Two-dimensional maps to predict the neurological recovery after cardiac arrest

Abstract

AbstractBackgroundSeverity of neuronal damage in comatose patients following anoxic brain injury is assessed through a multimodal evaluation. However, predicting the return to full consciousness of hospitalized post-anoxic comatose patients remains challenging.MethodsWe present here a method to predict the return to consciousness and good neurological outcome based on the analysis of responses to auditory periodic stimulations to auditory evoked potentials. We extracted several EEG features from the time series responses in a window of few hundreds of milliseconds from the standard and deviant auditory stimulations that we considered independently. By combining these features, we built a two-dimensional map to evaluate possible group clustering. Using Gaussian, K-neighbourhood and SVM classifiers, we could predict the neurological outcome of post-anoxic comatose patients, the validity of the method being tested by a cross-validation procedure. This method was developed using data acquired retrospectively in a cohort of 29 post-cardiac arrest comatose patients, recorded between day 3 and day 6 following admission. Data from event-related potentials (ERPs) were recorded non-invasively with four surface cranial electrodes at electro-encephalography (EEG), that we computed secondarily.ResultsAnalysis in two-dimensions of the present data revealed two separated clusters of patients with good versus bad neurological outcome. When favouring the highest specificity of our mathematical algorithms (0.91), we found a sensitivity of 0.83 and an accuracy of 0.90, maintained when calculation was performed using data from only one central electrode.To concludestatistics of standard and deviant responses considered separately provide complementary and confirmatory predictions of the outcome of anoxic comatose patients, better assessed when combining these features on a two-dimensional statistical map. The benefit of this method compared to classical EEG and ERP predictors should be tested in a large prospective cohort. If validated, this method could provide an alternative tool to intensivists, to better evaluate neurological outcome and improve patient management, without neurophysiologist assistance.