Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning
Year: 2021
Type: article
Abstract: OBJECTIVE: Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information. METHODS: We present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation. RESULTS: The proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)%, (55,95)%, and (62,90)%, (99,23)%, (95,47)%, and (90,62)%; whereas for predicting good outcome, the corresponding operating points were (17,99)%, (47,95)%, (62,90)%, (99,19)%, (95,48)%, (70,90)%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04). CONCLUSIONS AND SIGNIFICANCE: These findings suggest that accounting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest.
Institutions: Harvard University, Massachusetts General Hospital, Massachusetts Institute of Technology, University of California System, Michigan State University, Brigham and Women's Hospital, Yale University, Beth Israel Deaconess Medical Center, Barrow Neurological Institute, Université Libre de Bruxelles, University of Twente, Medisch Spectrum Twente, Rijnstate Hospital
Language: English
Subfield: Emergency Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: American Heart Association, Massachusetts Institute of Technology, Neurocritical Care Society, Epilepsiefonds, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering
Awards: Multimodal Network Connectivity Architecture (MOCA) of the Brain and its Role in the Recovery of Consciousness in Comatose Cardiac Arrest Patients, G1815946096, G2074738218, G2524655255, G3137807044, G3829564212, Advanced Multimodal Neuroimaging Training Grant, G4725628851, Establishing a Brain Health Index, Quantitative Monitoring and Control of Sedation and Pain in the ICU Environment, Investigation of Sleep, Big Data and Deep Learning for the Interictal-Ictal-Injury Contiuum, G9000279194, Neuroimaging Training Program, Training in Sleep, Circadian, and Respiratory Neurobiology
OpenAlex ID: W4200076046