Novel CPR system that predicts return of spontaneous circulation from amplitude spectral area before electric shock in ventricular fibrillation. (April 2017)

• Record Type: Journal Article
• Authors: Nakagawa, Yoshihide; Amino, Mari; Inokuchi, Sadaki; Hayashi, Satoshi; Wakabayashi, Tsutomu; Noda, Tatsuya
• Is Part Of: Resuscitation. Volume 113(2017)
• Page Start: 8
• Page End: 12
• Publication Date: 2017-04
• Subjects: AMSA amplitude spectral area -- AUC area under the curve -- AED automated external defibrillators -- CPP coronary perfusion pressure -- ECG electrocardiogram -- EMS emergency medical service -- ROC receiver operating curves -- ROSC return of spontaneous circulation -- SMCC Shōnan-area Medical Control Council -- VF ventricular fibrillation; Ventricular fibrillation -- Defibrillation -- Out-of-hospital cardiac arrest -- Waveform analysis -- Amplitude spectral area; Resuscitation -- Periodicals; Resuscitation -- Periodicals; Réanimation -- Périodiques; Electronic journals; 616.025
• DOI: 10.1016/j.resuscitation.2016.12.025
• Languages: English
• ISSNs: 0300-9572

Abstract

Abstract: Aim: Amplitude spectral area (AMSA), an index for analysing ventricular fibrillation (VF) waveforms, is thought to predict the return of spontaneous circulation (ROSC) after electric shocks, but its validity is unconfirmed. We developed an equation to predict ROSC, where the change in AMSA (ΔAMSA) is added to AMSA measured immediately before the first shock (AMSA1). We examine the validity of this equation by comparing it with the conventional AMSA1-only equation. Method: We retrospectively investigated 285 VF patients given prehospital electric shocks by emergency medical services. ΔAMSA was calculated by subtracting AMSA1 from last AMSA immediately before the last prehospital electric shock. Multivariate logistic regression analysis was performed using post-shock ROSC as a dependent variable. Results: Analysis data were subjected to receiver operating characteristic curve analysis, goodness-of-fit testing using a likelihood ratio test, and the bootstrap method. AMSA1 (odds ratio (OR) 1.151, 95% confidence interval (CI) 1.086–1.220) and ΔAMSA (OR 1.289, 95% CI 1.156–1.438) were independent factors influencing ROSC induction by electric shock. Area under the curve (AUC) for predicting ROSC was 0.851 for AMSA1-only and 0.891 for AMSA1 + ΔAMSA. Compared with the AMSA1-only equation, the AMSA1 + ΔAMSA equation had significantly better goodness-of-fit (likelihood ratio test P < 0.001) and showed good fit in the bootstrap method. Conclusions: Post-shock ROSC was accurately predicted by adding ΔAMSA to AMSA1. AMSA-based ROSC prediction enables application of electric shock to only those patients with high probability of ROSC, instead of interrupting chest compressions and delivering unnecessary shocks to patients with low probability of ROSC.