Dynamic frailty risk prediction in elderly hip replacement: a deep learning approach to personalized rehabilitation.

Abstract

Background: Osteoarthritis and related degenerative conditions in the elderly often necessitate hip replacement surgery. Frailty is common in this population and significantly increases the risk of postoperative complications and delayed recovery. Accurate prediction of postoperative frailty risk and its temporal progression is essential for guiding personalized rehabilitation strategies.

Methods: This study prospectively included 647 patients aged 60 years or older who underwent hip replacement surgery at the Affiliated Hospital of Shanxi Medical University between June 2021 and December 2023. Clinical, biochemical, demographic, and surgical data were collected at preoperative and postoperative stages. To mitigate sample size limitations, data augmentation was applied, expanding the dataset to approximately 2,500 cases for model training. Seven survival analysis models-Cox-Time, DeepHit, DeepSurv, MP-RSF, MP-AdaBoost, MP-LogitR-were employed to dynamically predict frailty risk over time. Model performance was evaluated using the C-index and Brier score. Model interpretability was assessed using SHAP analysis.

Results: DeepSurv demonstrated the highest predictive performance (C-index = 0.95, Brier score = 0.03), while MP-RSF performed less optimally (C-index = 0.77). The predicted frailty risk peaked around postoperative day 30 and declined by day 90. SHAP analysis identified low-density lipoprotein cholesterol (LDL-C), age, body mass index (BMI), and surgical indication as key contributors to frailty prediction across models.

Conclusion: The findings of this study suggest that the DeepSurv model may more accurately predict the postoperative frailty trajectory than other models. Identifying high-risk periods and key clinical predictors enables clinicians to implement timely, individualized interventions that may reduce frailty risk and improve functional recovery.