Literature DB >> 28923243

Sensitivity and specificity of two different automated external defibrillators.

Johan Israelsson1, Burkard von Wangenheim2, Kristofer Årestedt3, Birgitta Semark4, Kristina Schildmeijer5, Jörg Carlsson6.   

Abstract

AIM: The aim was to investigate the clinical performance of two different types of automated external defibrillators (AEDs).
METHODS: Three investigators reviewed 2938 rhythm analyses performed by AEDs in 240 consecutive patients (median age 72, q1-q3=62-83) who had suffered cardiac arrest between January 2011 and March 2015. Two different AEDs were used (AED A n=105, AED B n=135) in-hospital (n=91) and out-of-hospital (n=149).
RESULTS: Among 194 shockable rhythms, 17 (8.8%) were not recognized by AED A, while AED B recognized 100% (n=135) of shockable episodes (sensitivity 91.2 vs 100%, p<0.01). In AED A, 8 (47.1%) of these episodes were judged to be algorithm errors while 9 (52.9%) were caused by external artifacts. Among 1039 non-shockable rhythms, AED A recommended shock in 11 (1.0%), while AED B recommended shock in 63 (4.1%) of 1523 episodes (specificity 98.9 vs 95.9, p<0.001). In AED A, 2 (18.2%) of these episodes were judged to be algorithm errors (AED B, n=40, 63.5%), while 9 (81.8%) were caused by external artifacts (AED B, n=23, 36.5%).
CONCLUSIONS: There were significant differences in sensitivity and specificity between the two different AEDs. A higher sensitivity of AED B was associated with a lower specificity while a higher specificity of AED A was associated with a lower sensitivity. AED manufacturers should work to improve the algorithms. In addition, AED use should always be reviewed with a routine for giving feedback, and medical personnel should be aware of the specific strengths and shortcomings of the device they are using.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  AED; Arrhythmia; Defibrillation; Sensitivity; Specificity

Mesh:

Year:  2017        PMID: 28923243     DOI: 10.1016/j.resuscitation.2017.09.009

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


  3 in total

1.  Atrial fibrillation mimicking ventricular fibrillation confuses an automated external defibrillator.

Authors:  M Hulleman; M T Blom; A Bardai; H L Tan; R W Koster
Journal:  Neth Heart J       Date:  2018-05       Impact factor: 2.380

2.  Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms.

Authors:  Vessela Krasteva; Sarah Ménétré; Jean-Philippe Didon; Irena Jekova
Journal:  Sensors (Basel)       Date:  2020-05-19       Impact factor: 3.576

3.  Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations.

Authors:  Jasmeet Soar; Katherine M Berg; Lars W Andersen; Bernd W Böttiger; Sofia Cacciola; Clifton W Callaway; Keith Couper; Tobias Cronberg; Sonia D'Arrigo; Charles D Deakin; Michael W Donnino; Ian R Drennan; Asger Granfeldt; Cornelia W E Hoedemaekers; Mathias J Holmberg; Cindy H Hsu; Marlijn Kamps; Szymon Musiol; Kevin J Nation; Robert W Neumar; Tonia Nicholson; Brian J O'Neil; Quentin Otto; Edison Ferreira de Paiva; Michael J A Parr; Joshua C Reynolds; Claudio Sandroni; Barnaby R Scholefield; Markus B Skrifvars; Tzong-Luen Wang; Wolfgang A Wetsch; Joyce Yeung; Peter T Morley; Laurie J Morrison; Michelle Welsford; Mary Fran Hazinski; Jerry P Nolan
Journal:  Resuscitation       Date:  2020-10-21       Impact factor: 5.262

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.