital cardiac arrest.

Yuki Kishihara, Hideto Yasuda, Masahiro Kashiura, Shunsuke Amagasa, Hiroyuki Tamura, Yutaro Shinzato, Takashi Moriya

Abstract read
In one paragraph

Article in Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Observational
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Yuki KishiharaDepartment of Emergency and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan.
Hideto YasudaDepartment of Emergency and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan.
Masahiro KashiuraDepartment of Emergency and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan.
Shunsuke AmagasaDepartment of Emergency and Transport Medicine, National Center for Child Health and Development, Tokyo, Japan.
Hiroyuki TamuraDepartment of Emergency and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan.
Yutaro ShinzatoDepartment of Emergency and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan.
Takashi MoriyaDepartment of Emergency and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim of the study: To determine which out-of-hospital cardiac arrest (OHCA) patients should receive advanced treatment is extremely challenging. The objective was to identify sub-phenotypes predicting the prognoses of adult OHCA patients by latent class analysis (LCA) using data up to just after admission. Material and Methods: We conducted a retrospective observational study using multicentre OHCA registry from 95 Japanese hospitals including adult non-traumatic hospitalized OHCA. The primary outcome was 30-day favourable neurological outcome. Our LCA used clinically relevant variables up to just after admission and the optimal class number was determined from clinical importance and Bayesian information criterion. The associations between subphenotypes and outcomes were analysed using univariate logistic regression analysis with odds ratios (ORs) and 95% confidence intervals (CIs). Results: Our LCA included 2,162 patients and identified four sub-phenotypes. The base excess on hospital arrival had the highest discriminative power. Thirty-day favourable neurological outcomes were observed in 526 patients (24.3%), including 284 (53.8%) in Group 1, 179 (21.2%) in Group 2, 26 (11.4%) in Group 3, and 37 (6.6%) in Group 4. Prehospital return of spontaneous circulation (ROSC) was achieved in 1,009 patients (46.7%), including 379 (81.8%) in Group 1, 340 (40.3%) in Group 2, 115 (50.4%) in Group 3, and 175 (31.1%) in Group 4. Univariate logistic regression analysis for primary outcome using Group 4 as reference revealed ORs (95% CI) of 16.5 (11.4-24.1) in Group 1, 3.83 (2.64-5.56) in Group 2, and 1.83 (1.08-3.10) in Group 3. Conclusions: Our LCA classified OHCA into four sub-phenotypes showing significant differences for prognosis. In cases who achieved prehospital ROSC, it might be meaningful to continue advanced therapeutic interventions.

Indexed as

adultcardiopulmonary resuscitationlatent class analysisout-of-hospital cardiac arrestprognosis

Identifiers

PMID40386702
PMCPMC12080534

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.