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

ABSTRACT:

Background

A clear understanding of minimal clinically important difference (MCID) and substantial clinical benefit (SCB) is essential for effectively implementing patient-reported outcome measurements (PROMs) as a performance measure for total knee arthroplasty (TKA). Since not achieving MCID and SCB may reflect suboptimal surgical benefit, the primary aim of this study was to use machine learning to predict patients who may not achieve the threshold-based outcomes (i.e., MCID and SCB) on the Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) following TKA.

Methods

Data from 1064 patients who underwent TKA at a single academic medical center between 2016 and 2022 contained 81 preoperative variables, including routinely collected measures and PROMs

SUBMITTER: Park J 

PROVIDER: S-EPMC12424216 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature