• Ruben Crespo-Diaz, Julian Wolfson, Demetris Yannopoulos, and Jason A Bartos.
• Mayo Clinic, Department of Cardiovascular Diseases, Rochester, MN.
• Crit. Care Med. 2024 Jul 1; 52 (7): 106510761065-1076.

ObjectivesExtracorporeal cardiopulmonary resuscitation (ECPR) has been shown to improve neurologically favorable survival in patients with refractory out-of-hospital cardiac arrest (OHCA) caused by shockable rhythms. Further refinement of patient selection is needed to focus this resource-intensive therapy on those patients likely to benefit. This study sought to create a selection model using machine learning (ML) tools for refractory cardiac arrest patients undergoing ECPR.DesignRetrospective cohort study.SettingCardiac ICU in a Quaternary Care Center.PatientsAdults 18-75 years old with refractory OHCA caused by a shockable rhythm.MethodsThree hundred seventy-six consecutive patients with refractory OHCA and a shockable presenting rhythm were analyzed, of which 301 underwent ECPR and cannulation for venoarterial extracorporeal membrane oxygenation. Clinical variables that were widely available at the time of cannulation were analyzed and ranked on their ability to predict neurologically favorable survival.InterventionsML was used to train supervised models and predict favorable neurologic outcomes of ECPR. The best-performing models were internally validated using a holdout test set.Measurements And Main ResultsNeurologically favorable survival occurred in 119 of 301 patients (40%) receiving ECPR. Rhythm at the time of cannulation, intermittent or sustained return of spontaneous circulation, arrest to extracorporeal membrane oxygenation perfusion time, and lactic acid levels were the most predictive of the 11 variables analyzed. All variables were integrated into a training model that yielded an in-sample area under the receiver-operating characteristic curve (AUC) of 0.89 and a misclassification rate of 0.19. Out-of-sample validation of the model yielded an AUC of 0.80 and a misclassification rate of 0.23, demonstrating acceptable prediction ability.ConclusionsML can develop a tiered risk model to guide ECPR patient selection with tailored arrest profiles.Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine and Wolters Kluwer Health, Inc.

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