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
The World Health Organization (WHO) and the Centre
for Disease Control and Prevention (CDC) recently published guidelines
for the prevention of surgical site infection. The WHO guidelines,
if implemented worldwide, could have an immense impact on our practices
and those of the CDC have implications for healthcare policy in
the United States. Our aim was to review the strategies for prevention of periprosthetic
joint infection in light of these and other recent guidelines. Cite this article:
