Construction and validation of a risk prediction model for depressive symptoms in a middle-aged and elderly arthritis
Jun-Wei Shi1, Wei Kang1, Xin-Hao Wang2
1Wuxi School of Medicine, Jiangnan University, Wuxi 214122, Jiangsu Province, China.
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.
