Abstract
The ACS risk calculator (ARC) has proven less effective in predicting patient-specific risk of early reoperation after primary total knee arthroplasty (TKA), compromising care quality and cost efficiency. This study compared the performance of a machine learning (ML) model and ARC in predicting 30-day reoperation after primary TKA using a national-scale dataset. Data of 366,151 TKAs were acquired from the ACS-NSQIP database. A random forest model was derived using ARC build-in parameters from the training dataset via techniques of hyperparameter optimization and cross-validation. The predictive performance of random forest and ARC was evaluated by metrics of discrimination, calibration, and clinical utility using the testing dataset. The ML model demonstrated good discrimination and calibration (AUC: 0.72, slope: 1.18, intercept: - 0.14, Brier score: 0.012), outperforming ARC in all metrics (AUC: 0.51, slope: - 0.01, intercept: 0.01, Brier score: 0.135) including clinical utility measured by decision curve analyses. Age (> 67years) and BMI (> 34kg/m2) were the important predictors of reoperation. This study suggests the superiority of ML models in identifying individualized 30-day reoperation risk following TKA. ML models may be an adjunct prediction tool in enhancing patient-specific risk stratification and postoperative care management.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Similar Papers
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.