Aims

To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials.

Femoroacetabular Impingement (FAI) syndrome, characterised by abnormal hip contact causing symptoms and osteoarthritis, is measured using the International Hip Outcome Tool (iHOT). This study uses machine learning to predict patient outcomes post-treatment for FAI, focusing on achieving a minimally clinically important difference (MCID) at 52 weeks. A retrospective analysis of 6133 patients from the NAHR who underwent hip arthroscopic treatment for FAI between November 2013 and March 2022 was conducted. MCID was defined as half a standard deviation (13.61) from the mean change in iHOT score at 12 months. SKLearn Maximum Absolute Scaler and Logistic Regression were applied to predict achieving MCID, using baseline and 6-month follow-up data. The model's performance was evaluated by accuracy, area under the curve, and recall, using pre-operative and up to 6-month postoperative variables. A total of 23.1% (1422) of patients completed both baseline and 1-year follow-up iHOT surveys. The best results were obtained using both pre and postoperative variables. The machine learning model achieved 88.1% balanced accuracy, 89.6% recall, and 92.3% AUC. Sensitivity was 83.7% and specificity 93.5%. Key variables determining outcomes included MCID achievement at 6 months, baseline iHOT score, 6-month iHOT scores for pain, and difficulty in walking or using stairs. The study confirmed the utility of machine learning in predicting long-term outcomes following arthroscopic treatment for FAI. MCID, based on the iHOT 12 tools, indicates meaningful clinical changes. Machine learning demonstrated high accuracy and recall in distinguishing between patients achieving MCID and those who did not. This approach could help early identification of patients at risk of not meeting the MCID threshold one year after treatment

MCID and PASS are thresholds driven from PROMS to reflect clinical effectiveness. Statistical significance can be derived from a change in PROMS, whereas MCID and PASS reflect clinical significance. Its role has been increasingly used in the world of young adult hip surgery with several publications determining the thresholds for Femoro-acetabular impingement FAI. To our knowledge MCID and PASS for patient undergoing PAO for dysplasia has not been reported. 593 PAOs between 1/2013 and 7/2023 were extracted from the Northumbria Hip Preservation Registry. Patients with available PROMS at 1year and/or 2years were included. PAOs for retroversion, residual Perthes and those combined with FO were excluded. MCID was calculated using the distribution method 0.5SD of baseline score(1). PASS was calculated using anchor method, ROC analysis performed, and value picked maximizing Youden index. A Logistic Regression analysis was performed to determine which independent variables correlated with achieving MCID and PASS. The MCID threshold for iHOt12 was 8.6 with 83.4 and 86.3 % of patients achieved it at 1 and 2 years respectively. The PASS score at 1 and 2 year follow up was 43 and 44 respectively, with 72.6 and 75.2% achieving it at 1 and 2 year postop. At 2 years a Higher preop iHOT 12 was associated with not achieving MCID and PASS (p<0.05). Preop acetabular version was negatively correlated with achieving MCID and previous hip arthroscopy was negatively correlated with PASS. The % of patients achieving MCID and PASS mimics that of FAI surgery (2). The negative correlation with preop iHOT12 reaffirms the importance of patient selection. The negative correlation of hip arthroscopy highlights the importance of having a high index of suspicion for dysplasia prior to hip arthroscopy and poorer outcomes of patients with mixed CAM and dysplasia pathology