Predicting early return to sport after periacetabular osteotomy : a machine-learning approach.

Aims: Periacetabular osteotomy (PAO) is the primary surgical treatment for developmental dysplasia of the hip (DDH), providing considerable pain relief and improved joint function. Return to sport (RTS) is a key outcome for young, active patients. This study aimed to identify preoperative predictors of RTS timing and develop a machine-learning-based prediction model to optimize patient counselling.

Methods: This retrospective analysis of prospectively collected data included 235 patients who underwent PAO between January 2019 and December 2023. Preoperative variables, including demographic, functional, and psychological assessments, were analyzed. RTS was assessed at three, six, and 12 months postoperatively. Logistic regression with recursive feature elimination and a conditional inference tree (ctree) model were used to identify predictors of RTS.

Results: At three months, 102 patients (43%) had returned to sports, increasing to 182 (77%) at six months and 223 (95%) at 12 months. Key predictors of early RTS included the minimally invasive surgical approach, higher preoperative physical activity (≥ two sessions/week), lower anxiety scores, and higher Hip disability and Osteoarthritis Outcome Score (HOOS) pain scores. Male sex and older age were associated with delayed RTS. The ctree model stratified patients based on their likelihood of early RTS, providing an individualized prognosis.

Conclusion: PAO enables early RTS in over 90% of patients within the first year. The use of a minimally invasive approach allowing immediate active hip flexion, higher preoperative activity levels, and lower anxiety scores significantly improves RTS timing. The machine-learning model provides precise, individualized RTS predictions, offering a valuable tool for patient counselling and rehabilitation planning.