• Xiang Yi Wong, AngYu KaiYKPre-hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore., Keqi Li, ChinYip HanYHYong Loo Lin School of Medicine, National University of Singapore, Singapore., LamSean Shao WeiSSWHealth Services Research Centre, SingHealth, Singapore., TanKenneth Boon KiatKBKDepartment of Emergency Medicine, Singapore General Hospital, Singapore., ChuaMatthew Chin HengMCHInstitute of System Science, National University of Singapore, Singapore., OngMarcus Eng HockMEHDepartment of Emergency Medicine, Singapore General Hospital, Singapore; Health Services & Systems Research, Duke-NUS Medical School, Singapore., Nan Liu, Ahmad Reza Pourghaderi, HoAndrew Fu WahAFWPre-hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore. Electronic address: sophronesis@gmail.com., and PAROS Singapore Investigators.
• Pre-hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore; Singapore Civil Defence Force, Ministry of Home Affairs, Singapore. Electronic address: wongxiangyi@u.duke.nus.edu.
• Resuscitation. 2022 Jan 1; 170: 126-133.

BackgroundAccurate and timely prognostication of patients with out-of-hospital cardiac arrest (OHCA) who achieved the return of spontaneous circulation (ROSC) is crucial in clinical decision-making, resource allocation, and communications with next-of-kins. We aimed to develop the Survival After ROSC in Cardiac Arrest (SARICA), a practical clinical decision tool to predict survival in OHCA patients who attained ROSC.MethodsWe utilized real-world Singapore data from the population-based Pan-Asian Resuscitation Outcomes Study between 2010-2018. We excluded patients without ROSC. The dataset was segmented into training (60%), validation (20%) and testing (20%) cohorts. The primary endpoint was survival (to 30-days or hospital discharge). AutoScore, an interpretable machine-learning based clinical score generation algorithm, was used to develop SARICA. Candidate factors were chosen based on objective demographic and clinical factors commonly available at the time of admission. Performance of SARICA was evaluated based on receiver-operating curve (ROC) analyses.Results5970 patients were included, of which 855 (14.3%) survived. A three-variable model was determined to be most parsimonious. Prehospital ROSC, age, and initial heart rhythm were identified for inclusion via random forest selection. Finally, SARICA consisted of these 3 variables and ranged from 0 to 10 points, achieving an area under the ROC (AUC) of 0.87 (95% confidence interval: 0.84-0.90) within the testing cohort.ConclusionWe developed and internally validated the SARICA score to accurately predict survival of OHCA patients with ROSC at the time of admission. SARICA is clinically practical and developed using an interpretable machine-learning framework. SARICA has unknown generalizability pending external validation studies.Copyright © 2021 Elsevier B.V. All rights reserved.

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