(2025) Preoperatively predicting failure to achieve the minimum clinically important difference and substantial clinical benefit for total knee arthroplasty patients using machine learning

This study focused on preoperatively predicting failure to achieve the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) on the KOOS JR in patients undergoing a total knee arthroplasty (TKA). Machine-learning models were able to identify patients at risk of failure to achieve the threshold-based metrics (i.e., MCID and SCB) and other relevant preoperative factors. As such, these models may be used to both improve shared decision-making and help create risk-stratification tools to improve quality assessment of surgical outcomes.

External Collaborators: Jaeyoung Park, PhD; Xiang Zhong, PhD; Chancellor Gray, MD

Partner Institutions: University of Central Florida, University of Florida, Florida State University, and Florida Orthopaedic Institute