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

被引:0
作者
Park, Jaeyoung [1 ]
Zhong, Xiang [2 ]
Miley, Emilie N. [3 ,4 ]
Gray, Chancellor F. [5 ]
机构
[1] Univ Cent Florida, Sch Global Hlth Management & Informat, Orlando, FL USA
[2] Univ Florida, Dept Ind & Syst Engn, Gainesville, FL USA
[3] Florida State Univ, Inst Sports Sci & Med, Dept Hlth Nutr & Food Sci, Tallahassee, FL USA
[4] Tallahassee Orthoped Clin, Tallahassee, FL USA
[5] Florida Orthopaed Inst, Gainesville, FL 32607 USA
关键词
Patient-reported outcome measures; Minimal clinically important difference; Substantial clinical benefit; Total hip arthroplasty; Machine learning; Risk factor analysis; TOTAL JOINT ARTHROPLASTY; KNEE REPLACEMENT; IMPROVEMENT; ALGORITHMS;
D O I
10.1186/s12891-025-08339-y
中图分类号
R826.8 [整形外科学]; R782.2 [口腔颌面部整形外科学]; R726.2 [小儿整形外科学]; R62 [整形外科学(修复外科学)];
学科分类号
摘要
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.
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页数:10
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