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.

Journal: BMC musculoskeletal disorders

PMID: 39953514

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.

Authors

Jaeyoung Park

Booth School of Business, University of Chicago, Chicago, IL, USA.
Xiang Zhong

Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA.
Emilie N Miley

Department of Orthopaedic Surgery and Sports Medicine, University of Florida, Gainesville, FL, USA.
Chancellor F Gray

Florida Orthopaedic Institute, Gainesville, FL, USA.

Keywords

Aged Arthroplasty, Replacement, Hip Female Humans Machine Learning Male Middle Aged Minimal Clinically Important Difference Osteoarthritis, Hip Patient Reported Outcome Measures Predictive Value of Tests Retrospective Studies Treatment Failure

External Resources

View on PubMed Access via DOI PubMed (39953514)

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

Authors

Keywords

External Resources

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