ArticleResuscitation2026
Large-scale automated phenotyping of cardiac arrest and withdrawal of life-sustaining therapy using electronic health record data.
Article in Resuscitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- Automated Derivation of Cerebral Performance Category at Hospital Discharge After Cardiac Arrest Using Natural Language Processing and Machine Learning.Neurocritical care · 2026Article
- Review
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
aims of the studyAnoxic 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.
methodsWe used structured and unstructured EHR (Electronic Health Record) data from two major hospitals to train models that identify (1) patients with cardiac arrest and coma, and (2) patients who died after WLST. Performance was evaluated using the area under the receiver operating characteristic (AUROC) and precision-recall (AUPRC) curves, as well as other precision metrics.
resultsOn holdout (internal) testing the models achieved AUROC/AUPRC values of 0.984/0.968 (cardiac arrest) and 0.992/0.991 (WLST). Cross-hospital evaluation showed strong performance for the cardiac arrest phenotype but variable generalizability for the WLST phenotype, with sensitivity depending on the training site. Population-level error rates were low (<0.5 %) for the cardiac arrest phenotype; estimates for WLST varied by hospital.
conclusionThese models establish a reproducible framework for automated cohort identification. Nearly half of comatose post-arrest patients died following WLST, with 42 % of these deaths occurring within 72 h, highlighting the impact of early prognostication decisions. The models enable rapid cohort identification for research on neuroprognostication, including how WLST decisions may perpetuate self-fulfilling prophecies. Broader validation across health systems and larger cohorts will improve generalizability and inform evidence-based end-of-life decision-making. Institutional review board approval: Mass General Brigham IRB BIDMC: 2022P000481; MGB: 2013P001024. All procedures complied with institutional and national ethical standards; informed consent was waived for use of de-identified data.
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Registered trials
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.