Predicting defibrillation success in sudden cardiac arrest patients

Although the importance of quality cardiopulmonary resuscitation (CPR) and its link to survival is still emphasized, there has been recent debate about the balance between CPR and defibrillation, particularly for long response times. Defibrillation shocks for ventricular fibrillation (VF) of recently perfused hearts have high success for the return of spontaneous circulation (ROSC), but hearts with depleted adenosine triphosphate (ATP) stores have low recovery rates. Since quality CPR has been shown to both slow the degradation process and restore cardiac viability, a measurement of patient condition to optimize the timing of defibrillation shocks may improve outcomes compared to time-based protocols. Researchers have proposed numerous predictive features of VF and shockable ventricular tachycardia (VT) which can be computed from the electrocardiogram (ECG) signal to distinguish between the rhythms which convert to spontaneous circulation and those which do not. We looked at the shock-success prediction performance of thirteen of these features on a single evaluation database including the recordings from 116 out-of-hospital cardiac arrest patients which were collected for a separate study using defibrillators in ambulances and medical centers in 4 European regions and the US between March 2002 and September 2004. A total of 469 shocks preceded by VF or shockable VT rhythm episodes were identified in the recordings. Based on the experts' annotation for the post-shock rhythm, the shocks were categorized to result in either pulsatile (ROSC) or non-pulsatile (no-ROSC) rhythm. The features were calculated on a 4-second ECG segment prior to the shock delivery. These features examined were: Mean Amplitude, Average Peak-Peak Amplitude, Amplitude Range, Amplitude Spectrum Analysis (AMSA), Peak Frequency, Centroid Frequency, Spectral Flatness Measure (SFM), Energy, Max Power, Centroid Power, Power Spectrum Analysis (PSA), Mean Slope, and Median Slope. Statistical hypothesis tests (two-tailed t-test and Wilcoxon with 5% significance level) were applied to determine if the means and medians of these features were significantly different between the ROSC and no-ROSC groups. The ROC curve was computed for each feature, and Area Under the Curve (AUC) was calculated. Specificity (Sp) with Sensitivity (Se) held at 90% as well as Se with Sp held at 90% was also computed. All features showed statistically different mean and median values between the ROSC and no-ROSC groups with all p-values less than 0.0001. The AUC was >76% for all features. For Sp = 90%, the Se range was 33-45%; for Se = 90%, the Sp range was 49-63%. The features showed good shock-success prediction performance. We believe that a defibrillator employing a clinical decision tool based on these features has the potential to improve overall survival from cardiac arrest.