Assessment of emergency physicians' performance in identifying shockable rhythm in out-of-hospital cardiac arrest: an observational simulation study.

BACKGROUND: Emergency physicians can use a manual or an automated defibrillator to provide defibrillation of patients who had out-of-hospital cardiac arrest (OHCA). Performance of emergency physicians in identifying shockable rhythm with a manual defibrillator has been poorly explored whereas that of automated defibrillators is well known (sensitivity 0.91-1.00, specificity 0.96-0.99). We conducted this study to estimate the sensitivity/specificity and speed of shock/no-shock decision-making by prehospital emergency physicians for shockable or non-shockable rhythm, and their preference for manual versus automated defibrillation. METHODS: We developed a web application that simulates a manual defibrillator (https://simul-shock.firebaseapp.com/). In 2019, all (262) emergency physicians of six French emergency medical services were invited to participate in a study in which 60 ECG rhythms from real OHCA recordings were successively presented to the physicians for determination of whether they would or would not administer a shock. Time to decision was recorded. Answers were compared with a gold standard (concordant answers of three experts). We report sensitivity for shockable rhythms (decision to shock) and specificity for non-shockable rhythms (decision not to shock). Physicians were also asked whether they preferred manual or automated defibrillation. RESULTS: Among 215 respondents, we were able to analyse results for 190 physicians. 57% of emergency physicians preferred manual defibrillation. Median (IQR) sensitivity for a shock delivery for shockable rhythm was 0.91 (0.81-1.00); median specificity for no-shock delivery for non-shockable rhythms was 0.91 (0.80-0.96). More precisely, sensitivities for shock delivery for ventricular tachycardia (VT) and coarse ventricular fibrillation (VF) were both 1.0 (1.0-1.0); sensitivity for fine VF was 0.6 (0.2-1). Specificity for not shocking a pulseless electrical activity (PEA) was 0.83 (0.72-0.86), and for asystole, specificity was 0.93 (0.86-1). Median speed of decision-making (in seconds) were: VT 2.0 (1.6-2.7), coarse VF 2.1 (1.7-2.9), asystole 2.4 (1.8-3.5), PEA 2.8 (2.0-4.2) and fine VF 2.8 (2.1-4.3). CONCLUSIONS: Global sensitivity and specificity were comparable with published automated external defibrillator studies. Shockable rhythms with the best clinical prognoses (VT and coarse VF) were very rapidly recognised with very good sensitivity. The decision-making for fine VF or asystole and PEA was less accurate.

Automatic external defibrillator provided by unmanned aerial vehicle (drone) in Greater Paris: A real world-based simulation.

AIM: To reduce the delay in defibrillation of out-of-hospital cardiac arrest (OHCA) patients, recent publications have shown that drones equipped with an automatic external defibrillator (AED) appear to be effective in sparsely populated areas. To study the effectiveness of AED-drones in high-density urban areas, we developed an algorithm based on emergency dispatch parameters for the rate and detection speed of cardiac arrests and technical and meteorological parameters. METHODS: We ran a numerical simulation to compare the actual time required by the Basic Life Support team (BLSt) for OHCA patients in Greater Paris in 2017 to the time required by an AED-drone. Endpoints were the proportion of patients with "AED-drone first" and the defibrillation time gained. We built an open-source website (https://airborne-aed.org/) to allow modelling by modifying one or more parameters and to help other teams model their own OHCA data. RESULTS: Of 3014 OHCA patients, 72.2 ±â€¯0.7% were in the "no drone flight" group, 25.8 ±â€¯0.2% in the "AED-drone first" group, and 2.1 ±â€¯0.2% in the "BLSt-drone first" group. When a drone flight was authorized, it arrived an average 190 s before BLSt in 93% of cases. The possibility of flying the drone during the aeronautical night improved the results of the "AED-drone first" group the most (+60%). CONCLUSIONS: In our very high-density urban model, at most 26% of OHCA patients received an AED from an AED-drone before BLSt. The flexible parameters of our website model allows evaluation of the impact of each choice and concrete implementation of the AED-drone.