RESUMEN
Optimal defibrillation timing guided by ventricular fibrillation (VF) waveform analysis would contribute to improved survival of out-of-hospital cardiac arrest (OHCA) patients by minimizing myocardial damage caused by futile defibrillation shocks and minimizing interruptions to cardiopulmonary resuscitation. Recently, fuzzy entropy (FuzzyEn) tailored to jointly measure VF amplitude and regularity has been shown to be an efficient defibrillation success predictor. In this study, 734 shocks from 296 OHCA patients (50 survivors) were analyzed, and the embedding dimension (m) and matching tolerance (r) for FuzzyEn and sample entropy (SampEn) were adjusted to predict defibrillation success and patient survival. Entropies were significantly larger in successful shocks and in survivors, and when compared to the available methods, FuzzyEn presented the best prediction results, marginally outperforming SampEn. The sensitivity and specificity of FuzzyEn were 83.3% and 76.7% when predicting defibrillation success, and 83.7% and 73.5% for patient survival. Sensitivities and specificities were two points above those of the best available methods, and the prediction accuracy was kept even for VF intervals as short as 2s. These results suggest that FuzzyEn and SampEn may be promising tools for optimizing the defibrillation time and predicting patient survival in OHCA patients presenting VF.
RESUMEN
BACKGROUND AND AIM: Unsuccessful defibrillation shocks adversely affect survival from out-of-hospital cardiac arrest (OHCA). Ventricular fibrillation (VF) waveform analysis is the tool-of-choice for the non-invasive prediction of shock success, but surrogate markers of perfusion like end-tidal CO2 (EtCO2) could improve the prediction. The aim of this study was to evaluate EtCO2 as predictor of shock success, both individually and in combination with VF-waveform analysis. MATERIALS AND METHODS: In total 514 shocks from 214 OHCA patients (75 first shocks) were analysed. For each shock three predictors of defibrillation success were automatically calculated from the device files: two VF-waveform features, amplitude spectrum area (AMSA) and fuzzy entropy (FuzzyEn), and the median EtCO2 (MEtCO2) in the minute before the shock. Sensitivity, specificity, receiver operating characteristic (ROC) curves and area under the curve (AUC) were calculated, for each predictor individually and for the combination of MEtCO2 and VF-waveform predictors. Separate analyses were done for first shocks and all shocks. RESULTS: MEtCO2 in first shocks was significantly higher for successful than for unsuccessful shocks (31mmHg/25mmHg, p<0.05), but differences were not significant for all shocks (32mmHg/29mmHg, p>0.05). MEtCO2 predicted shock success with an AUC of 0.66 for first shocks, but was not a predictor for all shocks (AUC 0.54). AMSA and FuzzyEn presented AUCs of 0.76 and 0.77 for first shocks, and 0.75 and 0.75 for all shocks. For first shocks, adding MEtCO2 improved the AUC of AMSA and FuzzyEn to 0.79 and 0.83, respectively. CONCLUSIONS: MEtCO2 predicted defibrillation success only for first shocks. Adding MEtCO2 to VF-waveform analysis in first shocks improved prediction of shock success. VF-waveform features and MEtCO2 were automatically calculated from the device files, so these methods could be introduced in current defibrillators adding only new software.