ArticleJournal of the American Heart Association2026
Optimizing Extracorporeal Cardiopulmonary Resuscitation Candidate Selection in out-of-Hospital Cardiac Arrest: A Machine-Learning Individualized Treatment Effect Approach Versus Rule-Based Criteria.
Article in Journal of the American Heart Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Humans Versus Machine: Machine Learning-Based Individualized Treatment Effect Modeling for Extracorporeal Cardiopulmonary Resuscitation in Out-of-Hospital Cardiac Arrest.Journal of the American Heart Association · 2026Article
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14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundExtracorporeal cardiopulmonary resuscitation (ECPR) has demonstrated survival benefit in selected patients with out-of-hospital cardiac arrest, yet optimal selection criteria remain uncertain. Machine-learning-based individualized treatment effect (ITE) modeling may identify patients most likely to benefit by capturing heterogeneity of treatment response, potentially providing a more accurate strategy for ECPR candidate selection than current rule-based criteria.
methodsWe retrospectively analyzed adult, nontraumatic, emergency medical services-attended patients with out-of-hospital cardiac arrest from 4 tertiary centers in Taiwan between 2016 and 2024. After propensity score matching for shockable rhythm and witnessed arrest, a gradient-boosted trees-based causal forest model was developed to estimate ITE and predict the survival benefit of ECPR to hospital discharge. Absolute observed treatment effect across different ITE thresholds was compared with trial-based (ARREST [Advanced reperfusion strategies for patients with out-of-hospital cardiac arrest and refractory ventricular fibrillation] trial, PRAGUE OHCA [Effect of Intra-arrest Transport, Extracorporeal Cardiopulmonary Resuscitation, and Immediate Invasive Assessment and Treatment on Functional Neurologic Outcome in Refractory Out-of-Hospital Cardiac Arrest] trial, and INCEPTION [Early Extracorporeal CPR for Refractory Out-of-Hospital Cardiac Arrest] trial) and hospital-specific criteria.
resultsAmong 1953 matched patients, 977 received ECPR. Overall survival was similar between ECPR and non-ECPR groups (11.1% versus 12.8%; standardized mean difference=0.054). In the top 10% of patients with the highest predicted benefit, survival to discharge was 50.0% with ECPR versus 20.0% without, yielding an absolute observed treatment effect of 30.0% (95% CI, 1.56-57.58;
conclusionsThe ITE-based model was associated with greater observed survival benefit compared with current rule-based criteria and may demonstrate potential as a data-driven framework for candidate selection for ECPR. Further validation in prospective settings is warranted.
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