Diagnostic Accuracy of Commercially Available Automated External Defibrillators

Nishiyama, Takahiko; Nishiyama, Ako; Negishi, Masachika; Kashimura, Shin; Katsumata, Yoshinori; Kimura, Tokuhiro; Nishiyama, Nobuhiro; Tanimoto, Yoko; Aizawa, Yoshifusa; Mitamura, Hideo; Fukuda, Keiichi; Takatsuki, Seiji

doi:10.1161/jaha.115.002465

Cited by 13 publications

(18 citation statements)

References 39 publications

(54 reference statements)

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“…The incidence of incorrect ECG analysis by an AED is rare. 22 A previous study reported the error rate in the ECG rhythm assignment by EMS providers for a portion of this dataset was 3.1%. 11 Bystander AED shock was defined as any shock delivered by a bystander-applied AED prior to EMS arrival as reported by the bystander to EMS personnel or when AED records were available and indicated a shock was delivered.…”

Section: Methodsmentioning

confidence: 82%

Impact of Bystander Automated External Defibrillator Use on Survival and Functional Outcomes in Shockable Observed Public Cardiac Arrests

Brown²,

et al. 2018

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Section: Methodsmentioning

confidence: 82%

Impact of Bystander Automated External Defibrillator Use on Survival and Functional Outcomes in Shockable Observed Public Cardiac Arrests

Brown²,

et al. 2018

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“…We measured a very high sensitivity for the shockable rhythms with the best clinical outcome (VT and coarse VF). For VT, our sensitivity was 1 (1–1), while prior literature suggests that AEDs are least sensitive for these rhythms (0.36–0.96 for different AEDs in the literature) 9 18. For coarse VF, our sensitivity was similar to that of the AEDs (12).…”

Section: Discussionmentioning

confidence: 62%

“…However, in France, as in the majority of European countries, emergency physicians (EPs) can use a manual defibrillator 8. A fairly extensive literature exists on the performance (sensitivity, specificity, speed of recognition) of AEDs, in contrast to the performance of EPs, which has been poorly explored 9–13…”

Section: Introductionmentioning

confidence: 99%

Assessment of emergency physicians’ performance in identifying shockable rhythm in out-of-hospital cardiac arrest: an observational simulation study

et al. 2022

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BackgroundEmergency physicians can use a manual or an automated defibrillator to provide defibrillation of patients who had out-of-hospital cardiac arrest (OHCA). Performance of emergency physicians in identifying shockable rhythm with a manual defibrillator has been poorly explored whereas that of automated defibrillators is well known (sensitivity 0.91–1.00, specificity 0.96–0.99). We conducted this study to estimate the sensitivity/specificity and speed of shock/no-shock decision-making by prehospital emergency physicians for shockable or non-shockable rhythm, and their preference for manual versus automated defibrillation.MethodsWe developed a web application that simulates a manual defibrillator (https://simul-shock.firebaseapp.com/). In 2019, all (262) emergency physicians of six French emergency medical services were invited to participate in a study in which 60 ECG rhythms from real OHCA recordings were successively presented to the physicians for determination of whether they would or would not administer a shock. Time to decision was recorded. Answers were compared with a gold standard (concordant answers of three experts). We report sensitivity for shockable rhythms (decision to shock) and specificity for non-shockable rhythms (decision not to shock). Physicians were also asked whether they preferred manual or automated defibrillation.ResultsAmong 215 respondents, we were able to analyse results for 190 physicians. 57% of emergency physicians preferred manual defibrillation. Median (IQR) sensitivity for a shock delivery for shockable rhythm was 0.91 (0.81–1.00); median specificity for no-shock delivery for non-shockable rhythms was 0.91 (0.80–0.96). More precisely, sensitivities for shock delivery for ventricular tachycardia (VT) and coarse ventricular fibrillation (VF) were both 1.0 (1.0–1.0); sensitivity for fine VF was 0.6 (0.2–1). Specificity for not shocking a pulseless electrical activity (PEA) was 0.83 (0.72–0.86), and for asystole, specificity was 0.93 (0.86–1). Median speed of decision-making (in seconds) were: VT 2.0 (1.6–2.7), coarse VF 2.1 (1.7–2.9), asystole 2.4 (1.8–3.5), PEA 2.8 (2.0–4.2) and fine VF 2.8 (2.1–4.3).ConclusionsGlobal sensitivity and specificity were comparable with published automated external defibrillator studies. Shockable rhythms with the best clinical prognoses (VT and coarse VF) were very rapidly recognised with very good sensitivity. The decision-making for fine VF or asystole and PEA was less accurate.

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“…Another electronic equipment, which has been used commonly for the SCA treatment, is the implantable cardioverter defibrillator (ICD). However, the Se of 100% is recommended for this device 23 , which is opposite to that of the AED. It is because the ICD delivers the discharge countershock, which carries a small amount of electrical power in comparison with defibrillation provided by the AED to treat the VF/VT rhythms.…”

Section: Discussionmentioning

confidence: 99%

Deep Feature Learning for Sudden Cardiac Arrest Detection in Automated External Defibrillators

Nguyen

Kim

2018

Sci Rep

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Ventricular fibrillation and ventricular tachycardia (VF/VT), known as shockable (SH) rhythms, are the mainly cause of sudden cardiac arrests (SCA), which is cured efficiently by the automated external defibrillator (AED). The performance of the shock advice algorithm (SAA) applied in the AED has been improved by using machine learning technique and variously conventional features, recently. In this paper, we propose a novel algorithm with relatively high performance for the SCA detection on electrocardiogram (ECG) signal. The algorithm consists of a convolutional neural network as a feature extractor (CNNE) and a Boosting (BS) classifier. A grid search with nested 5-folds cross validation (CV) is used to select the CNNE trained with preprocessed ECG, SH, and NSH signals using the modified variational mode decomposition technique. The deep feature vector learned by this CNNE is extracted at the first fully connected layer and then fed into BS classifier to validate its performance using 5-folds CV procedure. The secondary learning of the BS classifier and the use of three input channels for the CNNE improve certainly the detection performance of the proposed SAA with the validated accuracy of 99.26%, sensitivity of 97.07%, and specificity of 99.44%.

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Diagnostic Accuracy of Commercially Available Automated External Defibrillators

Cited by 13 publications

References 39 publications

Impact of Bystander Automated External Defibrillator Use on Survival and Functional Outcomes in Shockable Observed Public Cardiac Arrests

Impact of Bystander Automated External Defibrillator Use on Survival and Functional Outcomes in Shockable Observed Public Cardiac Arrests

Assessment of emergency physicians’ performance in identifying shockable rhythm in out-of-hospital cardiac arrest: an observational simulation study

Deep Feature Learning for Sudden Cardiac Arrest Detection in Automated External Defibrillators

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