A Machine Learning Algorithm Outperforms Traditional Multiple Regression to Predict Risk of Unplanned Overnight Stay Following Outpatient Medial Patellofemoral Ligament Reconstruction
| Autor: | Chimere O. Ezuma, B.S., Yining Lu, M.D., Ayoosh Pareek, M.D., Ryan Wilbur, B.S., Aaron J. Krych, M.D., Brian Forsythe, M.D., Christopher L. Camp, M.D. |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Arthroscopy, Sports Medicine, and Rehabilitation, Vol 4, Iss 3, Pp e1103-e1110 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2666-061X 56743327 |
| DOI: | 10.1016/j.asmr.2022.03.009 |
| Popis: | Purpose: To determine whether conventional logistic regression or machine learning algorithms were more precise in identifying the risk factors for unplanned overnight admission after medial patellofemoral ligament (MPFL) reconstruction. Methods: A retrospective review of the prospectively collected National Surgical Quality Improvement Program database was performed to identify patients who underwent outpatient MPFL reconstruction from 2006–2018. Patients admitted overnight were identified as those with length of stay of 1 or more days. Models were generated using random forest, extreme gradient boosting, adaptive boosting, or elastic net penalized logistic regression, and an additional model was produced as a weighted ensemble of the 4 final algorithms. The predictive capacity of these models was compared to that of logistic regression. Results: Of the 1307 patients identified, 221 (16.9%) required at least one overnight stay after MPFL reconstruction. Multivariate logistic regression found the following variables to be predictors of inpatient admission: age (odds ratio [OR] = 1.03 [95% confidence interval {CI} 1.02-1.04]; P |
| Databáze: | Directory of Open Access Journals |
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