Literature DB >> 25281188

Systematic downloading and analysis of data from automated external defibrillators used in out-of-hospital cardiac arrest.

Marco Bo Hansen1, Freddy Knudsen Lippert2, Lars Simon Rasmussen3, Anne Møller Nielsen4.   

Abstract

BACKGROUND: Valuable information can be retrieved from automated external defibrillators (AEDs) used in victims of out-of-hospital cardiac arrest (OHCA). We describe our experience with systematic downloading of data from deployed AEDs. The primary aim was to compare the proportion of shockable rhythm from AEDs used by laypersons with the corresponding proportion recorded by the Emergency Medical Services (EMS) on arrival.
METHODS: In a 20-month study, we collected data on OHCAs in the Capital Region of Denmark where an AED was deployed prior to arrival of EMS. The AEDs were brought to the emergency medical dispatch centre for data downloading and rhythm analysis. Patient data were retrieved from the medical records from the admitting hospital, whereas data on EMS rhythm analyses were obtained from the Danish Cardiac Arrest Register between 2001 and 2010.
RESULTS: A total of 121 AEDs were deployed, of which 91 cases were OHCAs with presumed cardiac origin. The prevalence of initial shockable rhythm was 55.0% (95% CI [44.7-64.8%]). This was significantly greater than the proportion recorded by the EMS (27.6%, 95% CI [27.0-28.3%], p<0.0001). Shockable arrests were significantly more likely to be witnessed (92% vs. 34%, p<0.0001) and the bystander CPR rate was higher (98% vs. 85%, p=0.04). More patients with initial shockable rhythm achieved return of spontaneous circulation upon hospital arrival (88% vs. 7%, p<0.0001) and had higher 30-day survival rate (72% vs. 5%, p<0.0001).
CONCLUSION: AEDs used by laypersons revealed a higher proportion of shockable rhythms compared to the EMS rhythm analyses.
Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Entities:  

Keywords:  Basic life support; Cardiopulmonary resuscitation; Defibrillation; Defibrillator; Public-access defibrillation; Survival

Mesh:

Year:  2014        PMID: 25281188     DOI: 10.1016/j.resuscitation.2014.08.038

Source DB:  PubMed          Journal:  Resuscitation        ISSN: 0300-9572            Impact factor:   5.262


  3 in total

1.  Combining Amplitude Spectrum Area with Previous Shock Information Using Neural Networks Improves Prediction Performance of Defibrillation Outcome for Subsequent Shocks in Out-Of-Hospital Cardiac Arrest Patients.

Authors:  Mi He; Yubao Lu; Lei Zhang; Hehua Zhang; Yushun Gong; Yongqin Li
Journal:  PLoS One       Date:  2016-02-10       Impact factor: 3.240

2.  Return of spontaneous circulation and long-term survival according to feedback provided by automated external defibrillators.

Authors:  M Agerskov; M B Hansen; A M Nielsen; T P Møller; M Wissenberg; L S Rasmussen
Journal:  Acta Anaesthesiol Scand       Date:  2017-09-13       Impact factor: 2.105

3.  Community first response and out-of-hospital cardiac arrest: Identifying priorities for data collection, analysis, and use via the nominal group technique.

Authors:  Eithne Heffernan; Dylan Keegan; Jenny Mc Sharry; Tomás Barry; Peter Tugwell; Andrew W Murphy; Conor Deasy; David Menzies; Cathal O'Donnell; Siobhan Masterson
Journal:  Resusc Plus       Date:  2022-01-10
  3 in total

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