• Shao-An Wang, Chih-Jung Chang, Shan Do Shin, Sheng-En Chu, Chun-Yen Huang, Li-Min Hsu, Hao-Yang Lin, Ki Jeong Hong, Sabariah Faizah Jamaluddin, Do Ngoc Son, T V Ramakrishnan, Wen-Chu Chiang, Jen-Tang Sun, Huei-Ming MaMatthewMDepartment of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan; Department of Emergency Medicine, National Taiwan University Hospital, Yun-Lin Branch, Taipei, Taiwan., and PATOS Clinical Research Network.
• Department of Emergency Medicine, Far Eastern Memorial Hospital, No. 21, Sec. 2, Nan Ya South Rd, Banqiao Dist, New Taipei City, Taiwan.
• J Formos Med Assoc. 2024 Jan 1; 123 (1): 233523-35.

Background/PurposeTo develop a prediction model for emergency medical technicians (EMTs) to identify trauma patients at high risk of deterioration to emergency medical service (EMS)-witnessed traumatic cardiac arrest (TCA) on the scene or en route.MethodsWe developed a prediction model using the classical cross-validation method from the Pan-Asia Trauma Outcomes Study (PATOS) database from 1 January 2015 to 31 December 2020. Eligible patients aged ≥18 years were transported to the hospital by the EMS. The primary outcome (EMS-witnessed TCA) was defined based on changes in vital signs measured on the scene or en route. We included variables that were immediately measurable as potential predictors when EMTs arrived. An integer point value system was built using multivariable logistic regression. The area under the receiver operating characteristic (AUROC) curve and Hosmer-Lemeshow (HL) test were used to examine discrimination and calibration in the derivation and validation cohorts.ResultsIn total, 74,844 patients were eligible for database review. The model comprised five prehospital predictors: age <40 years, systolic blood pressure <100 mmHg, respiration rate >20/minute, pulse oximetry <94%, and levels of consciousness to pain or unresponsiveness. The AUROC in the derivation and validation cohorts was 0.767 and 0.782, respectively. The HL test revealed good calibration of the model (p = 0.906).ConclusionWe established a prediction model using variables from the PATOS database and measured them immediately after EMS personnel arrived to predict EMS-witnessed TCA. The model allows prehospital medical personnel to focus on high-risk patients and promptly administer optimal treatment.Copyright © 2023 Formosan Medical Association. Published by Elsevier B.V. All rights reserved.

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