Tau protein mediates the association between frailty and postoperative delirium - a machine learning model incorporating cerebrospinal fluid biomarkers

Aug 4, 2026

Postoperative delirium (POD) is a frequent neurological complication with serious outcomes, but its mechanisms remain insufficiently clarified. This study assessed whether preoperative frailty is associated with POD and whether cerebrospinal fluid (CSF) biomarkers mediate this relationship. In 625 Han Chinese patients undergoing hip or knee replacement, frailty, cognition, and POD were evaluated, and ten machine learning models were built and validated using cross-validation and multiple performance metrics. Tau-related biomarkers emerged as key predictors, with GBM showing strong predictive ability.

LanguageEnglish

CategoryResearch & Report

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Pages13

Reading Time33 min

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Introduction

Objective

Methods

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TYPE Original Research PUBLISHED 17 September 2025 DOI 10.3389/fneur.2025.1608264 OPEN ACCESS EDITED BY Chun Yang, Nanjing Medical University, China REVIEWED BY Manuela Tondelli, University of Modena and Reggio Emilia, Italy Weimin Yang, First Affiliated Hospital of Zhengzhou University, China *CORRESPONDENCE Bin Wang [wangbin1@qdu.edu.cn](wangbin1@qdu.edu.cn) RECEIVED 08 April 2025 ACCEPTED 03 September 2025 PUBLISHED 17 September 2025 CITATION Liang Y, Mu C, Kong W, Wang K, Hua S, Wang Y, Liu X, Gong H, Lin Y, Li C, Lin X, Bi Y and Wang B (2025) Tau protein mediates the association between frailty and postoperative delirium: a machine learning model incorporating cerebrospinal fluid biomarkers. Front. Neurol. 16:1608264 . doi: 10.3389/fneur.2025.1608264 COPYRIGHT © 2025 Liang, Mu, Kong, Wang, Hua, Wang, Liu, Gong, Lin, Li, Lin, Bi and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Tau protein mediates the association between frailty and postoperative delirium: a machine learning model incorporating cerebrospinal fluid biomarkers Yizhi Liang 1, Chuanlin Mu 1, Wenjie Kong 2, Kun Wang3, Shuhui Hua 2, Yuanlong Wang 2, Xia Liu3, Hongyan Gong 1, Yanan Lin 1, Chuan Li 1, Xu Lin 1, Yanlin Bi 1 and Bin Wang 1* 1 Department of Anesthesiology, Qingdao Municipal Hospital, Qingdao, China, 2The Second School of Clinical Medicine, Binzhou Medical University, Yantai, China, 3 Department of Anesthesiology, Shandong Second Medical University, Weifang, China Objective: 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 preope

FAQs

What was the main objective of this study?

To investigate the relationship between postoperative delirium and preoperative frailty, and to determine whether frailty affects POD incidence through cerebrospinal fluid biomarkers, using machine learning to identify key predictors.

How were frailty and POD assessed in the participants?

Frailty was evaluated using the Frailty Scale and Frailty Phenotype criteria. Cognitive status was measured with the MMSE, POD was diagnosed using the CAM, and severity was quantified using the MDAS.

Which machine learning model performed best and what did it predict?

Gradient Boosting Machine (GBM) performed best, achieving an AUC of 0.973 in the test set. The GBM-based model supported early identification of patients at high risk of POD after hip or knee replacement.

Aug 4, 2026

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