Development of a deep learning-based prediction model for postoperative delirium using intraoperative electroencephalogram in adults.
retrospective_cohort · Level III
Where this comes from
- Record sourced from PubMed, PMID 41249487.
- Also identified by DOI 10.1038/s41746-025-02033-y and PMC identifier 12623934.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Postoperative delirium (POD) is associated with increased morbidity and mortality. This study aims to develop a deep learning-based model (DELPHI-EEG) to predict postoperative delirium using intraoperative electroencephalogram (EEG) waveform. A total of 34,550 surgical cases (267 event cases), with 6-lead intraoperative EEG monitoring between 2022 and 2024, were included for model development. During 5-fold cross-validation, the DELPHI-EEG model showed an area under the receiver operating characteristic (AUROC) curve of 0.870 (95% confidence interval [CI]: 0.789-0.935) and the area under the precision-recall curve (AUPRC) of 0.038 (95% CI: 0.017-0.084), significantly outperforming the logistic regression model using burst suppression ratio with AUROC of 0.729 (95% CI: 0.624-0.825, p = 0.004) and AUPRC of 0.013 (95% CI: 0.007-0.026, p = 0.002). The DELPHI-EEG model might serve as a risk predictor for postoperative delirium, potentially enabling targeted preventive interventions for surgical patients; nonetheless, external validation in diverse clinical settings is required.