Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Orthop. Dec 18, 2024; 15(12): 1164-1174
Published online Dec 18, 2024. doi: 10.5312/wjo.v15.i12.1164
Construction and validation of a risk prediction model for depressive symptoms in a middle-aged and elderly arthritis population
Jun-Wei Shi, Wei Kang, Xin-Hao Wang, Jin-Long Zheng, Wei Xu
Jun-Wei Shi, Wei Kang, Wuxi School of Medicine, Jiangnan University, Wuxi 214122, Jiangsu Province, China
Xin-Hao Wang, Department of Rheumatology and Immunology, Minda Hospital of Hubei Minzu University, Enshi 445000, Hubei Province, China
Jin-Long Zheng, Department of Nursing, Xiangyang Centre Hospital, Xiangyang 441100, Hubei Province, China
Wei Xu, Department of Orthopedics, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200336, China
Author contributions: Shi JW contributed to the study design and paper written; Shi JW, Kang W, Wang XH, and Zheng JL contributed to the data collection, data analysis, and statistical analyses; Xu W contributed to the critical revision of the article.
Supported by the Changning District Health Committee Excellent Innovation Talent Training Project, No. RCJD2022S01.
Institutional review board statement: The China Health and Retirement Longitudinal Study research project obtained approval from the Biomedical Ethics Committee of Peking University (IRB00001052-11015).
Informed consent statement: All volunteers who participated in the China Health and Retirement Longitudinal Study signed an informed consent form.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The public can access this data at https://charls.pku.edu.cn/.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Wei Xu, Associate Professor, PhD, Department of Orthopedics, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai 200336, China. weixu@shsmu.edu.cn
Received: August 7, 2024
Revised: September 20, 2024
Accepted: November 14, 2024
Published online: December 18, 2024
Processing time: 132 Days and 8.2 Hours
Abstract
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 middle-aged 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 prediction model developed in this study has strong predictive performance and is useful for screening and assessing depression symptoms in middle-aged and elderly arthritis patients.

Keywords: Middle-aged and elderly individuals; Arthritis; Depression symptoms; Current status; Influencing factors; Risk prediction models

Core Tip: This study focuses on developing and validating a risk prediction model specifically for depressive symptoms in middle-aged and elderly individuals with arthritis utilizing data from the China Health and Retirement Longitudinal Study national survey. By addressing the unique challenges of this population, the model aims to improve early detection and management of depression, ultimately enhancing patient outcomes and guiding more effective interventions.