Construction and validation of a risk prediction model for depressive symptoms in a middle-aged and elderly arthritis population  

作　　者：Jun-Wei Shi Wei Kang Xin-Hao Wang Jin-Long Zheng Wei Xu 

机构地区：[1]Wuxi School of Medicine,Jiangnan University,Wuxi 214122,Jiangsu Province,China [2]Department of Rheumatology and Immunology,Minda Hospital of Hubei Minzu University,Enshi 445000,Hubei Province,China [3]Department of Nursing,Xiangyang Centre Hospital,Xiangyang 441100,Hubei Province,China [4]Department of Orthopedics,Tongren Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai 200336,China

出　　处：《World Journal of Orthopedics》2024年第12期1164-1174,共11页世界骨科杂志（英文）

基　　金：Supported by the Changning District Health Committee Excellent Innovation Talent Training Project,No.RCJD2022S01.

摘　　要：BACKGROUND Arthritis is a prevalent and debilitating condition that affects a significant proportion of middle-aged and older adults worldwide.Characterized by chronic pain,inflammation,and joint dysfunction,arthritis can severely impact physical function,quality of life,and mental health.The overall burden of arthritis is further compounded in this population due to its frequent association with depression.As the global population both the prevalence and severity of arthritis are anticipated to increase.AIM To investigate depressive symptoms in the middle-aged and elderly arthritic population in China,a risk prediction model was constructed,and its effectiveness was validated.METHODS Using the China Health and Retirement Longitudinal Study 2018 data on middleaged and elderly arthritic individuals,the population was randomly divided into a training set(n=4349)and a validation set(n=1862)at a 7:3 ratio.Based on 10-fold cross-validation,least absolute shrinkage and selection regression was used to screen the model for the best predictor variables.Logistic regression was used to construct the nomogram model.Subject receiver operating characteristic and calibration curves were used to determine model differentiation and accuracy.Decision curve analysis was used to assess the net clinical benefit.RESULTS The prevalence of depressive symptoms in the middle-aged and elderly arthritis population in China was 47.1%,multifactorial logistic regression analyses revealed that gender,age,number of chronic diseases,number of pain sites,nighttime sleep time,education,audiological status,health status,and place of residence were all predictors of depressive symptoms.The area under the curve values for the training and validation sets were 0.740(95%confidence interval:0.726-0.755)and 0.731(95%confidence interval:0.709-0.754),respectively,indicating good model differentiation.The calibration curves demonstrated good prediction accuracy,and the decision curve analysis curves demonstrated good clinical utility.CONCLUSION The risk pre

关 键 词：Middle-aged and elderly individuals ARTHRITIS Depression symptoms Current status Influencing factors Risk prediction models 

分 类 号：R683.42[医药卫生—骨科学]