• Jeong Ho Park, Sang Do Shin, Kyoung Jun Song, Ki Jeong Hong, Young Sun Ro, Jin-Wook Choi, and Sae Won Choi.
• Department of Biomedical Engineering, Seoul National University College of Medicine; Department of Emergency Medicine, Seoul National University College of Medicine. Electronic address: timthe@gmail.com.
• Resuscitation. 2019 Sep 1; 142: 127-135.

BackgroundThis study aimed to train, validate and compare predictive models that use machine learning analysis for good neurological recovery in OHCA patients.MethodsAdult OHCA patients who had a presumed cardiac etiology and a sustained return of spontaneous circulation between 2013 and 2016 were analyzed; 80% of the individuals were analyzed for training and 20% were analyzed for validation. We developed using six machine learning algorithms: logistic regression (LR), extreme gradient boosting (XGB), support vector machine, random forest, elastic net (EN), and neural network. Variables that could be obtained within 24 hours of the emergency department visit were used. The area under the receiver operation curve (AUROC) was calculated to assess the discrimination. Calibration was assessed by the Hosmer-Lemeshow test. Reclassification was assessed by using the continuous net reclassification index (NRI).ResultsA total of 19,860 OHCA patients were included in the analysis. Of the 15,888 patients in the training group, 2228 (14.0%) had a good neurological recovery; of the 3972 patients in the validation group, 577 (14.5%) had a good neurological recovery. The LR, XGB, and EN models showed the highest discrimination powers (AUROC (95% CI)) of 0.949 (0.941-0.957) for all), and all three models were well calibrated (Hosmer-Lemeshow test: p >0.05). The XGB model reclassified patients according to their true risk better than the LR model (NRI: 0.110), but the EN model reclassified patients worse than the LR model (NRI: -1.239).ConclusionThe best performing machine learning algorithm was the XGB and LR algorithm.Copyright © 2019 Elsevier B.V. All rights reserved.

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