Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy.Aims
Methods
The primary aim of our study was to assess the influence of age on hip-specific outcome following total hip arthroplasty (THA). Secondary aims were to assess health-related quality of life (HRQoL) and level of activity according to age. A prospective cohort study was conducted. All patients were fitted with an Exeter stem with a 32 mm head on highly cross-linked polyethylene (X3RimFit) cemented acetabulum. Patients were recruited into three age groups: < 65 years, 65 to 74 years, and ≥ 75 years, and assessed preoperatively and at three, 12, 24, and 60 months postoperatively. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Harris Hip Score (HHS), and Hip disability and Osteoarthritis Outcome Score (HOOS), were used to assess hip-specific outcome. EuroQol five-dimension five-level questionnaire (EQ-5D-5L) and 36-Item Short Form Survey (SF-36) scores were used to assess HRQoL. The Lower Extremity Activity Scale (LEAS) and Timed Up and Go (TUG) were used to assess level of activity.Aims
Methods
The aim of this modified Delphi process was to create a structured Revision Hip Complexity Classification (RHCC) which can be used as a tool to help direct multidisciplinary team (MDT) discussions of complex cases in local or regional revision networks. The RHCC was developed with the help of a steering group and an invitation through the British Hip Society (BHS) to members to apply, forming an expert panel of 35. We ran a mixed-method modified Delphi process (three rounds of questionnaires and one virtual meeting). Round 1 consisted of identifying the factors that govern the decision-making and complexities, with weighting given to factors considered most important by experts. Participants were asked to identify classification systems where relevant. Rounds 2 and 3 focused on grouping each factor into H1, H2, or H3, creating a hierarchy of complexity. This was followed by a virtual meeting in an attempt to achieve consensus on the factors which had not achieved consensus in preceding rounds.Aims
Methods
The main aims were to identify risk factors predictive of a radiolucent line (RLL) around the acetabular component with an interface bioactive bone cement (IBBC) technique in the first year after THA, and evaluate whether these risk factors influence the development of RLLs at five and ten years after THA. A retrospective review was undertaken of 980 primary cemented THAs in 876 patients using cemented acetabular components with the IBBC technique. The outcome variable was any RLLs that could be observed around the acetabular component at the first year after THA. Univariate analyses with univariate logistic regression and multivariate analyses with exact logistic regression were performed to identify risk factors for any RLLs based on radiological classification of hip osteoarthritis.Aims
Methods
