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

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 at the 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.