ENTERPRISE AI ANALYSIS
Development of a deep learning-based prediction model for postoperative delirium using intraoperative electroencephalogram in adults
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.
Executive Impact & Key Findings
The DELPHI-EEG model, a deep learning solution, significantly outperforms traditional logistic regression and other ML models in predicting postoperative delirium. Leveraging raw EEG waveforms and spatiotemporal features, it achieves an AUROC of 0.870, offering an advanced, real-time risk stratification tool for surgical patients.
0.870
AUROC
0.038
AUPRC
34550
Cases Analyzed
Deep Analysis & Enterprise Applications
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Model Performance
Interpretability
Clinical Significance
The DELPHI-EEG model exhibited superior performance with an AUROC of 0.870 (95% CI: 0.789–0.935) and an AUPRC of 0.038 (95% CI: 0.017–0.084), significantly outperforming baseline logistic regression (AUROC 0.729, AUPRC 0.013). This indicates a strong capability for discriminating between patients who will and will not develop POD.
Interpretability analysis revealed that reduced relative alpha power was strongly correlated with POD risk, consistent with previous studies on thalamic hyperpolarization and thalamocortical synchronization. Increased delta and theta power also showed significant associations. Ablation studies confirmed the importance of alpha, beta, slow, and theta bands, with alpha being the most critical for model performance.
The DELPHI-EEG model's real-time prediction capability for POD could enable targeted preventive interventions early in the postoperative course, potentially improving patient outcomes and reducing healthcare costs. It processes raw EEG waveforms, eliminating the need for complex manual feature engineering and enhancing generalizability across different surgical populations.
DELPHI-EEG Model AUROC
0.870 AUROC (95% CI: 0.789-0.935) for POD PredictionDELPHI-EEG Development Process
Eligible Criteria (35,115 cases)
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Exclusion (213 cases)
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Analysis (34,550 cases)
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Initial Development Set (31,043 cases)
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5:1 Undersampling
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Development Set (804 negative, 142 positive samples)
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5-fold Cross Validation
| Model | AUROC | AUPRC | Key Advantages |
|---|---|---|---|
| DELPHI-EEG | 0.870 | 0.038 |
|
| Logistic Regression | 0.729 | 0.013 |
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| XGBoost | 0.801 | 0.042 |
|
Clinical Impact Scenario
A 70-year-old patient undergoing major abdominal surgery is identified by DELPHI-EEG as having a high risk of POD based on intraoperative EEG patterns, even without overt burst suppression. Proactive interventions, such as tailored anesthetic management and early post-operative mobilization, are initiated. The patient experiences a smoother recovery with no episodes of delirium, potentially reducing hospital stay by 3 days and saving an estimated $5,000 in healthcare costs.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach to integrating DELPHI-EEG into your clinical workflow.
Phase 1: Assessment & Data Integration (Weeks 1-4)
Initial consultation to understand your specific clinical environment and existing data infrastructure. Securely integrate intraoperative EEG data streams and relevant patient demographics.
Phase 2: Model Customization & Validation (Weeks 5-8)
Refine DELPHI-EEG model parameters based on your institution's patient population and clinical protocols. Conduct internal validation with retrospective data to ensure accuracy and relevance.
Phase 3: Pilot Deployment & Training (Weeks 9-12)
Implement the DELPHI-EEG model in a pilot clinical setting (e.g., a specific surgical unit). Provide comprehensive training for anesthesiologists, surgeons, and nursing staff on interpreting predictions and initiating preventive interventions.
Phase 4: Full Integration & Ongoing Optimization (Month 4+)
Expand DELPHI-EEG deployment across all relevant surgical departments. Establish continuous monitoring, feedback loops, and regular model updates to ensure sustained high performance and adapt to evolving clinical practices.
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