摘要
Abstract
Objective To construct a prediction model of sarcopenia in middle-aged and elderly peritoneal dialysis(PD)patients based on machine learning algorithm,and to provide a reference for the diagnosis and treatment of sarcopenia.Methods A total of 648 middle-aged and elderly PD patients who were hospitalized between May 2020 and October 2023 were selected as the study subjects.Clinical data of the patients were collected,and they were divided into a sarcopenia group of 169 cases and a non sarcopenia group of 479 cases based on the occurrence of sarcopenia.Based on machine learning algorithms,extreme gradient boosting(XGBoost)and logistic regression,random forest(RF),and support vector machine(SVM)methods were used to construct risk models for sarcopenia in different PD patients,and the models were evaluated and compared.Results A total of 26 indicators were collected,and 9 risk factors of sarcopenia were screened out by single factor and Logistic regression.After verification on the test set,the area under the curve(AUC)constructed by XGBoost,Logistic,RF and SVM models to predict the occurrence of sarcopenia in middle-aged and elderly PD patients was 0.807,0.788,0.804 and 0.791 respectively,and the accuracy was 0.829,0.813,0.855 and 0.819,respectively.F1 scores were 0.733,0.659,0.728 and 0.653.Conclusions The prediction model based on machine learning algorithm XGBoost is superior to RF,Logistic and SVM models in sensitivity and accuracy.It is helpful to guide clinical staff to identify PD patients at high risk of developing sarcopenia and to facilitate intervention strategies as early as possible.关键词
腹膜透析/肌少症/预测模型/机器学习算法Key words
Peritoneal dialysis/Sarcopenia/Prediction model/Machine learning algorithm
