• Araz Rawshani, Fredrik Hessulf, John Deminger, Pedram Sultanian, Vibha Gupta, Peter Lundgren, Mohammed Mohammed, Monér Abu Alchay, Tobias Siöland, Emilia Gryska, and Adam Piasecki.
• Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Wallenberg Laboratory, Blå stråket 5, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Blå stråket 5, 413 45 Gothenburg, Sweden; The Swedish Registry for Cardiopulmonary Resuscitation, Medicinaregatan 18G, 413 90 Gothenburg, Sweden.
• Resuscitation. 2024 Aug 12; 202: 110359110359.

UnlabelledOut-of-hospital cardiac arrest (OHCA) is a critical condition with low survival rates. In patients with a return of spontaneous circulation, brain injury is a leading cause of death. In this study, we propose an interpretable machine learning approach for predicting neurologic outcome after OHCA, using information available at the time of hospital admission.MethodsThe study population were 55 615 OHCA cases registered in the Swedish Cardiopulmonary Resuscitation Registry between 2010 and 2020. The dataset was split to training and validation sets (for model development) and test set (for evaluation of the final model). We used an XGBoost algorithm with stratified, repeated 10-fold cross-validation along with Optuna framework for hyperparameters tuning. The final model was trained on 10 features selected based on the importance scores and evaluated on the test set in terms of discrimination, calibration and bias-variance tradeoff. We used SHapley Additive exPlanations to address the 'black-box' model and align with eXplainable artificial intelligence.ResultsThe final model achieved: area under the receiver operating characteristic value 0.964 (95% confidence interval (CI) [0.960-0.968]), sensitivity 0.606 (95% CI [0.573-0.634]), specificity 0.975 (95% CI [0.972-0.978]), positive predictive value (PPV) 0.664 (95% CI [0.625-0.696]), negative predictive value (NPV) 0.969 (95% CI [0.966-0.972]), macro F1 0.803 (95% CI [0.788-0.816]), and showed a very good calibration. SHAP features with the highest impact on the model's output were:'ROSC on arrival to hospital', 'Initial rhythm asystole' and 'Conscious on arrival to hospital'.ConclusionsThe XGBoost machine learning model with 10 features available at the time of hospital admission showed good performance for predicting neurologic outcome after OHCA, with no apparent signs of overfitting.Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.

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