Anterior approach total hip arthroplasty (AA-THA) has a steep learning curve, with higher complication rates in initial cases. Proper surgical case selection during the learning curve can reduce early risk. This study aims to identify patient and radiographic factors associated with AA-THA difficulty using Machine Learning (ML).

Consecutive primary AA-THA patients from two centres, operated by two expert surgeons, were enrolled (excluding patients with prior hip surgery and first 100 cases per surgeon). K- means prototype clustering – an unsupervised ML algorithm – was used with two variables - operative duration and surgical complications within 6 weeks - to cluster operations into difficult or standard groups.

Radiographic measurements (neck shaft angle, offset, LCEA, inter-teardrop distance, Tonnis grade) were measured by two independent observers. These factors, alongside patient factors (BMI, age, sex, laterality) were employed in a multivariate logistic regression analysis and used for k-means clustering. Significant continuous variables were investigated for predictive accuracy using Receiver Operator Characteristics (ROC).

Out of 328 THAs analyzed, 130 (40%) were classified as difficult and 198 (60%) as standard. Difficult group had a mean operative time of 106mins (range 99–116) with 2 complications, while standard group had a mean operative time of 77mins (range 69–86) with 0 complications. Decreasing inter-teardrop distance (odds ratio [OR] 0.97, 95% confidence interval [CI] 0.95–0.99, p = 0.03) and right-sided operations (OR 1.73, 95% CI 1.10–2.72, p = 0.02) were associated with operative difficulty. However, ROC analysis showed poor predictive accuracy for these factors alone, with area under the curve of 0.56. Inter-observer reliability was reported as excellent (ICC >0.7).

Right-sided hips (for right-hand dominant surgeons) and decreasing inter-teardrop distance were associated with case difficulty in AA-THA. These data could guide case selection during the learning phase. A larger dataset with more complications may reveal further factors.

Total knee and hip arthroplasty (TKA and THA) are the most commonly performed surgical procedures, the costs of which constitute a significant healthcare burden. Improving access to care for THA/TKA requires better efficiency. It is hypothesized that this may be possible through a two-stage approach that utilizes prediction of surgical time to enable optimization of operating room (OR) schedules.

Data from 499,432 elective unilateral arthroplasty procedures, including 302,490 TKAs, and 196,942 THAs, performed from 2014-2019 was extracted from the American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database. A deep multilayer perceptron model was trained to predict duration of surgery (DOS) based on pre-operative clinical and biochemical patient factors. A two-stage approach, utilizing predicted DOS from a held out “test” dataset, was utilized to inform the daily OR schedule. The objective function of the optimization was the total OR utilization, with a penalty for overtime. The scheduling problem and constraints were simulated based on a high-volume elective arthroplasty centre in Canada. This approach was compared to current patient scheduling based on mean procedure DOS. Approaches were compared by performing 1000 simulated OR schedules.

The predict then optimize approach achieved an 18% increase in OR utilization over the mean regressor. The two-stage approach reduced overtime by 25-minutes per OR day, however it created a 7-minute increase in underutilization. Better objective value was seen in 85.1% of the simulations.

With deep learning prediction and mathematical optimization of patient scheduling it is possible to improve overall OR utilization compared to typical scheduling practices. Maximizing utilization of existing healthcare resources can, in limited resource environments, improve patient's access to arthritis care by increasing patient throughput, reducing surgical wait times and in the immediate future, help clear the backlog associated with the COVID-19 pandemic.

Knee pain is common, representing a significant socioeconomic burden. Caused by a variety of pathologies, its evaluation in primary-care is challenging. Subsequently, an over-reliance on magnetic resonance imaging (MRI) exists. Prior to orthopaedic surgeon referral, many patients receive no, or incorrect, imaging. Electronic-triage (e-triage) tools represent an innovative solution to address this problem. The primary aim of this study was to ascertain whether an e-triage tool is capable of outperforming existing clinical pathways to determine the correct pre-hospital imaging based on knee pain diagnosis.

Patients ≥18 years with a new presentation of knee pain were retrospectively identified. The timing and appropriateness of imaging was assessed. A symptom-based e-triage tool was developed, using the Amazon LEXbotplatform, and piloted to predict five common knee pathologies and suggest appropriate imaging.

1462 patients were identified. 17% of arthroplasty patients received an ‘unnecessary MRI’, whilst 28% of arthroscopy patients did not have a ‘necessary MRI’, thus requiring a follow-up appointment, with a mean delay of three months (SD 2.6, range 0.2-20.2). Using NHS tariffs, a wasted cost through unnecessary/necessary MRIs and subsequent follow-up appointments was estimated at £45,816. The e-triage pilot was trialled with 41 patients (mean age:58.4 years, 58.5% female). Preliminary diagnoses were available for 34 patients. Using the highest proportion of reported symptoms in the corresponding group, the e-triage tool correctly identified three of the four knee pathologies. The e-triage tool did not correctly identify anterior cruciate ligament injuries (n=3). 79.2% of participants would use the tool again.

A significant number of knee pathology patients received incorrect imaging prior to their initial hospital appointment, incurring delays and unnecessary costs. A symptom-based e-triage tool was developed, with promising pilot data and user feedback. With refinement, this tool has the potential to improve wait-times and referral quality, whilst reducing costs.