Drones reduce the time to defibrillation in a highly visited non-urban area: A randomized simulation-based trial.

AED Drone HEMS OHCA PAD Unmanned aerial vehicle

Journal

The American journal of emergency medicine
ISSN: 1532-8171
Titre abrégé: Am J Emerg Med
Pays: United States
ID NLM: 8309942

Informations de publication

Date de publication:
17 Sep 2024
Historique:
received: 04 07 2024
revised: 20 08 2024
accepted: 13 09 2024
medline: 22 9 2024
pubmed: 22 9 2024
entrez: 21 9 2024
Statut: aheadofprint

Résumé

Out-of-hospital cardiac arrest (OHCA) has a high global incidence and mortality rate, with early defibrillation significantly improving survival. Our aim was to assess the feasibility of autonomous drone delivery of automated external defibrillators (AED) in a non-urban area with physical barriers and compare the time to defibrillate (TTD) with bystander retrieval from a public access defibrillator (PAD) point and helicopter emergency medical services (HEMS) physician performed defibrillation. This randomized simulation-based trial with a cross-over design included bystanders performing AED retrievals either delivered by automated drone flight or on foot from a PAD point, and simulated HEMS interventions. The primary outcome was the time to defibrillation, with secondary outcomes comparing workload, perceived physical effort, and ease of use. Thirty-six simulations were performed. Drone-delivered AED intervention had a significantly shorter TTD [2.2 (95 % CI 2.0-2.3) min] compared to PAD retrieval [12.4 (95 % CI 10.4-14.4) min] and HEMS [18.2 (95 % CI 17.1-19.2) min]. The self-reported physical effort on a visual analogue scale for drone-delivered AED was significantly lower versus PAD [2.5 (1 - 22) mm vs. 81 (65-99) mm, p = 0.02]. The overall mean workload measured by NASA-TLX was also significantly lower for drone delivery compared to PAD [4.3 (1.2-11.7) vs. 11.9 (5.5-14.5), p = 0.018]. The use of drones for automated AED delivery in a non-urban area with physical barriers is feasible and leads to a shorter time to defibrillation. Drone-delivered AEDs also involve a lower workload and perceived physical effort than AED retrieval on foot.

Identifiants

pubmed: 39305698
pii: S0735-6757(24)00472-8
doi: 10.1016/j.ajem.2024.09.036
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

5-10

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare no conflict of interest.

Auteurs

Michiel J van Veelen (MJ)

Institute of Mountain Emergency Medicine, Eurac Research, Bolzano, Italy; Department of Sport Science, Medical Section, University of Innsbruck, Innsbruck, Austria. Electronic address: michiel.vanveelen@eurac.edu.

Giovanni Vinetti (G)

Institute of Mountain Emergency Medicine, Eurac Research, Bolzano, Italy.

Tomas Dal Cappello (TD)

Institute of Mountain Emergency Medicine, Eurac Research, Bolzano, Italy.

Frederik Eisendle (F)

Institute of Mountain Emergency Medicine, Eurac Research, Bolzano, Italy; Department of Anaesthesiology and Intensive Care Medicine, Medical University of Innsbruck, Innsbruck, Austria.

Abraham Mejia-Aguilar (A)

Center for Sensing Solutions, Eurac Research, Bolzano, Italy.

Riccardo Parin (R)

terraXcube, Eurac Research, Bolzano, Italy.

Rosmarie Oberhammer (R)

HELI Helicopter Emergency Medical Services South Tyrol, Bolzano, Italy; Department of Anaesthesia and Intensive Care, Emergency Medicine and Pain Therapy, Hospital of Brunico (SABES-ASDAA), Teaching Hospital of Paracelsus Medical University, Brunico, Italy.

Marika Falla (M)

Institute of Mountain Emergency Medicine, Eurac Research, Bolzano, Italy; Department of Neurology/Stroke Unit, Hospital of Bolzano (SABES-ASDAA), Teaching Hospital of Paracelsus Medical University, Bolzano, Italy.

Giacomo Strapazzon (G)

Institute of Mountain Emergency Medicine, Eurac Research, Bolzano, Italy; Corpo Nazionale Soccorso Alpino e Speleologico, National Medical School (CNSAS SNaMed), Milano, Italy.

Classifications MeSH