DeepCRI: Real-time EEG-based Prognostication after Cardiac Arrest

Abstract Accurate prediction of neurological outcome after cardiac arrest is essential for guiding intensive care decisions. Electroencephalography (EEG) supports prognostication; however, interpretation relies on expert judgment and is often subjective and delayed. We developed DeepCRI, a bedside-integrated deep learning system that produces continuously updated prognostic trajectories during the first 36 hours after arrest. DeepCRI uses time-dependent decision boundaries to define good-, poor-, and gray-zone regions over time, and applies a lock-in rule that fixes classification only after sustained, concordant high-confidence evidence within a compact temporal window, thereby preventing transient threshold crossings from driving decisions. During model development in a cohort of 522 patients, DeepCRI achieved an area under the receiver operating characteristic curve (AUC) of 0.97 at 24 h, with low calibration error (ECE=0.049). Independent validation was performed in an internal (n=219) and an external cohort (n=167). In the internal validation, DeepCRI provided lock-in classifications in 81.7% of patients, achieving 100% specificity for poor outcome with a sensitivity of 49.5%, and 95.5% sensitivity for good outcome at 73.2% specificity; 18.3% remained in the gray zone. Performance in the external validation cohort was lower: 59.9% locked, and a single false predictions reduced poor-outcome specificity to 98.4%. Post hoc analysis indicated residual EMG artifacts contributed to this false poor-outcome prediction. By embedding DeepCRI into routine ICU EEG infrastructure, we demonstrate the technical feasibility and clinical promise of continuous, real-time AI-driven prognostication for comatose patients after cardiac arrest. Competing Interest Statement MvP is co-founder of Clinical Science Systems, a manufacturer of EEG software (NeuroCenter EEG) 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: The Medical Ethical Committee Twente approved the protocol and waived the need for informed consent as EEG monitoring and clinical follow-up are part of standard care in the participating centers. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. 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, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Update of methods and hyperparameters to define boundaries for lock-in. Slight change in performance. Data Availability Data can be made available upon reasonable request from the corresponding author.