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. 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.Aims
Methods
We aimed to assess the influence of ethnicity on the incidence
of heterotopic ossification (HO) after total hip arthroplasty (THA). We studied the six-month post-operative anteroposterior radiographs
of 1449 consecutive primary THAs (1324 patients) and retrospectively
graded them for the presence of HO, using the Brooker Classification. Aims
Patients and Methods
