RESUMEN
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 190â¯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.
