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 purpose of this study was to determine the impact of the removal of total knee arthroplasty (TKA) from the Medicare Inpatient Only (IPO) list on our Bundled Payments for Care Improvement (BPCI) Initiative in 2018. We examined our institutional database to identify all Medicare patients who underwent primary TKA from 2017 to 2018. Hospital inpatient or outpatient status was cross-referenced with Centers for Medicare & Medicaid Services (CMS) claims data. Demographics, comorbidities, and outcomes were compared between patients classified as ‘outpatient’ and ‘inpatient’ TKA. Episode-of-care BPCI costs were then compared from 2017 to 2018.Aims
Methods
