Givens Ritt R, Kim Terrence T, Malka Matan S, Lu Kevin, Zervos Thomas M, Lombardi Joseph, Sardar Zeeshan, Lehman Ronald, Lenke Lawrence, Sethi Rajiv, Lewis Stephen, Hedequist Daniel, Protopsaltis Themistocles, Larson A Noelle, Qureshi Sheeraz, Carlson Brandon, Skaggs David, Vitale Michael G
Division of Pediatric Orthopedics, Columbia University Medical Center, New York, NY, USA.
Department of Orthopaedic Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Spine Deform. 2025 Apr 1. doi: 10.1007/s43390-025-01066-3.
Robotic-assisted spine surgery (RASS) has increased in prevalence over recent years, and while much work has been done to analyze differences in outcomes when compared to the freehand technique, little has been done to characterize the potential pitfalls associated with using robotics. This study's goal was to leverage expert opinion to develop a classification system of potential sources of error that may be encountered when using robotics in spine surgery. This not only provides practitioners, particularly those in the early stages of robotic adoption, with insight into possible sources of error but also provides the community at large with a more standardized language through which to communicate.
The Delphi method, which is a validated system of developing consensus, was utilized. The method employed an iterative presentation of classification categories that were then edited, removed, or elaborated upon during several rounds of discussion. Voting took place to accept or reject the individual classification categories with consensus defined as ≥ 80% agreement.
After a three-round iterative survey and video conference Delphi process, followed by an in-person meeting at the Safety in Spine Surgery Summit, consensus was achieved on a classification system that includes four key types of potential sources of error in RASS as well as a list of the most commonly identified sources within each category. Initial sources of error that were considered included: cannula skidding/skive, penetration, screw misplacement, registration failure, and frame shift. After completion of the Delphi process, the final classification included four major types of pitfalls including: Reference/Navigation, Patient Factors, Technique, and Equipment Factors (available at https://safetyinspinesurgery.com/ ).
This work provides expert insight into potential sources of error in the setting of robotic spine surgery. The working group established four discrete categories while providing a standardized language to unify communication.
近年来，机器人辅助脊柱手术（RASS）的普及率有所上升。虽然已经做了很多工作来分析与徒手技术相比在手术结果上的差异，但对于使用机器人技术可能存在的潜在缺陷的描述却很少。本研究的目的是利用专家意见，开发一种分类系统，以识别在脊柱手术中使用机器人技术时可能遇到的潜在错误来源。这不仅为从业者，尤其是那些处于机器人技术应用初期的人，提供了对可能的错误来源的深入了解，还为整个领域提供了一种更标准化的交流语言。
采用德尔菲法，这是一种经过验证的达成共识的系统。该方法采用迭代方式呈现分类类别，然后在几轮讨论中对其进行编辑、删除或详细阐述。进行投票以接受或拒绝各个分类类别，共识定义为≥80%的一致同意。
经过三轮迭代调查和视频会议德尔菲流程，随后在脊柱手术安全峰会上召开了一次面对面会议，就一个分类系统达成了共识，该系统包括RASS中四种关键类型的潜在错误来源以及每个类别中最常见的错误来源列表。最初考虑的错误来源包括：套管打滑/刮擦、穿透、螺钉放置错误、注册失败和框架偏移。在德尔菲流程完成后，最终分类包括四种主要类型的缺陷，即：参考/导航、患者因素、技术和设备因素（可在https://safetyinspinesurgery.com/获取）。
这项工作为机器人脊柱手术中潜在的错误来源提供了专家见解。工作组确定了四个离散类别，同时提供了一种标准化语言来统一交流。
