# Development of a deep learning-based prediction model for postoperative delirium using intraoperative electroencephalogram in adults

## TL;DR

A deep learning model called DELPHI-EEG was developed to predict postoperative delirium using EEG data during surgery, showing better performance than traditional methods.

## Contribution

The novel contribution is the development of a deep learning model for predicting postoperative delirium using intraoperative EEG data.

## Key findings

- DELPHI-EEG achieved an AUROC of 0.870 and AUPRC of 0.038 during cross-validation.
- It outperformed logistic regression models using burst suppression ratio in predicting delirium.
- External validation in diverse clinical settings is needed for broader application.

## 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.