Aims. Failure of irrigation and debridement (I&D) for prosthetic joint infection (PJI) is influenced by numerous host, surgical, and pathogen-related factors. We aimed to develop and validate a practical, easy-to-use tool based on machine learning that may accurately predict outcome following I&D surgery taking into account the influence of numerous factors. Methods. This was an international, multicentre retrospective study of 1,174 revision total hip (THA) and knee arthroplasties (TKA) undergoing I&D for PJI between January 2005 and December 2017. PJI was defined using the Musculoskeletal Infection Society (MSIS) criteria. A total of 52 variables including demographics, comorbidities, and clinical and laboratory findings were evaluated using random forest machine learning analysis. The algorithm was then verified through cross-validation. Results. Of the 1,174 patients that were included in the study, 405 patients (34.5%) failed treatment. Using random forest analysis, an algorithm that provides the probability for failure for each specific patient was created. By order of importance, the ten most important variables associated with failure of I&D were serum CRP levels, positive blood cultures, indication for index arthroplasty other than osteoarthritis, not exchanging the modular components, use of immunosuppressive medication, late acute (haematogenous) infections, methicillin-resistant Staphylococcus aureus infection, overlying skin infection, polymicrobial infection, and older age. The algorithm had good discriminatory capability (area under the curve = 0.74). Cross-validation showed similar probabilities comparing predicted and observed failures indicating high accuracy of the model. Conclusion. This is the first study in the orthopaedic literature to use machine learning as a tool for predicting outcomes following I&D surgery. The developed algorithm provides the medical profession with a tool that can be employed in clinical decision-making and improve patient care. Future studies should aid in further validating this tool on additional cohorts. Cite this article: Bone Joint J 2020;102-B(7 Supple B):11–19

Introduction. There is a paucity of research investigating the effect of the experience of the operating surgeon on the long term function and survivorship of total hip replacements (THR). With the advent of individualised surgeon data being available to patients via the National Joint Registry, the desire to avoid complications and poor performance grows. This potentially reduces the availability of operative opportunities for trainees as consultants seek to ensure good results. Method & Results. A multicentre retrospective study of 879 THR was undertaken to investigate any differences in outcome between trainee surgeons and consultants. The effect of trainee supervision on the surgical outcome was also assessed. The primary outcome measures were survivorship and the Harris Hip Score (HHS). Rates of deep infection and dislocation were also recorded. Patients were evaluated pre-operatively and at 1, 3, 5, 7 and 10 years post-operatively. Surgical outcome was compared between junior trainees, senior trainees and consultants. The effect of supervision on final outcome was determined by comparing supervised and unsupervised trainees. 66.4% of patients were operated by consultants, 15.7% by junior trainees (ST3–5 equivalent) and 16.8% by senior trainees (ST6–8 equivalent). 10 year implant survival rates were; consultants 96.4 %, senior trainees 98.0 % and junior trainees 97.1%. There was no significant difference in post-operative HHS among consultants, senior and junior trainees at 1 year (p=0.122), 3 year (p=0.282), 5 year (p=0.063), 7 year (p=0.875), or at 10 years (p=0.924). There was no significant difference in HHS between supervised and unsupervised trainees at 1 year (p=0.220), 3 year (p=0.0.542), 5 year (p=0.880), 7 year (p=0.953) and 10-years (p=0.787). Comparison of surgical outcome between the supervised and unsupervised trainees also shows no significant difference in implant survival years (p=0.257). Conclusion. This study provides evidence that when trainees are appropriately supervised there is no negative effect on patient outcomes

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The objectives of this study were to investigate the patient characteristics and mortality of Vancouver type B periprosthetic femoral fractures (PFF) subgroups divided into two groups according to femoral component stability and to compare postoperative clinical outcomes according to treatment in Vancouver type B2 and B3 fractures.

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In this study, we aimed to visualize the spatial distribution characteristics of femoral head necrosis using a novel measurement method.