Depression and anxiety · 2026
A Prediction Model for 9-Year Depression Risk Among Patients With COPD in China.
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Wang Cui, Wang Mengya, Bi Yanying, Zhang Nan
Аннотация
OBJECTIVES: To establish a prediction model for the 9-year risk of depression in individuals with chronic obstructive pulmonary disease (COPD) using survey data from a random, nationally representative population of Chinese adults with COPD.
METHODS: Data from 851 middle-aged and older adults with COPD participating in the China Health and Retirement Longitudinal Study (CHARLS) were analyzed. Depression risk was described as the development of depression during follow-up among participants who were free of depression risk at baseline (CHARLS 2011) and met the depression risk criteria at the 9-year follow-up (CHARLS 2020). Relationships between candidate predictive factors and depression risk were examined using logistic regression, with bootstrapping applied to validate the final model. Model discrimination was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, while calibration was examined via the Hosmer-Lemeshow test.
RESULTS: Of the 851 participants included in the analysis, 434 developed depression risk during the subsequent 9 years. The final multivariable model incorporated age, educational level, marital status, exercise levels, peak expiratory flow (PEF), and smoking. The discriminative ability of the model was good, with an AUC of 0.717 (95% CI: 0.683-0.751). The Hosmer-Lemeshow test indicated satisfactory calibration (χ 2 = 9.400, p = 0.310). A simple clinical scoring system was also developed to facilitate quantitative estimation of depression risk.
CONCLUSION: This predictive model may facilitate earlier identification of COPD patients likely to develop depression within 9 years and may help inform targeted preventive interventions.
METHODS: Data from 851 middle-aged and older adults with COPD participating in the China Health and Retirement Longitudinal Study (CHARLS) were analyzed. Depression risk was described as the development of depression during follow-up among participants who were free of depression risk at baseline (CHARLS 2011) and met the depression risk criteria at the 9-year follow-up (CHARLS 2020). Relationships between candidate predictive factors and depression risk were examined using logistic regression, with bootstrapping applied to validate the final model. Model discrimination was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, while calibration was examined via the Hosmer-Lemeshow test.
RESULTS: Of the 851 participants included in the analysis, 434 developed depression risk during the subsequent 9 years. The final multivariable model incorporated age, educational level, marital status, exercise levels, peak expiratory flow (PEF), and smoking. The discriminative ability of the model was good, with an AUC of 0.717 (95% CI: 0.683-0.751). The Hosmer-Lemeshow test indicated satisfactory calibration (χ 2 = 9.400, p = 0.310). A simple clinical scoring system was also developed to facilitate quantitative estimation of depression risk.
CONCLUSION: This predictive model may facilitate earlier identification of COPD patients likely to develop depression within 9 years and may help inform targeted preventive interventions.