Evidence map›Paper›PMID 39953514›Full record

ArticleBMC musculoskeletal disorders2025

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.

Jaeyoung Park, Xiang Zhong, Emilie N Miley, Chancellor F Gray

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Article in BMC musculoskeletal disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jaeyoung ParkSchool of Global Health Management and Informatics, University of Central Florida, Orlando, FL, USA.
Xiang ZhongDepartment of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA.
Emilie N MileyInstitute of Sports Sciences and Medicine, Department of Health, Nutrition, and Food Sciences, Florida State University, Tallahassee, FL, USA.
Chancellor F GrayFlorida Orthopaedic Institute, Gainesville, FL, USA. cgray@floridaortho.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAttention 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.

methodsA 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 each outcome and were evaluated by areas under the receiver operating characteristic curve and the precision-recall curve. Furthermore, logistic regression models were used to identify significant risk factors.

resultsOf the 857 patients who underwent THA, 350 patients completed both pre- and postoperative surveys. Of the final sample (i.e., 350 patients), 56 (16.0%), 29 (8.3%), and 71 (20.3%) failed to reach the anchor-based (i.e., 17.7 points) and distribution-based (10.6 points) MCIDs and the SCB (i.e., 22.0 points). The machine learning model performances were far beyond the baseline and comparable to the ones in existing studies, suggesting reliability in the prediction. Two shared factors associated with the failure in both the MCIDs and the SCB were highlighted: a patient's race and pre-existing mental illness.

conclusionUnderstanding the risk factors of failing to meet MCID and SCB may provide a more objective opportunity to quantify patient and surgeon expectations associated with THA. Our findings call stakeholders' particular attention to patients with preoperative mental disorders, and raise further questions regarding the impact of race, in the care of patients with degenerative hip disease.

Indexed as

Arthroplasty, Replacement, HipMachine LearningMinimal Clinically Important DifferenceOsteoarthritis, HipPatient Reported Outcome MeasuresAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesTreatment FailureMachine learningMinimal clinically important differencePatient-reported outcome measuresRisk factor analysisSubstantial clinical benefitTotal hip arthroplasty

Identifiers

PMID39953514
PMCPMC11827135

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.