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

Abstract

Background: Severity 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. Methods: We 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. Results: Analysis 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 conclude, statistics 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.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

his study is an ancillary study of the PHRC CAPACITY AOR10109 and was approved by the ethics committee (Comité de Protection des Personnes, CPP Paris IV #2012/22).

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Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

All data produced in the present study are available upon reasonable request to the authors

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