Development of a Machine Learning Model to Predict Early Ambulation after Proximal Femoral Fracture Surgery
- VernacularTitle:大腿骨近位部骨折患者の術後早期歩行獲得を予測する機械学習モデルの開発
- Author:
Shu AKIBA
1
;
Tetsuya KATAKURA
1
;
Chinatsu KUTSUMA
1
;
Mei AMANO
1
;
Junko MIZUTANI
1
Author Information
- Keywords: proximal femoral fracture; prediction; machine learning; ambulation; postoperative
- From:The Japanese Journal of Rehabilitation Medicine 2026;():25010-
- CountryJapan
- Language:Japanese
- Abstract: Objective: This study aimed to develop a predictive model for early ambulation using clinical indicators obtained immediately after surgery in patients with proximal femoral fractures.Methods: Patients who sustained a proximal femoral fracture and underwent surgery between April 2022 and April 2024, and whose medical records confirmed independent ambulation of at least 10 m before injury, were included. Those who died or had postoperative weight-bearing restrictions were excluded. The outcome variable was the ability to walk 10 meters without assistance at two weeks postoperatively. Predictive features included body mass index (BMI), abbreviated mental test score (AMTS), American Society of Anesthesiologists physical status, use of walking aids pre injury, intraoperative blood loss, and surgical method. A gradient boosting decision tree was used to develop the model.Results: A total of 122 patients were included. Key predictors of ambulation at two weeks were AMTS, BMI, and the use of an intramedullary nail. The model achieved a recall of 72.7%, a precision of 66.6%, and an ROC AUC of 0.80 in an independent test dataset.Conclusion: This study demonstrated the feasibility of a machine learning model to predict early ambulation using immediate postoperative indicators. As walking ability at two weeks is associated with long-term gait recovery and discharge outcomes, this model may aid in optimizing rehabilitation planning and discharge strategies.
