Prediction of good neurological recovery after out-of-hospital cardiac arrest: A machine learning analysis

Resuscitation
Jeong Ho ParkSae Won Choi

Abstract

This study aimed to train, validate and compare predictive models that use machine learning analysis for good neurological recovery in OHCA patients. Adult 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). A 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....Continue Reading

Citations

Nov 30, 2019·Healthcare Informatics Research·Sae Won ChoiKyung Hwan Kim
Nov 20, 2021·Journal of Molecular Medicine : Official Organ of the Gesellschaft Deutscher Naturforscher Und Ärzte·Omar IbrahimLyn R Griffiths

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