EEG-based machine-learning prognostication in comatose patients with indeterminate outcome after cardiac arrest

Article Article scientifique Article EEG-based machine-learning prognostication in comatose patients with indeterminate outcome after cardiac arrest eng 2026-03-27 2026-03-27 author Xhepa Bernasconi Sophie Lucia Cherubina Neurology Unit, Department of Clinical Neurosciences, University Hospital of Geneva, Geneva, Switzerland.,Neuro-Intensive Care Unit, Department of Intensive Care, University Hospital of Geneva, Geneva, Switzerland. 444633 author Degano Giulio Neurology Unit, Department of Clinical Neurosciences, University Hospital of Geneva, Geneva, Switzerland.,Neuro-Intensive Care Unit, Department of Intensive Care, University Hospital of Geneva, Geneva, Switzerland. 950125 Département des neurosciences cliniques (OTONO) author Francini Nikita Neurology Unit, Department of Clinical Neurosciences, University Hospital of Geneva, Geneva, Switzerland.,Neuro-Intensive Care Unit, Department of Intensive Care, University Hospital of Geneva, Geneva, Switzerland. author Rochat Tommaso Neuro-Intensive Care Unit, Department of Intensive Care, University Hospital of Geneva, Geneva, Switzerland. 445549 author Kleinschmidt Andréas Karl Neurology Unit, Department of Clinical Neurosciences, University Hospital of Geneva, Geneva, Switzerland. 81042 Département des neurosciences cliniques (OTONO) author Quintard Hervé 0000-0002-3870-6370 Neuro-Intensive Care Unit, Department of Intensive Care, University Hospital of Geneva, Geneva, Switzerland. 951770 Département d'anesthésiologie, pharmacologie et soins intensifs (APSIC) author De Stefano Pia Neurology Unit, Department of Clinical Neurosciences, University Hospital of Geneva, Geneva, Switzerland. pia.destefano@hug.ch.,Neuro-Intensive Care Unit, Department of Intensive Care, University Hospital of Geneva, Geneva, Switzerland. pia.destefano@hug.ch. 926484 Département des neurosciences cliniques (OTONO) 237 10.1186/s13054-026-05982-2 41896890 1364-8535 PMC13151320 https://link.springer.com/10.1186/s13054-026-05982-2 <p>Neurological prognostication after cardiac arrest remains challenging despite multimodal approaches recommended by the European Resuscitation Council and the European Society of Intensive Care Medicine (ERC/ESICM). Prognostic certainty is achieved in only a subset of comatose patients, leaving a substantial proportion with an indeterminate prognosis. In this population, the incremental prognostic value of electroencephalography beyond established predictors remains insufficiently characterized. METHODS: We conducted a prospective 5-year (2021–2025) single-center observational study of adult comatose patients after cardiac arrest admitted to the Intensive Care Unit (ICU) of the University Hospitals of Geneva. Patients presenting with fewer than two ERC/ESICM poor outcome factors were included. Continuous electroencephalogram (EEG) recordings obtained within 72 h after cardiac arrest were analyzed. Quantitative EEG features reflecting spectral power, functional connectivity, signal complexity, and background continuity were extracted and combined with automated estimates of rhythmic and periodic patterns derived from a previously validated neural network. These features were integrated into a random forest classifier to predict neurological outcome at 3–6 months, dichotomized as good (Cerebral Performance Category 1–2) or poor (3–5). Model performance was evaluated using repeated stratified cross-validation, and feature contributions were assessed using Shapley value analysis. RESULTS: Of a total of 313 patients, 77 met the inclusion criteria (mean age 61, 21 female), of whom 35 (45%) achieved a good neurological outcome. The model demonstrated good discriminative performance, with a mean Receiver Operating Characteristic – Area Under the Curve (ROC-AUC) of 0.80 and a Precision–Recall Area Under the Curve (PR-AUC of 0.83). Delta-band electroencephalography features accounted for most of the predictive contribution. Higher delta functional connectivity, higher delta relative power, and higher probability of periodic discharges were associated with poor outcome, whereas preserved relative alpha power and higher probability of rhythmic delta activity were associated with good outcome. CONCLUSIONS: In comatose patients after cardiac arrest with ERC/ESICM guideline-defined indeterminate prognosis, an interpretable EEG-based prognostic model provides clinically relevant information. This approach may complement current multimodal prognostication strategies by refining early risk stratification in a population characterized by substantial prognostic uncertainty.</p> Cardiac arrest EEG Hypoxic-ischemic encephalopathy Machine learning Neurological prognostication Dewey 616.8 Dewey 617 63 Département d'anesthésiologie, pharmacologie, soins intensifs et urgences 229 61 Département des neurosciences cliniques 226 false Critical care 30 1 af9d4e45-201a-4312-a0a5-fb64fe1929d7 Article (Published version) Public s13054-026-05982-2-1.pdf 1895049 application/pdf CC BY-NC-ND-4.0 8c9dd795405d75c390332affb206351a37dffb83d4e892ae356e2c36caac7828 40136c74-7905-42e3-8c19-a19588c0bb4d Supplemental data Public 13054_2026_5982_MOESM1_ESM-1.docx 1777722 application/vnd.openxmlformats-officedocument.wordprocessingml.document 49968f9bb1a3eea48fcf4f8e1674c4cb8dce6e71a51ef3a7b4502e6c96262f03 openAccess Faculté de médecine / Section de médecine clinique / Département d'anesthésiologie, pharmacologie, soins intensifs et urgences Faculté de médecine / Section de médecine clinique / Département des neurosciences cliniques

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Xhepa Bernasconi, Sophie Lucia Cherubina