Department of Orthopedic Surgery, Golden Jubilee Medical Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Nakhon Pathom, Thailand.
J Orthop Surg Res. 2024 Aug 17;19(1):482. doi: 10.1186/s13018-024-04984-6.
The adoption of robot-assisted total knee arthroplasty (TKA) aims to enhance the precision of implant positioning and limb alignment. Despite its benefits, the adoption of such technology is often accompanied by an initial learning curve, which may result in increased operative times. This study sought to determine the learning curve for the ROSA (Robotic Surgical Assistant) Knee System (Zimmer Biomet) in performing TKA and to evaluate the accuracy of the system in executing bone cuts and angles as planned. The hypothesis of this study was that cumulative experience with this robotic system would lead to reduced operative times. Additionally, the ROSA system demonstrated reliability in terms of the accuracy and reproducibility of bone cuts.
In this retrospective observational study, we examined 110 medical records from 95 patients who underwent ROSA-assisted TKA performed by three surgeons. We employed the cumulative summation methodology to assess the learning curves related to operative time. Furthermore, we evaluated the accuracy of the ROSA Knee System in performing TKA by comparing planned versus validated values for femoral and tibial bone cuts and angles.
The learning curve for the ROSA Knee System spanned 14, 14, and 6 cases for the respective surgeons, with operative times decreasing by 22 min upon reaching proficiency (70.8 vs. 48.9 min; p < 0.001). Significant discrepancies were observed between the average planned and validated cuts and angles for femoral bone cuts (0.4 degree ± 2.4 for femoral flexion, 0.1 degree ± 0.6 for femoral coronal alignment, 0.3 mm ± 1.2 for distal medial femoral resection, 1.4 mm ± 8.8 for distal lateral femoral resection) and hip-knee-ankle axis alignment (0.3 degree ± 1.9 )(p < 0.05) but not for tibial bone cuts. Differences between planned and validated measurements during the learning and proficiency phases were nonsignificant across all parameters, except for the femoral flexion angle (0.42 degree ± 0.8 vs. 0.44 degree ± 2.7) (p = 0.49).
The ROSA Knee System can be integrated into surgical workflows after a modest learning curve of 6 to 14 cases. The system demonstrated high accuracy and reproducibility, particularly for tibial bone cuts. Acknowledging the learning curve associated with new robot-assisted TKA technologies is vital for their effective implementation.
机器人辅助全膝关节置换术（TKA）的采用旨在提高植入物定位和肢体对线的精度。尽管有这些好处，但采用这种技术通常伴随着初始学习曲线，这可能导致手术时间延长。本研究旨在确定 ROSA（机器人手术助手）膝关节系统（捷迈邦美）进行 TKA 的学习曲线，并评估该系统在执行计划中的骨切割和角度方面的准确性。本研究的假设是，随着使用该机器人系统经验的积累，手术时间会减少。此外，ROSA 系统在骨切割的准确性和可重复性方面表现出可靠性。
在这项回顾性观察研究中，我们检查了 95 名患者的 110 份病历，这些患者均由 3 名外科医生进行了 ROSA 辅助 TKA。我们采用累积和方法评估与手术时间相关的学习曲线。此外，我们通过比较股骨和胫骨骨切割和角度的计划值与验证值来评估 ROSA 膝关节系统在执行 TKA 方面的准确性。
ROSA 膝关节系统的学习曲线分别为三位外科医生的 14、14 和 6 例，达到熟练程度时手术时间减少 22 分钟（70.8 分钟与 48.9 分钟；p<0.001）。股骨骨切割的平均计划和验证值之间存在显著差异（股骨屈曲时 0.4 度±2.4，股骨冠状对线时 0.1 度±0.6，股骨远端内侧切除时 0.3 毫米±1.2，股骨远端外侧切除时 1.4 毫米±8.8）和髋关节-膝关节-踝关节轴对齐（0.3 度±1.9）（p<0.05），但胫骨骨切割则不然。在学习和熟练阶段，除股骨屈曲角度（0.42 度±0.8 与 0.44 度±2.7）（p=0.49）外，所有参数的计划和验证测量值之间的差异均无统计学意义。
ROSA 膝关节系统在经历 6 至 14 例适度学习曲线后即可整合到手术流程中。该系统表现出高精度和可重复性，特别是对于胫骨骨切割。认识到新的机器人辅助 TKA 技术相关的学习曲线对于其有效实施至关重要。
