Preoperatively predicting failure to achieve the minimum clinically important difference and the substantial clinical benefit in patient-reported outcome measures for total hip arthroplasty patients using machine learning.

ABSTRACT:

Background

Attention to the collection of patient-reported outcomes measures (PROMs) associated with total hip arthroplasty (THA) is growing. The aim of this study was to preoperatively predict failure to achieve the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) between pre- and postoperative PROMs. In addition, we sought to identify factors predictive of failure to achieve MCID and SCB in patients undergoing a THA.

Methods

A retrospective query of the electronic health record data was performed at a single institution. Outcomes of interest were the anchor-based MCID, the distribution-based MCID, and the SCB for the Hip Disability and Osteoarthritis Outcome Score for Joint Replacement. Several machine learning models were built for eac

SUBMITTER: Park J 

PROVIDER: S-EPMC11827135 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature