Stryker Corporation, Mahwah, NJ, USA.
Department of Orthopaedics, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
Knee Surg Sports Traumatol Arthrosc. 2023 Aug;31(8):3160-3171. doi: 10.1007/s00167-023-07314-1. Epub 2023 Jan 18.
Increased operative time can be due to patient, surgeon and surgical factors, and may be predicted by machine learning (ML) modeling to potentially improve staff utilization and operating room efficiency. The purposes of our study were to: (1) determine how demographic, surgeon, and surgical factors affected operative times, and (2) train a ML model to estimate operative time for robotic-assisted primary total knee arthroplasty (TKA).
A retrospective study from 2007 to 2020 was conducted including 300,000 unilateral primary TKA cases. Demographic and surgical variables were evaluated using Wilcoxon/Kruskal-Wallis tests to determine significant factors of operative time as predictors in the ML models. For the ML analysis of robotic-assisted TKAs (> 18,000), two algorithms were used to learn the relationship between selected predictors and operative time. Predictive model performance was subsequently assessed on a test data set comparing predicted and actual operative time. Root mean square error (RMSE), R and percentage of predictions with an error < 5/10/15 min were computed.
Males, BMI > 40 kg/m and cemented implants were associated with increased operative time, while age > 65yo, cementless, and high surgeon case volume had reduced operative time. Robotic-assisted TKA increased operative time for low-volume surgeons and decreased operative time for high-volume surgeons. Both ML models provided more accurate operative time predictions than standard time estimates based on surgeon historical averages.
This study demonstrated that greater surgeon case volume, cementless fixation, manual TKA, female, older and non-obese patients reduced operative time. ML prediction of operative time can be more accurate than historical averages, which may lead to optimized operating room utilization.
III.
手术时间的延长可能与患者、外科医生和手术因素有关，并且可以通过机器学习 (ML) 建模进行预测，从而有可能提高员工利用率和手术室效率。我们研究的目的是：(1)确定人口统计学、外科医生和手术因素如何影响手术时间，以及 (2) 训练一个 ML 模型来估计机器人辅助初次全膝关节置换术 (TKA) 的手术时间。
对 2007 年至 2020 年期间的 30 万例单侧初次 TKA 病例进行回顾性研究。使用 Wilcoxon/Kruskal-Wallis 检验评估人口统计学和手术变量，以确定手术时间的显著因素作为 ML 模型中的预测因素。对于机器人辅助 TKA (>18,000 例) 的 ML 分析，使用两种算法来学习所选预测因子与手术时间之间的关系。随后，在测试数据集上评估预测模型性能，比较预测和实际手术时间。计算均方根误差 (RMSE)、R 和预测误差 <5/10/15 分钟的百分比。
男性、BMI>40kg/m2 和骨水泥固定假体与手术时间延长有关，而年龄>65 岁、非骨水泥固定和外科医生高手术量与手术时间缩短有关。机器人辅助 TKA 增加了低手术量外科医生的手术时间，减少了高手术量外科医生的手术时间。两种 ML 模型提供的手术时间预测比基于外科医生历史平均值的标准时间估计更准确。
本研究表明，外科医生手术量较大、非骨水泥固定、手动 TKA、女性、年龄较大和非肥胖患者的手术时间缩短。手术时间的 ML 预测可能比历史平均值更准确，这可能导致手术室利用率的优化。
III 级。
