Evidence map›Paper›PMID 32627636›Full record

ArticleJournal of the American Heart Association2020

Improving Access to Automated External Defibrillators in Rural and Remote Settings: A Drone Delivery Feasibility Study.

Sheldon Cheskes, Shelley L McLeod, Michael Nolan, Paul Snobelen, Christian Vaillancourt, Steven C Brooks, Katie N Dainty, Timothy C Y Chan, Ian R Drennan

Abstract readComparative Study
In one paragraph

Article in Journal of the American Heart Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed.

  1. Article
  2. Article
  3. Barriers and enablers to public access defibrillation - an international RAND-UCLA consensus study.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026
    Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sheldon CheskesSunnybrook Centre for Prehospital Medicine Sunnybrook Health Sciences Centre Toronto Ontario Canada.
Shelley L McLeodDivision of Emergency Medicine Department of Family and Community Medicine University of Toronto Ontario Canada.
Michael NolanCounty of Renfrew Paramedic Service Pembroke Ontario Canada.
Paul SnobelenPeel Regional Paramedic Services Brampton Ontario Canada.
Christian VaillancourtDepartment of Emergency Medicine Ottawa Hospital Research Institute University of Ottawa Ontario Canada.
Steven C BrooksDepartments of Emergency Medicine and Public Health Sciences Queen's University Kingston Ontario Canada.
Katie N DaintyNorth York General Hospital Toronto Ontario Canada.
Timothy C Y ChanLi Ka Shing Knowledge Institute St. Michael's Hospital Toronto Ontario Canada.
Ian R DrennanSunnybrook Centre for Prehospital Medicine Sunnybrook Health Sciences Centre Toronto Ontario Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Time to treatment is critical for survival from sudden cardiac arrest. Every minute delay in defibrillation results in a 7% to 10% reduction in survival. This is particularly problematic in rural and remote regions, where emergency medical service response is prolonged and automated external defibrillators (AEDs) are often not available. Our primary objective was to examine the feasibility of a novel AED drone delivery method for rural and remote sudden cardiac arrest. A secondary objective was to compare response times between AED drone delivery and ambulance to mock sudden cardiac arrest resuscitations. Methods and Results We conducted 6 simulations in 2 rural communities in southern Ontario, Canada. In the first 2 simulations, the drone and ambulance were dispatched from the same paramedic base. In simulations 3 and 4, the drone and ambulance were dispatched from separate paramedic bases; and in simulations 5 and 6, the drone was dispatched from an optimized location. During each simulation, a "mock" call was placed to 911 and a single AED drone and an ambulance were simultaneously dispatched to a predetermined destination. On scene, trained first responders retrieved the AED from the drone and initiated resuscitative efforts on a mannequin until paramedics arrived. No difficulties were encountered during drone activation by dispatch, ascent, landing, or bystander retrieval of the AED from the drone. During simulations 1 and 2, the distance to the scene was 6.6 km. For simulations 3 and 4, the ambulance response distance increased to 8.8 km while drone remained at 6.6 km; and in simulations 5 and 6, the ambulance response distance was 20 km compared with 9 km for the drone. During each flight, the AED drone arrived on scene before the ambulance, between 1.8 and 8.0 minutes faster. Conclusions This study suggests AED drone delivery is feasible, with the potential for improvements in response time during simulated sudden cardiac arrest scenarios. Further research is required to determine the appropriate system configuration for AED drone delivery in an integrated emergency medical service system as well as optimal strategies to simplify bystander application of a drone-delivered AED.

Indexed as

Death, Sudden, CardiacDefibrillatorsEmergency Medical DispatchAircraftFeasibility StudiesHumansOut-of-Hospital Cardiac Arrestautomated external defibrillationcardiopulmonary resuscitationdronesemergency medical serviceout‐of‐hospital cardiac arrest

Identifiers

PMID32627636
PMCPMC7660725

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.