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
For this retrospective cohort study, patients aged ≤ 30 years
(very young) who underwent total hip arthroplasty (THA) were compared
with patients aged ≥ 60 years (elderly) to evaluate the rate of
revision arthroplasty, implant survival, the indications for revision,
the complications, and the patient-reported outcomes. We retrospectively reviewed all patients who underwent primary
THA between January 2000 and May 2015 from our institutional database.
A total of 145 very young and 1359 elderly patients were reviewed.
The mean follow-up was 5.3 years (1 to 18). Logistic generalized
estimating equations were used to compare characteristics and the revision
rate. Survival was evaluated using Kaplan–Meier curves and hazard
rates were created using Cox regression.Aims
Patients and Methods
