OpenPredictor, a machine learning-enabled clinical decision aid, has been developed to manage backlogs in elective surgeries. It aims to optimise the use of high volume, low complexity surgical pathways by accurately stratifying patient risk, thereby facilitating the allocation of patients to the most suitable surgical sites. The tool augments elective surgical pathways by providing automated secondary opinions for perioperative risk assessments, enhancing decision-making. Its primary application is in elective sites utilising lighter pre-assessment methods, identifying patients with minimal complication risks and those high-risk individuals who may benefit from early pre-assessment.

The Phase 1 clinical evaluation of OpenPredictor entailed a prospective analysis of 156 patient records from elective hip and knee joint replacement surgeries. Using a polynomial logistic regression model, patients were categorised into high, moderate, and low-risk groups. This categorisation incorporated data from various sources, including patient demographics, co-morbidities, blood tests, and overall health status.

In identifying patients at risk of postoperative complications, OpenPredictor demonstrated parity with consultant-led preoperative assessments. It accurately flagged 70% of patients who later experienced complications as moderate or high risk. The tool's efficiency in risk prediction was evidenced by its balanced accuracy (75.6%), sensitivity (70% with a 95% confidence interval of 62.05% to 76.91%), and a high negative predictive value (96.7%).

OpenPredictor presents a scalable and consistent solution for managing elective surgery pathways, comparable in performance to secondary consultant opinions. Its integration into pre-assessment workflows assists in efficient patient categorisation, reduces late surgery cancellations, and optimises resource allocation. The Phase 1 evaluation of OpenPredictor underscores its potential for broader clinical application and highlights the need for ongoing data refinement and system integration to enhance its performance.

This study used a national registry to assess the outcomes of hip arthroscopy (HA) for the treatment femoroacetabular impingement (FAI).

All HAs for FAI recorded in the UK Non-Arthroplasty Hip Registry (NAHR) between January 2012 and September 2023 were identified. Cases were grouped according to the index procedure performed for FAI (cam, pincer, or mixed). Patient outcomes captured included the International Hip Outcome Tool (iHOT)-12.

7,511 HAs were identified; 4,583 cam (61%), 675 pincer (9%), 2,253 mixed (30%). Mean age (34.8) was similar between groups. There was a greater proportion of females in the pincer group (75%) compared to cam (52%) and mixed (50%). A higher proportion of patients had a recorded cartilage injury in association with a cam lesion compared to pincer. The pincer group had poorer mean pre-op iHOT-12 scores (31.6 \[95%CI 29.9 to 33.3\]; n=364) compared to cam (33.7 \[95%CI 32.1 to 34.4\]; n=3,941) and achieved significantly lower scores at 12 months (pincer = 52.6 (50.2 to 55); n=249, cam = 58.3 (57.1 to 59.5); n=1,679).

Overall, significant (p < 0.0001) iHOT-12 and EQ-5D improvement vs baseline pre-operative scores were achieved for all FAI subtypes at 6 months (overall mean iHOT-12 improvement +26.0 \[95%CI 25.0 to 26.9\]; n=2,983) and maintained out to 12 months (+26.2 \[25.1 to 27.2\]; n=2,760) at which point 67% and 48% of patients continued to demonstrate a score improvement greater than or equal to the minimum clinically important difference (>/=13 points) and substantial clinical benefit (>/=28 points) for iHOT-12 respectively.

This study demonstrates excellent early functional outcomes following HA undertaken for FAI in a large national registry.

There is little known about how patient socioeconomic status impacts clinical outcomes in hip preservation surgery. The aim of this study was to evaluate the relationship between indices of multiple deprivation, funding provider (NHS Funded or Private Funded) and clinical outcomes following surgery for femoroacetabular impingement (FAI)

The study analysed the data of 5590 patients recorded in the NAHR who underwent primary hip arthroscopic treatment for FAI between November 2013 and July 2023. Records were matched to the UK National index of multiple deprivation using the lower layer super output area. Using iHOT12 score, patient reported outcome measures were analysed at base line and 1 year following surgery.

2358 records were matched to LLSOA deciles. Between the lowest (most deprived) 3 deciles and the highest (least deprived) the average baseline iHOT12 score was 28.98 (n=366) and 35.33 (n=821). The proportion of patients receiving treatment through NHS funding compared to independent funding for the most deprived, 292 (90%) 37 (10%) respectively compared to the least deprived 515 (70%) and 244 (30%) respectively. At 1year, iHOT12 scores for each group were 51.64 (29.1 SD) compared to 61.5 (28.06 SD) respectively.

The study demonstrates that patients from lower socioeconomic backgrounds had poorer baseline and one-year post-surgery iHOT12 scores compared to those from higher socioeconomic strata. Furthermore, a higher reliance on NHS funding was observed among the most deprived, while more affluent patients predominantly opted for private funding. These findings underscore the significant influence of socioeconomic status on both the quality of healthcare received and recovery outcomes in hip preservation surgery, calling attention to the need for more equitable healthcare solutions.

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.