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Chinese Journal of Injury Repair and Wound Healing(Electronic Edition) ›› 2026, Vol. 21 ›› Issue (03): 167-176. doi: 10.3877/cma.j.issn.1673-9450.2026.03.002

• Original Article • Previous Articles    

Association between depression and osteoporotic fracture healing disorder and development and validation of a prediction model based on the NHANES database

Shuai Lu1, Jianming Chen2, Minjuan Li1, Yining Shan3, Renwei Cao1, Yejun Zha1, Xieyuan Jiang1,()   

  1. 1 Department of Orthopedic Trauma, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
    2 Department of Orthopedics, Lingcheng People's Hospital, Dezhou 253500, China
    3 Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
  • Received:2026-03-19 Online:2026-06-01 Published:2026-06-01
  • Contact: Xieyuan Jiang

Abstract:

Objective

To investigate the association between depression and osteoporotic fracture healing disorder and to construct and internally validate a multivariable prediction model.

Methods

Data were obtained from the National Health and Nutrition Examination Survey (NHANES) database from 2005 to 2018. Participants aged ≥40 years with complete data on fracture healing status, depression assessment, and covariates were included. Multivariable Logistic regression was used to analyze the association between depression and osteoporotic fracture healing disorder. The most predictive variables were selected to build a prediction model. Model performance was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

A total of 3 166 participants were included, of whom 958 (30.27%) had osteoporotic fracture healing disorder. Multivariable analysis showed that depressive status was significantly associated with an increased risk of fracture healing disorder (OR=6.92, 95%CI: 6.00~8.00, P<0.001). Subgroup analysis indicated this association was significant across all subgroups (all P<0.001), with significant interactions found for age, gender, and alcohol consumption subgroups (interaction P<0.05). The prediction model, built using variables selected by least absolute shrinkage and selection operator (LASSO) regression (age, gender, depression status, alcohol consumption status), demonstrated good predictive performance: the area under the curve (AUC) of training set was 0.82 (95%CI: 0.80~0.84) and validation set AUC was 0.81 (95%CI: 0.78~0.83). The calibration curve showed high consistency between predicted probabilities and actual risks. DCA indicated clinical net benefit across a wide range of threshold probabilities.

Conclusion

Depression is an independent risk factor for osteoporotic fracture healing disorder. A predictive model based on depressive status, age, sex, and alcohol consumption shows good predictive performance and may help identify high-risk individuals and guide clinical decision-making.

Key words: Depression, Osteoporosis, Fracture, Healing disorder, Prediction model

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