A Novel Machine Learning Model to Predict Revision ACL Reconstruction Failure in the MARS Cohort.

ABSTRACT:

Background

As machine learning becomes increasingly utilized in orthopaedic clinical research, the application of machine learning methodology to cohort data from the Multicenter ACL Revision Study (MARS) presents a valuable opportunity to translate data into patient-specific insights.

Purpose

To apply novel machine learning methodology to MARS cohort data to determine a predictive model of revision anterior cruciate ligament reconstruction (rACLR) graft failure and features most predictive of failure.

Study design

Cohort study; Level of evidence, 3.

Methods

The authors prospectively recruited patients undergoing rACLR from the MARS cohort and obtained preoperative radiographs, surgeon-reported intraoperative findings, and 2- and 6-year follow-up data on patien

SUBMITTER: MARS Group 

PROVIDER: S-EPMC11565622 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature