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Article

Large-scale automated phenotyping of cardiac arrest and withdrawal of life-sustaining therapy using electronic health record data.

Resuscitation - 1 Jan 2026

Clive Catherine, Singh Arjun, Overmeer Bram, Boris Spencer, Peterson Lydia, Searle Jaden, Hooke Greg, Turley Niels, Fernandes Marta, Gupta Aditya, Ghanta Manohar, Junior Valdery Moura, Mukeriji S, Zafar Sahar, Amorim Edilberto, Westover M Brandon, Sun Haoqi

Abstract excerpt

AIMS OF THE STUDY: Anoxic brain injury following cardiac arrest is a leading cause of death in the United States. Withdrawal of life-sustaining therapy (WLST) is a common end-of-life decision in these patients, but its contributing factors and outcomes remain poorly understood. We developed machine learning models to enable large-scale, automated phenotyping to identify patients who died following WLST. METHODS:...

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Identifiers

PMID: 41371332
DOI: 10.1016/j.resuscitation.2025.110919
PMCID: PMC13233148

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