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Predicting early return to sport after periacetabular osteotomy : a machine-learning approach
Lars Nonnenmacher1, Maximilian Fischer1, Lars Kaderali2
1Center for Orthopaedics, Trauma Surgery and Rehabilitation Medicine, University Medicine Greifswald, Greifswald, Germany.
Bone & Joint Open
|May 31, 2025
Summary
Periacetabular osteotomy (PAO) allows most patients to return to sports within a year. Minimally invasive surgery, higher activity, and lower anxiety predict earlier return to sport (RTS).
Area of Science:
- Orthopedic Surgery
- Sports Medicine
- Biomedical Engineering
Background:
- Developmental dysplasia of the hip (DDH) is commonly treated with periacetabular osteotomy (PAO).
- Return to sport (RTS) is a critical outcome for young, active patients post-PAO.
- Optimizing RTS prediction aids patient counseling and rehabilitation.
Purpose of the Study:
- Identify preoperative predictors of RTS timing after PAO.
- Develop a machine-learning model for individualized RTS prediction.
- Enhance patient counseling and rehabilitation strategies.
Main Methods:
- Retrospective analysis of 235 patients undergoing PAO (2019-2023).
- Analysis of preoperative demographic, functional, and psychological variables.
- Logistic regression and conditional inference tree (ctree) models used for prediction.
Main Results:
- 95% of patients achieved RTS by 12 months post-PAO.
- Early RTS predictors: minimally invasive surgery, high preoperative activity, low anxiety, high HOOS pain scores.
- Delayed RTS associated with male sex and older age.
Conclusions:
- PAO facilitates successful RTS in over 90% of patients within one year.
- Minimally invasive techniques, preoperative activity, and psychological factors influence RTS.
- Machine learning models offer precise, individualized RTS predictions for improved patient care.
