Achievement of accurate microbiological diagnosis prior to revision is key to reducing the high rates of persistent infection after revision knee surgery. The effect of change in the microorganism between the first- and second-stage revision of total knee arthroplasty for periprosthetic joint infection (PJI) on the success of management is not clear. A two-centre retrospective cohort study was conducted to review the outcome of patients who have undergone two-stage revision for treatment of knee arthroplasty PJI, focusing specifically on isolated micro-organisms at both the first- and second-stage procedure. Patient demographics, medical, and orthopaedic history data, including postoperative outcomes and subsequent treatment, were obtained from the electronic records and medical notes.Aims
Methods
To identify the responsiveness, minimal clinically important difference (MCID), minimal clinical important change (MIC), and patient-acceptable symptom state (PASS) thresholds in the 36-item Short Form Health Survey questionnaire (SF-36) (v2) for each of the eight dimensions and the total score following total knee arthroplasty (TKA). There were 3,321 patients undergoing primary TKA with preoperative and one-year postoperative SF-36 scores. At one-year patients were asked how satisfied they were and “How much did the knee arthroplasty surgery improve the quality of your life?”, which was graded as: great, moderate, little (n = 277), none (n = 98), or worse.Aims
Methods
The aims of this study were to assess mapping models to predict the three-level version of EuroQoL five-dimension utility index (EQ-5D-3L) from the Oxford Knee Score (OKS) and validate these before and after total knee arthroplasty (TKA). A retrospective cohort of 5,857 patients was used to create the prediction models, and a second cohort of 721 patients from a different centre was used to validate the models, all of whom underwent TKA. Patient characteristics, BMI, OKS, and EQ-5D-3L were collected preoperatively and one year postoperatively. Generalized linear regression was used to formulate the prediction models.Aims
Methods
