Association between intraoperative electroencephalograph complexity index and postoperative delirium in elderly...

The primary method for predicting POD (postoperative confusion) relies on the analysis of clinical features. Brain activity complexity is a promising factor associated with the state of consciousness. The aim of this study was to investigate the role of EEG (electroencephalography) complexity changes in predicting POD in elderly patients undergoing orthopedic surgery.

From January 2024 to August 2024, 289 elderly patients undergoing orthopedic surgery were recruited at the Second Affiliated Hospital of Nanjing Medical University. Intraoperative EEG data from patients were collected and then EEG nonlinear features were extracted by MATLAB custom scripts. The logistic regression and CNN (convolutional neural networks) were used to explore the predictive effect of nonlinear features on POD from both static and dynamic perspectives.

Low permutation Lempel-Ziv complexity (PLZC) among the EEG nonlinear features emerged as an independent risk factor for POD [OR = 0.210; 95% CI (0.050-0.850); p = 0.029]. Receiver operating characteristic curve (ROC) analysis revealed a poor area under the curve of 0.615 (95% CI 0.517-0.711) for PLZC in predicting POD. After the inclusion of temporal factors, the ROC analysis indicated that the EEG nonlinear indices had a moderate predictive effect on POD [AUC = 0.701; (95% CI 0.541-0.862)].

EEG nonlinear feature indices may be effective biomarkers for POD and could help predict POD in elderly patients undergoing orthopedic surgery.