社区老年人肌少⁃骨质疏松症风险预测模型的构建

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Construction of a risk prediction model of osteosarcopenia in the elderly in communities

LIU Keru1， HE Yaoyu1， WANG Yuhuan1， TAO Jing2， WANG Ruoxian1， WEI Shanshan1， HE Bin2*

1.Shihezi University School of Medicine， Xinjiang 832002 China；2.The Third Affiliated Hospital of Shihezi University School of Medicine

*Corresponding Author  HE Bin， E⁃mail： 13999736881@163.com

Abstract  Objective：To investigate the influencing factors of high risk population of osteosarcopenia in communities，and to establish a risk prediction model.Methods：From March to July 2023，a total of 1 051 elderly people from 20 communities in Shihezi city were selected by stratified random sampling method and divided into high risk groups of osteosarcopenia and high risk groups of non⁃osteosarcopenia.Univariate analysis and LASSO regression were used to preliminarily screen variables，and then the predictive variables were determined by Logistic regression analysis.The Nomogram prediction model was constructed and verified by R4.3.1 software.Results：The incidence of osteosarcopenia in 1 051 elderly patients was 21.9%.Age，sex，body mass index，comorbidities，history of fracture，type of health insurance，consumption of strong tea，coffee or carbonated beverages，daily sedentary time，activity of the elderly，nutritional status，social frailty and depression were the influencing factors for community groups at high risk of osteosarcopenia.The risk prediction model had a good fit，and the area under the receiver operating characteristic curve was 0.956，indicating a good differentiation.The average absolute error between the actual value and the predicted value of the calibration curve was 0.014，which has good accuracy.The results of decision curve showed good clinical effectiveness.Conclusions：The risk prediction model of osteosarcopenia in elderly people in communities is scientific and practical，and it can be used for the screening of community osteosarcopenia high⁃risk population.

Keywords  communities； the elderly； osteosarcopenia； Nomogram； predictive model

摘要  目的：探讨社区肌少⁃骨质疏松症高危人群的影响因素，并构建风险预测模型。（剩余13743字）