A prediction model for patients with emergency medical service witnessed out-of-hospital cardiac arrest

Background/purpose: The study aim was to develop a model for predicting patients with emergency medical service (EMS) witnessed out-of-hospital cardiac arrest (OHCA).

Methods: We used fire-based EMS data from Taipei city to develop the prediction model. Patients included in this study were those who were initially alive, non-traumatic, and age ≧20 years. Data were extracted from electronic records of ambulance run sheets and an Utstein-style OHCA registry. The primary outcome (EMS-witnessed OHCA) was defined as cardiac arrest occurring during the service of emergency medical technicians before arrival at a receiving hospital. Area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow (HL) test were used to examine discrimination and calibration. The point value system with Youden's J Index was used to find the optimal cut-off value.

Results: From 2011 to 2015, a total of 252,771 patients were included. Of them, 660 (0.26%) were EMS-witnessed OHCA. The model, including the predictors of male gender, respiratory rate≦10 cycles/min, heart rate <60 or ≧120 beats/min, systolic blood pressure <100 mmHg, level of consciousness, and oxygen saturation <94%, reached excellent discrimination with an AUROC of 0.94 [95% confidence interval (CI), 0.93-0.95] and excellent calibration (p = 0.42 for HL test) in a randomly selected derivation cohort. The results were comparable to those found in a validation cohort. The optimal cut-off value (≧13) of the tool demonstrated high sensitivity (87.84%) and specificity (86.20%).

Conclusion: This newly developed prediction model will help identify high-risk patients with EMS-witnessed OHCA.