Division of Cardiothoracic Surgery, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas.
Division of Vascular Surgery and Endovascular Therapy, Michael E DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas.
Ann Thorac Surg. 2024 Mar;117(3):635-643. doi: 10.1016/j.athoracsur.2023.07.013. Epub 2023 Jul 28.
Technical skill is essential for good outcomes in cardiac surgery. However, no objective methods exist to measure dexterity while performing surgery. The purpose of this study was to validate sensor-based hand motion analysis (HMA) of technical dexterity while performing a graft anastomosis within a validated simulator.
Surgeons at various training levels performed an anastomosis while wearing flexible sensors (BioStamp nPoint, MC10 Inc) with integrated accelerometers and gyroscopes on each hand to quantify HMA kinematics. Groups were stratified as experts (n = 8) or novices (n = 18). The quality of the completed anastomosis was scored using the 10 Point Microsurgical Anastomosis Rating Scale (MARS10). HMA parameters were compared between groups and correlated with quality. Logistic regression was used to develop a predictive model from HMA parameters to distinguish experts from novices.
Experts were faster (11 ± 6 minutes vs 21 ± 9 minutes; P = .012) and used fewer movements in both dominant (340 ± 166 moves vs 699 ± 284 moves; P = .003) and nondominant (359 ± 188 moves vs 567 ± 201 moves; P = .02) hands compared with novices. Experts' anastomoses were of higher quality compared with novices (9.0 ± 1.2 MARS10 vs 4.9 ± 3.2 MARS10; P = .002). Higher anastomosis quality correlated with 9 of 10 HMA parameters, including fewer and shorter movements of both hands (dominant, r = -0.65, r = -0.46; nondominant, r = -0.58, r = -0.39, respectively).
Sensor-based HMA can distinguish technical dexterity differences between experts and novices, and correlates with quality. Objective quantification of hand dexterity may be a valuable adjunct to training and education in cardiac surgery training programs.
技术技能对于心脏手术的良好结果至关重要。然而，目前还没有测量手术过程中灵巧度的客观方法。本研究的目的是验证基于传感器的手部运动分析（HMA）在经过验证的模拟器中进行吻合时对技术灵巧度的测量。
不同培训水平的外科医生在双手佩戴带有集成加速度计和陀螺仪的柔性传感器（BioStamp nPoint，MC10 Inc）时进行吻合，以量化 HMA 运动学。将小组分为专家（n=8）或新手（n=18）。使用 10 分显微吻合评分量表（MARS10）对完成的吻合质量进行评分。比较组间 HMA 参数，并将其与质量相关联。使用逻辑回归从 HMA 参数中建立一个预测模型，以区分专家和新手。
专家的吻合时间更快（11±6 分钟 vs 21±9 分钟；P=0.012），并且在主导手（340±166 次 vs 699±284 次；P=0.003）和非主导手（359±188 次 vs 567±201 次；P=0.02）中使用的动作更少。与新手相比，专家的吻合质量更高（9.0±1.2 MARS10 vs 4.9±3.2 MARS10；P=0.002）。更高的吻合质量与 10 个 HMA 参数中的 9 个相关，包括双手的动作更少且更短（主导手，r=-0.65，r=-0.46；非主导手，r=-0.58，r=-0.39）。
基于传感器的 HMA 可以区分专家和新手之间的技术灵巧度差异，并与质量相关。手部灵巧度的客观量化可能是心脏外科培训计划中培训和教育的有价值的辅助手段。
