肿瘤患者肌肉减少症预测模型的构建  

Construction of a prediction model for sarcopenia in cancer patients

作　　者：张延隆 王运良[2] 张娟娟 张一曼 赵紫琪 张红杰[1] ZHANG Yanlong;WANG Yunliang;ZHANG Juanjuan;ZHANG Yiman;ZHAO Ziqi;ZHANGHongjie(School of Public Health,Hebei University,Baoding 071000,China;Department of Oncology,Baoding No.1 Central Hospital,Baoding 071000,China;Department of Nutrition,Baoding No.1 Central Hospital,Baoding 071000,China)

机构地区：[1]河北大学公共卫生学院,河北保定071000 [2]保定市第一中心医院肿瘤内科,河北保定071000 [3]保定市第一中心医院营养科,河北保定071000

出　　处：《医学研究与教育》2025年第1期53-62,共10页Medical Research and Education

基　　金：河北省自然科学基金资助项目(H2024201039)。

摘　　要：目的探讨肿瘤患者肌肉减少症的影响因素,构建风险预测模型并验证,为早期识别肿瘤患者中肌肉减少症高危人群提供筛查工具。方法收集保定市某三甲医院肿瘤内科的肿瘤患者,按照7∶3的比例随机分为建模组和验证组。对建模组,采用单因素分析和多因素Logistic回归分析,筛选出肌肉减少症的独立预测因子,构建预测模型。根据受试者特征曲线下面积评估模型的预测效能。结果年龄、小腿围、吸烟史、合并糖尿病、肿瘤分期及PG-SGA评分是肿瘤患者发生肌肉减少症的独立影响因素。可建立预测模型方程logit(P)=6.686+0.244x1-0.806x2+1.036x3+1.186x4+1.828x5+2.635x6(x1为年龄,x2为小腿围,x3为吸烟史,x4为合并糖尿病,x5为肿瘤分期,x6为PG-SGA评分),P>27.53%可被认为是肌肉减少症高危人群。结论构建的预测模型具有拟合度高、区分能力良好及简便无创等优点,可以有效预测肿瘤患者肌肉减少症的发生。Objective To investigate the influential factors of sarcopenia in cancer patients,establish a risk prediction model and verify it,and provide a screening tool for early identification of high-risk groups of sarcopenia in cancer patients.Methods Cancer patients in the department of oncology of a three A grade hospital in Baoding city were randomly divided into modeling group and validation group according to a ratio of 7∶3.For the modeling group,univariate analysis and multivariate Logistic re-gression analysis were used to screen out independent predictors of sarcopenia,and the prediction mod-el was constructed.The predictive performance of the model was evaluated according to the area under the ROC curve(AUC).Results Age,calf circumference,smoking history,diabetes mellitus,tumor stage and PG-SGA score were the independent factors influencing the occurrence of sarcopenia in tumor patients.The prediction model equation logit(P)=6.686+0.244x1-0.806x2+1.036x3+1.186x4+1.828x5+2.635x6(x1:age,x2:calf circumference,x3:smoking history,x4:combined diabetes,x5:tumor stage,x6:PG-SGA score),P>27.53%could be considered as high risk group of sarcope-nia.Conclusion The constructed prediction model has the advantages of high fitting degree,good dif-ferentiation ability,simple and non-invasive,which can effectively predict the occurrence of sarcopenia in tumor patients.

关 键 词：肿瘤 肌肉减少症 影响因素 预测模型 

分 类 号：R1[医药卫生—公共卫生与预防医学] R74