主动机器人全膝关节置换术的学习曲线。

Learning curve for active robotic total knee arthroplasty.

机构信息

Orthopaedic Surgery Resident, Department of Orthopaedic Surgery, New York University, Langone Orthopaedic Hospital, 301 East 17th Street, New York, NY, 10003, USA.

Insall Scott Kelly Institute, 260 East 66th Street, 1st Floor, New York, NY, 10065, USA.

出版信息

Knee Surg Sports Traumatol Arthrosc. 2022 Aug;30(8):2666-2676. doi: 10.1007/s00167-021-06452-8. Epub 2021 Feb 20.

DOI:10.1007/s00167-021-06452-8

PMID:33611607

Abstract

PURPOSE

Total Knee Arthroplasty (TKA) procedures incorporate technology in an attempt to improve outcomes. The Active Robot (ARo) performs a TKA with automated resections of the tibia and femur in efforts to optimize bone cuts. Evaluating the Learning Curve (LC) is essential with a novel tool. The purpose of this study was to assess the associated LC of ARo for TKA.

METHODS

A multi-center prospective FDA cohort study was conducted from 2017 to 2018 including 115 patients that underwent ARo. Surgical time of the ARo was defined as Operative time (OT), segmented as surgeon-dependent time (patient preparation and registration) and surgeon-independent time (autonomous bone resection by the ARo). An average LC for all surgeons was computed. Complication rates and patient-reported outcome (PRO) scores were recorded and examined to evaluate for any LC trends in these patient related factors.

RESULTS

The OT for the cases 10-12 were significantly quicker than the OT time of cases 1-3 (p < 0.028), at 36.5 ± 7.4 down from 49.1 ± 17 min. CUSUM and confidence interval analysis of the surgeon-dependent time showed different LCs for each surgeon, ranging from 12 to 19 cases. There was no difference in device related complications or PRO scores over the study timeframe.

CONCLUSION

Active Robotic total knee arthroplasty is associated with a short learning curve of 10-20 cases. The learning curve was associated with the surgical time dedicated to the robotic specific portion of the case. There was no learning curve-associated device-related complications, three-dimensional component position, or patient-reported outcome scores.

LEVEL OF EVIDENCE

Level II.

摘要

目的

全膝关节置换术（TKA）采用了旨在改善手术效果的技术。主动机器人（ARo）通过自动进行胫骨和股骨的切除来执行 TKA，以优化骨切割。评估新工具的学习曲线（LC）至关重要。本研究旨在评估 ARo 进行 TKA 的相关 LC。

方法

这是一项 2017 年至 2018 年进行的多中心前瞻性 FDA 队列研究，共纳入 115 名接受 ARo 的患者。ARo 的手术时间定义为手术时间（OT），分为依赖外科医生的时间（患者准备和注册）和独立于外科医生的时间（ARo 自主进行骨切除）。计算了所有外科医生的平均 LC。记录并检查并发症发生率和患者报告的结果（PRO）评分，以评估这些患者相关因素是否存在 LC 趋势。

结果

病例 10-12 的 OT 明显快于病例 1-3 的 OT 时间（p<0.028），从 49.1±17 分钟降至 36.5±7.4 分钟。CUSUM 和每个外科医生的依赖外科医生时间的置信区间分析显示，每位外科医生的 LC 不同，范围为 12 到 19 例。在研究期间，没有设备相关并发症或 PRO 评分的差异。