Multi-task deep survival analysis links acute exacerbation frequency to hip fracture risk in advanced COPD.

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is linked to elevated hip fracture risk, but validated prediction tools integrating disease-specific pathophysiology are lacking.

OBJECTIVES

To develop a multitask deep learning model predicting hip fracture risk and acute exacerbation frequency in COPD patients, and identify key predictors of skeletal vulnerability.

METHODS

This retrospective cohort study analyzed 4995 COPD patients (245 incident hip fractures) from the China Health and Retirement Longitudinal Study (CHARLS). A multitask deep survival model combined Cox proportional hazards (fracture prediction) and regression (exacerbation frequency) tasks, integrating demographic, clinical, and biomarker data. Performance was evaluated via concordance index (C-index) and mean squared error (MSE).

RESULTS

The model achieved a C-index of 0.725 for fracture prediction and MSE of 0.522 for exacerbation frequency, outperforming conventional methods. Key predictors included acute exacerbation frequency (fracture group: 2.5 ± 4.4 vs. non-fracture: 1.1 ± 2.2 events/year; adjusted HR = 1.28 per additional event, 95 % CI: 1.19-1.38) and baseline lung function (fracture group: 262.7 ± 96.7 mL vs. non-fracture: 277.4 ± 85.7 mL). Frequent hospitalizations (≥2/year) increased fracture risk by 47 %. Systemic inflammation (elevated CRP/IL-6) and age further contributed to skeletal vulnerability.

CONCLUSION

This study establishes the first multitask deep learning framework for COPD-related fracture risk, demonstrating superior performance through multidimensional feature synthesis. The model enables personalized prevention by highlighting exacerbation burden, lung function decline, and inflammation as critical risk factors.