• Jeong Ho Park, Jinwook Choi, SangMyeong Lee, Sang Do Shin, and Kyoung Jun Song.
• Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea; Department of Emergency Medicine, Seoul National University College of Medicine and Hospital, Seoul, Korea; Laboratory of Emergency Medical Services, Seoul National University Hospital Biomedical Research Institute, Seoul, Korea.
• Ann Emerg Med. 2022 Feb 1; 79 (2): 132-144.

Study ObjectiveWe aimed to train and validate the time to on-scene return of spontaneous circulation prediction models using time-to-event analysis among out-of-hospital cardiac arrest patients.MethodsUsing a Korean population-based out-of-hospital cardiac arrest registry, we selected a total of 105,215 adults with presumed cardiac etiologies between 2013 and 2018. Patients from 2013 to 2017 and from 2018 were analyzed for training and test, respectively. We developed 4 time-to-event analyzing models (Cox proportional hazard [Cox], random survival forest, extreme gradient boosting survival, and DeepHit) and 4 classification models (logistic regression, random forest, extreme gradient boosting, and feedforward neural network). Patient characteristics and Utstein elements collected at the scene were used as predictors. Discrimination and calibration were evaluated by Harrell's C-index and integrated Brier score.ResultsAmong the 105,215 patients (mean age 70 years and 64% men), 86,314 and 18,901 patients belonged to the training and test sets, respectively. On-scene return of spontaneous circulation was achieved in 5,240 (6.1%) patients in the former set and 1,709 (9.0%) patients in the latter. The proportion of emergency medical services (EMS) management was higher and scene time interval longer in the latter. Median time from EMS scene arrival to on-scene return of spontaneous circulation was 8 minutes for both datasets. Classification models showed similar discrimination and poor calibration power compared to survival models; Cox showed high discrimination with the best calibration (C-index [95% confidence interval]: 0.873 [0.865 to 0.882]; integrated Brier score at 30 minutes: 0.060).ConclusionIncorporating time-to-event analysis could lead to improved performance in prediction models and contribute to personalized field EMS resuscitation decisions.Copyright © 2021 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

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