Combining Ventricular Fibrillation Features With Defibrillation Waveform Parameters Improves the Ability to Predict Shock Outcomes in a Rabbit Model of Cardiac Arrest.

Abstract

Background: Quantitative ventricular fibrillation (VF) analysis has the potential to optimize defibrillation by predicting shock outcomes, but its performance remains unsatisfactory. This study investigated whether combining VF features with defibrillation parameters could enhance the ability of shock outcome prediction.

Methods: VF was electrically induced and left untreated for 30 to 180 seconds in 55 New Zealand rabbits. A defibrillatory shock was applied with 1 of 9 biphasic waveforms with different tilts and durations. A 4-step up-and-down protocol was used to maintain the success rate near 50% for each waveform. Ten features and 10 parameters were obtained from the recorded VF and defibrillation waveforms. Logistic regression and a convolutional neural network were used to combine VF features with defibrillation parameters.

Results: The area under the curve value for the combination of a single VF feature and a single defibrillation parameter (0.725 [95% CI, 0.676-0.775] versus 0.644 [95% CI, 0.589-0.699]; P=0.002) was significantly greater than that for the optimal VF feature. The area under the curve value for the combination of multiple VF features and multiple defibrillation parameters (0.752 [95% CI, 0.704-0.800] versus 0.657 [95% CI, 0.602-0.712]; P<0.001) was significantly greater than that the combination of multiple VF features. The area under the curve for the combination of the raw VF waveform and raw defibrillation waveform (0.781 [95% CI, 0.734-0.828] versus 0.685 [95% CI, 0.632-0.738]; P=0.007) was significantly greater than that for the raw VF waveform.

Conclusions: In this animal model, combining VF features with defibrillation parameters greatly enhanced the ability of shock outcome prediction, whether it was based on extracted features/parameters or directly using raw waveforms with machine learning methods.