Application of machine learning in predicting perioperative neurocognitive disorders in elderly patients - the impact of sarcopenia-related features

Aug 5, 2026

Machine learning models were developed to predict perioperative neurocognitive disorders (PND) in elderly patients undergoing non-cardiac surgery, motivated by the clinical need for earlier risk stratification. Sarcopenia-related features were incorporated as key predictors, and five algorithms—SVM, XGBoost, GBM, AdaBoost, and Random Forest—were trained and validated using clinical data from a single hospital cohort. SHAP interpretation was applied to identify the most influential variables. AdaBoost achieved the highest performance (AUC 0.95), suggesting strong predictive power and interpretable sarcopenia-associated risk signals for preoperative decision-making.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Methods

Results

Conclusion

TYPE Original Research PUBLISHED 18 August 2025 DOI 10. 3389/fmed.2025.1604333 OPEN ACCESS EDITED BY Alessandro Gialluisi, LUM University “Giuseppe Degennaro”, Italy REVIEWED BY Vahid Rashedi, University of Social Welfare and Rehabilitation Sciences, Iran Antonietta Pepe, LUM University “Giuseppe Degennaro”, Italy *CORRESPONDENCE Tianyao Zhang [tianyaozhang123@163.com](tianyaozhang123@163.com) †These authors have contributed equally to this work RECEIVED 01 April 2025 ACCEPTED 29 July 2025 PUBLISHED 18 August 2025 CORRECTED 03 September 2025 CITATION Qian Z, Wu X, He K, Lin K, Luo X and Zhang T (2025) Application of machine learning in predicting perioperative neurocognitive disorders in elderly patients: the impact of sarcopenia-related features. Front. Med. 12:1604333 . doi: 10. 3389/fmed.2025.1604333 COPYRIGHT © 2025 Qian, Wu, He, Lin, Luo and Zhang. 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. Application of machine learning in predicting perioperative neurocognitive disorders in elderly patients: the impact of sarcopenia-related features Zhengyu Qian1,2†, Xiaochu Wu1,2,3†, Kunyang He1 , Kaijie Lin1 , Xiaobei Luo4 and Tianyao Zhang1,2* 1 School of Clinical Medicine, Chengdu Medical College, Chengdu, China, 2The First Affiliated Hospital of Chengdu Medical College, Chengdu, China, 3 National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China, 4 Suzhou Medical College, Soochow University, Suzhou, Jiangsu, China Background: Older surgical patients present with diverse clinical proﬁles, yet research indicates a signiﬁcant correlation between sarcopenia-related features and the incidence of perioperative neurocognitive disorder (PND) . The integration of machine learning techniques offers a promising avenue for identifying older surgical patients at elevated risk of PND, particularly those exhibiting sarcopenia-associated characteristics. This approach enhances preoperative risk stratiﬁcation and patient selection, thereby improving the precision of clinical management and treatment decisions. Methods: Data were collected from patients undergoing non-cardiac surgery atthe First Affiliated Hospital of Chengdu Medical College to develop and validate a predictive model. Five machine learning models—Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), and Random Forest—were constructed to evaluate the risk of PND in older surgical patients. Sarcopenia-related features were incorporated as key variables in these models. The SHapley Additive exPlanations (SHAP) method was subsequently utilized to interpret the most effective model. Results: A total of 443 patients were included in the study. Among the ﬁve models, AdaBoost performed best, achieving an AUC of 0.95. The six most important features identiﬁed by SHAP were 6-meter walking speed, preoperative MMSE score, maximum grip strength, appendicular skeletal muscle mass, and sarcopenia assessment age. These results demonstrate AdaBoost’s excellent predictive performance, with high interpretability and reliability. Conclusion: Machine learning models, particularly AdaBoost integrated with SHAP, show signiﬁcant potential in predicting PND in older surgical patients. The model’s ability to clarify the impact of sarcopenia-related features enhances its clinical utility in preoperative risk assessment. KEYWORDS machine learning, sarcopenia, postoperative cognitive dysfunction, SHapley Additive ex

FAQs

What is the study’s goal regarding perioperative neurocognitive disorders in elderly patients?

To build and validate machine learning predictive models for PND risk in older surgical patients, emphasizing the contribution of sarcopenia-related features to preoperative risk assessment.

Which machine learning models were compared, and which performed best?

Five models were constructed: SVM, XGBoost, GBM, AdaBoost, and Random Forest. AdaBoost performed best with an AUC of 0.95.

How were the key predictors identified in the best-performing model?

The study used SHAP (SHapley Additive exPlanations) to interpret the most effective model and determine the most important features.

Aug 5, 2026

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