Qian Zhengyu, Wu Xiaochu, He Kunyang, Lin Kaijie, Luo Xiaobei, Zhang Tianyao
School of Clinical Medicine, Chengdu Medical College, Chengdu, China.
The First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Front Med (Lausanne). 2025 Aug 18;12:1604333. doi: 10.3389/fmed.2025.1604333. eCollection 2025.
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.
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.
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.
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.
老年外科手术患者具有多样化的临床特征，但研究表明，与肌肉减少症相关的特征与围手术期神经认知障碍（PND）的发生率之间存在显著相关性。机器学习技术的整合为识别有较高PND风险的老年外科手术患者提供了一条有前景的途径，特别是那些表现出与肌肉减少症相关特征的患者。这种方法提高了术前风险分层和患者选择，从而提高了临床管理和治疗决策的精准度。
从成都医学院第一附属医院接受非心脏手术的患者中收集数据，以开发和验证一个预测模型。构建了五个机器学习模型——支持向量机（SVM）、极端梯度提升（XGBoost）、梯度提升机（GBM）、自适应提升（AdaBoost）和随机森林——来评估老年外科手术患者发生PND的风险。与肌肉减少症相关的特征被纳入这些模型作为关键变量。随后利用SHapley加性解释（SHAP）方法来解释最有效的模型。
该研究共纳入443例患者。在这五个模型中，AdaBoost表现最佳，曲线下面积（AUC）达到0.95。SHAP识别出的六个最重要特征为6米步行速度、术前简易精神状态检查表（MMSE）评分、最大握力、四肢骨骼肌质量和肌肉减少症评估年龄。这些结果表明AdaBoost具有出色的预测性能，具有高可解释性和可靠性。
机器学习模型，特别是与SHAP整合的AdaBoost，在预测老年外科手术患者的PND方面显示出巨大潜力。该模型阐明与肌肉减少症相关特征影响的能力增强了其在术前风险评估中的临床实用性。
