Abstract 354: A Prediction Model for Patients With Emergency Medical Technicians Witnessed Out-of-Hospital Cardiac Arrest

Introduction: In-hospital early warning system scores for prediction of clinical deterioration have been well-developed. However, such prediction tools in prehospital setting remain unavailable. Hypothesis: To develop a model for predicting patients with emergency medical technicians witnessed out-of-hospital cardiac arrest (EMT-witnessed OHCA) . Methods: We used the fire-based emergency medical service (EMS) data from Taipei city to develop the prediction model. Patients included in this study were those initially alive, non-traumatic, and aged ≧20 years. Data were extracted from records of ambulance run sheets and OHCA registry in Taipei. The primary outcome (i.e. EMT-witnessed OHCA) was defined as cardiac arrest occurring during EMT services before arrival at the receiving hospital. The prediction model was developed through the standard cross-validation method (i.e. divided dataset for training group and validation group). Area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow (HL) test were used to test discrimination and calibration. The point value system with Youden’s J Index was used to find the best cut-off value for practical application. Results: From 2011 to 2015, a total of 252,771 patients were included. Of them, 660 (0.26%) were EMT-witnessed OHCA. The prediction model, including gender, respiratory rate, heart rate, systolic blood pressure, level of consciousness and oxygen saturation, showed excellent discrimination (AUC 0.94) and calibration ( p =0.42 for HL test). When applied to the validation dataset, it maintained good discriminatory ability (AUC 0.94) and calibration ( p =0.11). The optimal cut-off value (≧13) of the point value system of the tool showed high sensitivity (87.84%) and specificity (86.20%). Conclusions: The newly developed prediction model will help identify high-risk patients with EMT-witnessed OHCA and indicate potential prevention by situation awareness in EMS.