Details

Autor(en) / Beteiligte

• Wang, Shao-An
• Chang, Chih-Jung
• Do Shin, Shan
• Chu, Sheng-En
• Huang, Chun-Yen
• Hsu, Li-Min
• Lin, Hao-Yang
• Hong, Ki Jeong
• Jamaluddin, Sabariah Faizah
• Son, Do Ngoc
• Ramakrishnan, T V
• Chiang, Wen-Chu
• Sun, Jen-Tang
• Huei-Ming Ma, Matthew

Titel

Development of a prediction model for emergency medical service witnessed traumatic out-of-hospital cardiac arrest: A multicenter cohort study

Ist Teil von

• Journal of the Formosan Medical Association, 2024-01, Vol.123 (1), p.23-35

Ort / Verlag

Singapore: Elsevier

Erscheinungsjahr

2024

Quelle

MEDLINE

Beschreibungen/Notizen

• To 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. We 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. In 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). We 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.