ArticleJournal of the American Heart Association2025
Combining Ventricular Fibrillation Features With Defibrillation Waveform Parameters Improves the Ability to Predict Shock Outcomes in a Rabbit Model of Cardiac Arrest.
Article in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
backgroundQuantitative 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.
methodsVF 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.
resultsThe 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];
conclusionsIn 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.
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