Tau Protein Mediates the Association Between Frailty and Postoperative Delirium: A Machine Learning Model Incorporating Cerebrospinal Fluid Biomarkers

Yizhi Liang¹Chuanlin Mu¹Wenjie Kong²Kun Wang²Shuhui Hua³Yuanlong Wang³Xia Liu³Hongyan Gong¹Yanan Lin¹Chuan Li¹Xu Lin¹Yanlin Bi¹Bin Wang^1*

• ¹Qingdao Municipal Hospital, Qingdao, China
• ²Weifang Medical College, Weifang, Shandong Province, China
• ³Binzhou Medical University Hospital, Binzhou, Shandong Province, China

Postoperative delirium (POD) is a prevalent neurological complication linked to adverse clinical outcomes. The underlying mechanisms of POD remain unclear. This study aimed to investigate the association between POD and frailty and determine whether frailty influences POD incidence. Furthermore, machine learning algorithms were utilized to identify key predictors of POD in patients undergoing hip or knee replacement. Methods: A total of 625 Han Chinese patients were recruited between September 2021 and May 2023. Preoperative frailty was assessed using the Frailty Scale and Frailty Phenotype criteria. The Mini-Mental State Examination (MMSE) evaluated preoperative cognitive function, while the Confusion Assessment Method (CAM) diagnosed POD. The severity of POD was additionally quantified using the Memorial Delirium Assessment Scale (MDAS). Receiver Operating Characteristic (ROC) curve analysis explored the association between preoperative frailty and POD, and the mediating effect of cerebrospinal fluid (CSF) biomarkers was analyzed. Ten machine learning algorithms-including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Artificial Neural Network (ANN), Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), AdaBoost, LightGBM, and CatBoost-were implemented to develop predictive models. The dataset was randomly split into training (70%) and testing (30%) subsets. Ten-fold cross-validation was incorporated during model training and validation to mitigate overfitting and enhance generalizability. Model performance was evaluated using multiple metrics, such as accuracy, sensitivity, specificity, precision, Brier score, area under the ROC curve (AUC), and F1 score. Furthermore, graphical analyses-including calibration curves, decision diagrams, clinical impact curves, and confusion matrices-were applied to assess model robustness and clinical utility. Finally, SHAP (Shapley Additive Explanations) analysis elucidated the model's decision-making process, emphasizing the pivotal role of preoperative frailty in POD prediction.