Health and Socioeconomic Risk Factors for Unplanned Hospitalization Following Ambulatory Unicompartmental Knee Arthroplasty: Development of a Patient Selection Tool Using Machine Learning.

Autor: Labott JR; Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota., Lu Y; Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota; Department of Orthopedic Surgery, Orthopedic Surgery Artificial Intelligence Lab (OSAIL), Mayo Clinic, Rochester, Minnesota., Salmons HI; Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota., Camp CL; Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota; Department of Orthopedic Surgery, Orthopedic Surgery Artificial Intelligence Lab (OSAIL), Mayo Clinic, Rochester, Minnesota., Wyles CC; Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota; Department of Orthopedic Surgery, Orthopedic Surgery Artificial Intelligence Lab (OSAIL), Mayo Clinic, Rochester, Minnesota., Taunton MJ; Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota; Department of Orthopedic Surgery, Orthopedic Surgery Artificial Intelligence Lab (OSAIL), Mayo Clinic, Rochester, Minnesota.
Jazyk: angličtina
Zdroj: The Journal of arthroplasty [J Arthroplasty] 2023 Oct; Vol. 38 (10), pp. 1982-1989. Date of Electronic Publication: 2023 Jan 26.
DOI: 10.1016/j.arth.2023.01.026
Abstrakt: Background: Identifying ambulatory surgical candidates at risk for adverse surgical outcomes can optimize outcomes. The purpose of this study was to develop and internally validate a machine learning (ML) algorithm to predict contributors to unexpected hospitalizations after ambulatory unicompartmental knee arthroplasty (UKA).
Methods: A total of 2,521 patients undergoing UKA from 2006 to 2018 were retrospectively evaluated. Patients admitted overnight postoperatively were identified as those who had a length of stay ≥ 1 day were analyzed with four individual ML models (ie, random forest, extreme gradient boosting, adaptive boosting, and elastic net penalized logistic regression). An additional model was produced as a weighted ensemble of the four individual algorithms. Area under the receiver operating characteristics (AUROC) compared predictive capacity of these models to conventional logistic regression techniques.
Results: Of the 2,521 patients identified, 103 (4.1%) required at least one overnight stay following ambulatory UKA. The ML ensemble model achieved the best performance based on discrimination assessed via internal validation (AUROC = 87.3), outperforming individual models and conventional logistic regression (AUROC = 81.9-85.7). The variables determined most important by the ensemble model were cumulative time in the operating room, utilization of general anesthesia, increasing age, and patient residency in more urban areas. The model was integrated into a web-based open-access application.
Conclusion: The ensemble gradient-boosted ML algorithm demonstrated the highest performance in identifying factors contributing to unexpected hospitalizations in patients receiving UKA. This tool allows physicians and healthcare systems to identify patients at a higher risk of needing inpatient care after UKA.
(Copyright © 2023 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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