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

This study presents an interpretable machine learning model for early neurological stratification in comatose OHCA survivors undergoing TTM. Using multinational registry data, the model demonstrated robust performance across both development and external validation cohorts. By integrating clinically relevant predictors, this approach provides individualized estimates to support early assessment and guide future therapeutic considerations. Notably, this study was not designed to compare different TTM temperature strategies, and results should not be interpreted in that context. The explainable framework is intended to complement clinical evaluation without replacing physician judgment or informing treatment withdrawal decisions.