Explainable machine learning for neurological outcome prediction in out-of-hospital cardiac arrest survivors undergoing targeted temperature management: a multi-cohort validation study.

ABSTRACT:

Background

Early risk assessment in comatose survivors of out-of-hospital cardiac arrest (OHCA) remains clinically challenging, particularly for patients undergoing targeted temperature management (TTM). This study aimed to develop and externally validate an interpretable machine learning model to predict neurological outcomes in TTM-treated comatose OHCA survivors, leveraging multinational registry data to improve generalizability and early-phase characterization.

Methods

Data were derived from two multi-center registries: the Korean Hypothermia Network prospective registry (KORHN-pro, n = 1,050) and the Taiwan Network of Targeted Temperature Management for Cardiac Arrest (TIMECARD, n = 393). Adult OHCA patients who remained comatose after return of spontaneous circulation

SUBMITTER: Bamodu OA 

PROVIDER: S-EPMC12771708 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature