使用术前患者因素的机器学习可以预测全膝关节置换术的手术时间和住院时间。
Machine learning using preoperative patient factors can predict duration of surgery and length of stay for total knee arthroplasty.
机构信息
Temerty Faculty of Medicine, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada; Orthopaedic Biomechanics Lab, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada.
Orthopaedic Biomechanics Lab, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON M5S 3G8, Canada.
出版信息
Int J Med Inform. 2022 Feb;158:104670. doi: 10.1016/j.ijmedinf.2021.104670. Epub 2021 Dec 22.
BACKGROUND
Total knee arthroplasty (TKA) is one of the most resource-intensive, high-volume surgical procedures. Two drivers of the cost of TKAs are duration of surgery (DOS) and postoperative inpatient length of stay (LOS). The ability to predict TKA DOS and LOS has substantial implications for hospital finances, scheduling, and resource allocation. The goal of this study was to predict DOS and LOS for elective unilateral TKAs using machine learning models (MLMs) based on preoperative factors.
METHODS
The American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database was queried for unilateral TKAs from 2014 to 2019. The dataset was split into training, validation, and testing based on year. Models (linear, tree-based, and multilayer perceptron (MLP)) were fitted to the training set in scikit-learn and PyTorch, with hyperparameters tuned on the validation set. The models were trained to minimize the mean squared error (MSE). Models with the best performance on the validation set were evaluated on the testing set according to 1) MSE, 2) buffer accuracy, and 3) classification accuracy, with results compared to a mean regressor.
RESULTS
A total of 302,300 patients were included in this study. During validation, the PyTorch MLPs had the best MSEs for DOS (0.918) and LOS (0.715). During testing, the PyTorch MLPs similarly performed best based on MSEs for DOS (0.896) and LOS (0.690). While the scikit-learn MLP yielded the best 30-minute buffer accuracy for DOS (78.8%), the PyTorch MLP provided the best 1-day buffer accuracy for LOS (75.2%). Nearly all the ML models were more accurate than the mean regressors for both DOS and LOS.
CONCLUSION
Conventional and deep learning models performed better than mean regressors for predicting DOS and LOS of unilateral elective TKA patients based on preoperative factors. Future work should include operational factors to improve overall predictions.
背景
全膝关节置换术（TKA）是资源最密集、高容量的手术之一。TKA 成本的两个驱动因素是手术持续时间（DOS）和术后住院时间（LOS）。预测 TKA DOS 和 LOS 的能力对医院财务、调度和资源分配具有重要意义。本研究的目的是使用基于术前因素的机器学习模型（MLM）预测择期单侧 TKA 的 DOS 和 LOS。
方法
从 2014 年至 2019 年，美国外科医师学会（ACS）国家手术和质量改进（NSQIP）数据库中查询单侧 TKA。根据年份将数据集分为训练集、验证集和测试集。在 scikit-learn 和 PyTorch 中，将模型（线性、基于树的和多层感知器（MLP））拟合到训练集，在验证集上调整超参数。模型的训练目标是最小化均方误差（MSE）。根据 1）MSE、2）缓冲区精度和 3）分类精度，将在验证集上表现最佳的模型评估测试集，结果与均值回归器进行比较。
结果
共有 302300 名患者纳入本研究。在验证过程中，PyTorch MLP 在 DOS（0.918）和 LOS（0.715）方面具有最佳的 MSE。在测试过程中，PyTorch MLP 同样基于 DOS（0.896）和 LOS（0.690）的 MSE 表现最佳。虽然 scikit-learn MLP 对 DOS 的 30 分钟缓冲区精度最高（78.8%），但 PyTorch MLP 对 LOS 的 1 天缓冲区精度最高（75.2%）。对于 DOS 和 LOS，几乎所有 ML 模型都比均值回归器更准确。
结论
基于术前因素，传统和深度学习模型在预测单侧择期 TKA 患者的 DOS 和 LOS 方面优于均值回归器。未来的工作应包括操作因素，以提高整体预测能力。
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