Department of Orthopaedic Surgery, Northwell Health, Lenox Hill Hospital, New York, New York.
Division of Joint Replacement, Stryker Orthopaedics, Mahwah, New Jersey.
J Knee Surg. 2023 Jul;36(8):873-877. doi: 10.1055/s-0042-1744193. Epub 2022 Mar 7.
Robotic-assisted total knee arthroplasty (RA-TKA) has been shown to improve the accuracy of bone resection, reduce radiographic outliers, and decrease iatrogenic injury. However, it has also been shown that RA-TKA surgical times can be longer than manual surgery during adoption. The purpose of this article was to investigate (1) the characteristics of the operative time curves and trends, noting the amount of surgeons who improved, for those who performed at least 12 cases (based on initial modeling); (2) the proportion of RA surgeons who achieved the same operative times for RA-TKA as compared with manual TKAs; and (3) the number of RA-TKA cases until a steady-state operative time was achieved. TKA operative times were collected from 30 hospitals for 146 surgeons between January 1, 2016, and December 31, 2019. A hierarchical Bayesian model was used to estimate the difference between the mean RA-TKA times by case interval and the weighted baseline for manual times. The learning curve was observed at the 12th case. Therefore, operative times were analyzed for each surgeon who performed at least 12 RA-TKA cases to determine the percentage of these surgeons who trended toward a decrease or increase in their times. These surgeons were further analyzed to determine the proportion who achieved the same operating times as manual TKAs. A further hierarchical Bayesian model was used to determine when these surgeons achieved steady-state operative times. There were 60 surgeons (82%) who had decreasing surgical times over the first 12 RA-TKA cases. The remaining 13 (18%) had increasing surgical times (mean increase of 0.59 minutes/case). Approximately two-thirds of the surgeons (64%) achieved the same operating times as manual cases. The steady-state time neutrality occurred between 15 and 20 cases and beyond. This study demonstrated the learning curve for a large cohort of RA-TKAs. This model demonstrated a learning curve between 15 and 20 cases and beyond. These are important findings for this innovative technology.
机器人辅助全膝关节置换术 (RA-TKA) 已被证明可以提高骨切除的准确性，减少放射学异常值，并降低医源性损伤。然而，在采用过程中，也已经表明 RA-TKA 的手术时间可能比手动手术长。本文的目的是调查 (1) 手术时间曲线和趋势的特征，注意那些至少进行了 12 例手术的医生的改进情况（基于初始建模）；(2) 实现 RA-TKA 手术时间与手动 TKA 相同的 RA 医生的比例；以及 (3) 达到稳定手术时间所需的 RA-TKA 病例数。从 2016 年 1 月 1 日至 2019 年 12 月 31 日，从 30 家医院收集了 TKA 手术时间，涉及 146 名外科医生。使用分层贝叶斯模型估计病例间隔和手动时间加权基线之间 RA-TKA 平均时间的差异。在第 12 例手术中观察到学习曲线。因此，对至少进行了 12 例 RA-TKA 手术的每位外科医生的手术时间进行了分析，以确定这些外科医生的时间趋势呈下降或上升的百分比。对这些外科医生进行了进一步分析，以确定他们达到与手动 TKA 相同手术时间的比例。进一步的分层贝叶斯模型用于确定这些外科医生何时达到稳定的手术时间。在最初的 12 例 RA-TKA 中，有 60 名外科医生 (82%) 的手术时间呈下降趋势。其余 13 名外科医生 (18%) 的手术时间呈上升趋势（平均每例增加 0.59 分钟）。大约三分之二的外科医生 (64%) 达到了与手动病例相同的手术时间。在 15 到 20 例和之后的手术中达到了稳定时间的中性。这项研究展示了一个大型 RA-TKA 队列的学习曲线。该模型在 15 到 20 例和之后的手术中展示了一个学习曲线。这些对这项创新技术来说是重要的发现。
