Analysis during chest compressions in out-of-hospital cardiac arrest patients, a cross/sectional study: The DEFI 2022 study.

AIMS: During out-of-hospital cardiac arrest (OHCA), an automatic external defibrillator (AED) analyzes the cardiac rhythm every two minutes; however, 80% of refibrillations occur within the first minute post-shock. We have implemented an algorithm for Analyzing cardiac rhythm While performing chest Compression (AWC). When AWC detects a shockable rhythm, it shortens the time between analyses to one minute. We investigated the effect of AWC on cardiopulmonary resuscitation quality. METHOD: In this cross-sectional study, we compared patients treated in 2022 with AWC, to a historical cohort from 2017. Inclusion criteria were OHCA patients with a shockable rhythm at the first analysis. Primary endpoint was the chest compression fraction (CCF). Secondary endpoints were cardiac rhythm evolution and survival, including survival analysis of non-prespecified subgroups. RESULTS: In 2017 and 2022, 355 and 377 OHCAs met the inclusion criteria, from which we analyzed the 285 first consecutive cases in each cohort. CCF increased in 2022 compared to 2017 (77% [72-80] vs 72% [67-76]; P < 0.001) and VF recurrences were shocked more promptly (53 s [32-69] vs 117 s [90-132]). Survival did not differ between 2017 and 2022 (adjusted hazard-ratio 0.96 [95% CI, 0.78-1.18]), but was higher in 2022 within the sub-group of OHCAs that occurred in a public place and within a short time from call to AED switch-on (adjusted hazard ratio 0.85[0.76-0.96]). CONCLUSIONS: OHCA patients treated with AWC had higher CCF, shorter time spent in ventricular fibrillation, but no survival difference, except for OHCA that occurred in public places with short intervention time.

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.