DelliCarpini Gennaro, Passano Brandon, Yang Jie, Yassin Sallie M, Becker Jacob C, Aphinyanaphongs Yindalon, Capozzi James D
Department of Orthopedic Surgery, NYU Langone, Long Island, New York.
Departments of Population Health and Medicine, NYU Langone Health, New York, New York.
J Arthroplasty. 2025 May;40(5):1185-1191. doi: 10.1016/j.arth.2024.10.100. Epub 2024 Oct 28.
Accurate operative scheduling is essential for the appropriation of operating room esources. We sought to implement a machine learning model to predict primary total hip arthroplasty (THA) and total knee arthroplasty (TKA) case time.
A total of 10,590 THAs and 12,179 TKAs between July 2017 and December 2022 were retrospectively identified. Cases were chronologically divided into training, validation, and test sets. The test set cohort included 1,588 TKAs and 1,204 THAs. There were four ML algorithms developed: linear ridge regression (LR), random forest, XGBoost, and explainable boosting machine. Each model's case time estimate was compared to the scheduled estimate measured in 15-minute "wait" time blocks ("underbooking") and "excess" time blocks ("overbooking"). Surgical case time was recorded, and SHAP values were assigned to patient characteristics, surgical information, and the patient's medical condition to understand feature importance.
The most predictive model input was "median previous 30 procedure case times." The XGBoost model outperformed the other models in predicting both TKA and THA case times. The model reduced TKA 'excess time blocks' by 85 blocks (P < 0.001) and 'wait time blocks' by 96 blocks (P < 0.001). The model did not significantly reduce 'excess time blocks' in THA (P = 0.89) but did significantly reduce 'wait time blocks' by 134 blocks (P < 0.001). In total, the model improved TKA operative booking by 181 blocks (2,715 minutes) and THA operative booking by 138 blocks (2,070 minutes).
Machine learning outperformed a traditional method of scheduling total joint arthroplasty cases. The median time of the prior 30 surgical cases was the most influential on scheduling case time accuracy. As ML models improve, surgeons should consider ML utilization in case scheduling; however, prior 30 surgical cases may serve as an adequate alternative.
准确的手术排班对于手术室资源的合理分配至关重要。我们试图实施一种机器学习模型来预测初次全髋关节置换术（THA）和全膝关节置换术（TKA）的手术时间。
回顾性确定了2017年7月至2022年12月期间的10590例THA和12179例TKA。病例按时间顺序分为训练集、验证集和测试集。测试集队列包括1588例TKA和1204例THA。开发了四种机器学习算法：线性岭回归（LR）、随机森林、XGBoost和可解释增强机器。将每个模型的手术时间估计值与以15分钟“等待”时间块（“预订不足”）和“超额”时间块（“预订过多”）测量的预定估计值进行比较。记录手术病例时间，并将SHAP值分配给患者特征、手术信息和患者的医疗状况，以了解特征重要性。
最具预测性的模型输入是“前30例手术病例的中位时间”。XGBoost模型在预测TKA和THA手术时间方面优于其他模型。该模型将TKA的“超额时间块”减少了85个块（P < 0.001），将“等待时间块”减少了96个块（P < 0.001）。该模型在THA中未显著减少“超额时间块”（P = 0.89），但确实将“等待时间块”显著减少了134个块（P < 0.001）。总体而言，该模型将TKA手术预订改善了181个块（2715分钟），将THA手术预订改善了138个块（2070分钟）。
机器学习在全关节置换病例的排班方面优于传统方法。前30例手术病例的中位时间对排班病例时间准确性影响最大。随着机器学习模型的改进，外科医生在病例排班中应考虑使用机器学习；然而，前30例手术病例可能是一个合适的替代方案。
