A development of machine learning models to preoperatively predict insufficient clinical improvement after total knee arthroplasty.

ABSTRACT: Identifying patients unlikely to achieve meaningful improvement following total knee arthroplasty (TKA) supports more effective shared decision-making (SDM). This study aimed to develop and validate machine learning (ML) models that preoperatively predict insufficient clinical improvement one year after TKA using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) subscales and total scores, and to assess the important predictive variables. A retrospective analysis was performed on consecutive primary TKA patients from 2004 to 2022 at a single tertiary hospital was conducted. Insufficient clinical improvement was defined as not achieving the minimal clinically important difference (MCID) for each WOMAC subscale and total. Candidate preoperative variables included demogra

SUBMITTER: Gimm G 

PROVIDER: S-EPMC12366143 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature