Neha Aluru

Background: Postoperative delirium (POD) is an acute disturbance in attention, awareness, and cognition that affects up to 15-25% of elderly, and is associated with longer hospitalizations, higher complication rates, functional decline, and increased healthcare costs.¹ Since POD is linked to substantial short and long-term patient harm, identifying preventable perioperative contributors is an important clinical goal. Prior literature has established that POD risk stems from nonmodifiable factors such as older age, frailty, lower preoperative cognition, type of surgery, and comorbidities.^1,2 However, certain intraoperative factors may act as modifiable factors, specifically excessive anesthetic depth, burst suppression, perioperative hypotension, and impaired cerebral perfusion.^3,4 Although EEG-based neuromonitoring and cerebral perfusion monitoring are increasingly used to guide anesthetic care, it remains unclear which signals best mitigate POD risk and how to integrate them into individualized management. This review examines whether neuromonitoring of modifiable risk factors can help identify pathways for reducing POD.

Methods: Primary studies were searched in PubMed and their reference lists were utilized to identify additional work, restricting inclusion to primary articles published between 2020 and 2026.

Results: A randomized trial of lighter versus deeper anesthesia (separated by ~0.2 MAC units) revealed targeting lighter depth reduced POD by 9% and improved one-year cognitive outcomes by 11% in elderly, supporting anesthetic depth as a modifiable risk factor.³ EEG studies demonstrated lower frontal alpha power was associated with a greater likelihood of burst suppression under anesthesia, with each 1 dB decrease associated with 1.33-fold higher odds.⁴ Lower baseline cognition was linked with intraoperative EEG features consistent with deeper anesthetic states, supporting patient-specific brain vulnerability.⁴ Personalized intraoperative BP management saw increased cerebral oxygen saturation and decreased burst suppression, showing perfusion also modifies anesthetic depth.⁵ While raw EEG-guided anesthesia did not significantly change postoperative complication rates, it helped reduce time spent in burst suppression.⁶ Bispectral Index (BIS)-guided anesthetic management did not reduce POD incidence but improved early recovery quality including shorter PACU stay and faster emergence-related outcomes.⁷ Finally, machine-learning models using EEG-derived features classifying anesthetic depth relative to BIS-defined states reached 83% accuracy using random forest models.⁸

Conclusion: Mitigation of POD relies on recognition of heterogenous brain vulnerability with careful titration of anesthetic depth, minimizing burst suppression, and maintaining cerebral perfusion. Combining raw EEG, BIS, and hemodynamic monitoring is more protective than any single modality. Future work should integrate EEG phenotypes, cognitive vulnerability, anesthetic exposure, and perfusion into personalized AI models that can detect real-time delirium risk and guide targeted prevention.

Works Cited

1. Raats, J. W., van Eijsden, W. A., Crolla, R. M., Steyerberg, E. W., & van der Laan, L. (2015). Risk Factors and Outcomes for Postoperative Delirium after Major Surgery in Elderly Patients. PloS one, 10(8), e0136071. https://doi.org/10.1371/journal.pone.0136071
2. Gutiérrez, R. G., Egaña, J. I., Maldonado, F. A., Sáez, I. A., Reyes, F. I., Soulat, H., Purdon, P. L., & Penna, A. (2021). Association Between Lower Preoperative Cognition With Intraoperative Electroencephalographic Features Consistent With Deep States of Anesthesia in Older Patients: An Observational Cohort Study. Anesthesia and analgesia, 133(1), 205–214. https://doi.org/10.1213/ANE.0000000000005262
3. Evered, L. A., Chan, M. T. V., Han, R., Chu, M. H. M., Cheng, B. P., Scott, D. A., Pryor, K. O., Sessler, D. I., Veselis, R., Frampton, C., Sumner, M., Ayeni, A., Myles, P. S., Campbell, D., Leslie, K., & Short, T. G. (2021). Anaesthetic depth and delirium after major surgery: a randomised clinical trial. British journal of anaesthesia, 127(5), 704–712. https://doi.org/10.1016/j.bja.07.021
4. Shao, Y. R., Kahali, P., Houle, T. T., Deng, H., Colvin, C., Dickerson, B. C., Brown, E. N., & Purdon, P. L. (2020). Low Frontal Alpha Power Is Associated With the Propensity for Burst Suppression: An Electroencephalogram Phenotype for a “Vulnerable Brain”. Anesthesia and analgesia, 131(5), 1529–1539. https://doi.org/10.1213/ANE.0000000000004781
5. Khader, W., Hein, M., Kouz, K., Bergholz, A., Saugel, B., Wallqvist, J., Goldmann, S., Gräfe, K., Larmann, J., & Grüßer, L. (2026). The effect of personalized perioperative blood pressure management on intraoperative cerebral oxygen saturation, burst suppression ratio and postoperative neurological outcomes in patients having major non-cardiac surgery: an observational substudy of the IMPROVE-pilot randomized controlled trial. Journal of clinical monitoring and computing, 40(2), 363–373. https://doi.org/10.1007/s10877-025-01402-y
6. He, Z., Zhang, H., Xing, Y., Liu, J., Gao, Y., Gu, E., Zhang, L., & Chen, L. (2023). Effect of raw electroencephalogram-guided anesthesia administration on postoperative outcomes in elderly patients undergoing abdominal major surgery: a randomized controlled trial. BMC anesthesiology, 23(1), 337. https://doi.org/10.1186/s12871-023-02297-5
7. Shi, M., Long, Y., Zhou, Z., Huang, L., Wu, D., & Zhang, X. (2025). The Impact of Anesthetic Management Under Bispectral Index Monitoring on the Early Recovery Quality of Elderly Patients Undergoing Laparoscopic Surgery: A Blinded Randomized Controlled Trial. Clinical interventions in aging, 20, 597–612. https://doi.org/10.2147/CIA.S507758
8. Anand, R. V., Abbod, M. F., Fan, S.-Z., & Shieh, J.-S. (2023). Depth Analysis of Anesthesia Using EEG Signals via Time Series Feature Extraction and Machine Learning. Sci, 5(2), 19. https://doi.org/10.3390/sci5020019