Within-patient correlation influence on defibrillation outcome prediction using a Gaussian Mixture Model

In this study, we examine whether it is reliable to use all successive shocks from one patient for the development of an outcome predictor model to discriminate “Success” versus “No success”. Vector of predictors v are extracted from time and non-linear dynamics domains and a Gaussian Mixture Model-based bayesian classifier, with probability density estimated by the Expectation-Maximization algorithm, is applied in order to detect shocks with “Success” according to the probability P(v/Success). A cross-validation analysis is performed independently on 136 first shocks (Group1) and 382 second and later shocks (Group2). At 5 s post-shock, an Organized Rhythm (OR) is considered as “Success” and Ventricular Fibrillation (VF) is defined as “No success”. A decrease in performance of discrimination of OR versus VF between Group1 and Group2 is observed with an Area Under the ROC Curve of 0.82 and 0.65, respectively. This corroborates the current hypothesis that within-patient correlation affects defibrillation outcome prediction accuracy.