Insights From Inputs: Enhancing Revision Total Joint Arthroplasty Resource Allocation With Machine Learning...

Revision total knee arthroplasty (rTKA) and revision total hip arthroplasty (rTHA) are among the most resource-intensive orthopaedic procedures. The primary aim of this study was to compare the accuracy of machine learning models between administrative and institutional datasets for predicting duration of surgery, length of stay, and 30-day hospital readmission for rTKA and rTHA based on preoperative factors and identify significant predictive features.

A national quality improvement database was queried from 2014 to 2019, and a local institutional arthroplasty database was queried from 2012 to 2022 for rTKAs and rTHAs. Datasets were independently split into training, validation, and testing and normalized based on year. Artificial neural networks (ANNs) for both procedures and each outcome were created, and their performance was compared to multivariable regression models. Models were compared between datasets using buffer accuracy (BA). Feature importance of ANNs was retrieved using Shapley Additive exPlanations values.

A total of 22,851 and 1,025 rTKA and 14,262 and 703 rTHA patients were included from the national and institutional datasets, respectively. For duration of surgery, the institutional ANNs outperformed the national ANNs with a 76.2 versus 45.4%, and 55.4 versus 43.1% 30-minute BA for rTKA and rTHA, respectively. For length of stay, the national ANNs yielded a superior 2-day BA than the institutional ANNs of 81.8 versus 67.7%, and 71.4 versus 48.2% for rTKA and rTHA, respectively. The 30-day readmission prediction using the national database had area under the curve scores of 0.593 and 0.590 for rTKA and rTHA, respectively.

The performance of ANN models varied significantly by dataset, but all models were superior to using historic averages. Future work should consider using accurate, arthroplasty-specific datasets based on the important features identified.

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