Background. For total hip arthroplasty (THA), cognitive training prior to performing real surgery may be an effective adjunct alongside simulation to shorten the learning curve. This study sought to create a cognitive training tool to perform direct anterior approach THA, validated by expert surgeons; and test its use as a training tool compared to conventional material. Methods. We employed a modified
The burden of revision total hip arthroplasty (rTHA) continues to grow. The surgery is complex and associated with significant costs. Regional rTHA networks have been proposed to improve outcomes and to reduce re-revisions, and therefore costs. The aim of this study was to accurately quantify the cost and reimbursement for a rTHA service, and to assess the financial impact of case complexity at a tertiary referral centre within the NHS. A retrospective analysis of all revision hip procedures was performed at this centre over two consecutive financial years (2018 to 2020). Cases were classified according to the Revision Hip Complexity Classification (RHCC) and whether they were infected or non-infected. Patients with an American Society of Anesthesiologists (ASA) grade ≥ III or BMI ≥ 40 kg/m2 are considered “high risk” by the RHCC. Costs were calculated using the Patient Level Information and Costing System (PLICS), and remuneration based on Healthcare Resource Groups (HRG) data. The primary outcome was the financial difference between tariff and cost per patient episode.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