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机器人辅助胸外科纵隔手术的学习曲线研究
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Abstract:
目的:基于CUSUM分析法,探讨机器人辅助胸外科纵隔手术的学习曲线。方法:按照纳入标准与排除标准,采集自2020年1月至2022年12月以来由我院胸外科同一主刀医师完成的最初95例RATS纵隔手术的临床资料,运用CUSUM Analysis绘制分析学习曲线。结果:本研究中共计纳入95例手术病例,包含男性46例,女性49例,平均年龄为(48.2 ± 14.7)岁。平均手术时间101.97 ± 41.19 (分钟)。得到CUSUM曲线的最佳拟合模型三次方曲线为CUSUM (分钟) = 0.005X3 ? 1.020X2 + 51.586X ? 115.028 (X为手术例数)。35例为跨越学习曲线所需要累积的最低手术例数。在熟练掌握阶段,手术时间、术中出血量、术后24小时胸管引流量、术后并发症方面较学习提升阶段有显著的缩短或减少,而在术后带管时间、住院时间方面则没有明显差异。结论:基于CUSUM分析方法,发现RATS纵隔手术的学习曲线可以清晰地分为学习提升及熟练掌握两个阶段。在经过至少35例手术经验的积累后,可以跨过学习曲线顶点,达到熟练掌握阶段。
Objective: Exploring the learning curve of robot-assisted thoracic mediastinal surgery based on CUSUM analysis. Methods: In accordance with the inclusion and exclusion criteria, clinical data on the initial 95 RATS mediastinal surgeries performed by the same attending surgeon in our thoracic surgery department since January 2020 to December 2022 were collected, and an analytic learning curve was plotted using CUSUM Analysis. Results: A total of 95 surgical cases were included in this study containing 46 males and 49 females with a mean age of (48.2 ± 14.7) years. The mean operative time was 101.97 ± 41.19 (minutes). The best fitting model cubic curve for the CUSUM curve was obtained as CUSUM (minutes) = 0.005X3 ? 1.020X2 + 51.586X ? 115.028 (X is the number of surgical cases). 35 cases is the minimum number of surgical cases that need to be accrued to cross the learning curve. In the proficiency stage, there was a significant shortening or reduction in operative time, intraoperative bleeding, 24-hour postoperative chest tube drainage, and postoperative complications compared to the learning improvement stage, while there was no significant difference in postoperative tube carrying time and hospitalization time. Conclusion: Based on the CUSUM analysis method, it was found that the learning curve of RATS mediastinal surgery can be clearly divided into two stages: learning improvement and proficiency. The learning curve apex can be crossed to reach the proficiency stage after the accumulation of experience in at least 35 surgical cases.
[1] | Mao, Y., Liang, H., Deng, S., Qiu, Y., Zhou, Y., Chen, H., et al. (2021) Non-Intubated Video-Assisted Thoracic Surgery for Subxiphoid Anterior Mediastinal Tumor Resection. Annals of Translational Medicine, 9, 403-403. https://doi.org/10.21037/atm-20-6125 |
[2] | 贾昱欣, 张亚杰, 李鹤成. 机器人手术在胸外科的应用现状与进展[J]. 机器人外科学杂志(中英文), 2022, 3(5): 367-375. |
[3] | Durrand, J.W., Moore, J. and Danjoux, G. (2021) Prehabilitation and Preparation for Surgery: Has the Digital Revolution Arrived? Anaesthesia, 77, 635-639. https://doi.org/10.1111/anae.15622 |
[4] | 中国医师协会医学机器人医师分会胸外科专业委员会筹备组, 谭群友, 陶绍霖, 等. 机器人辅助纵隔肿瘤手术中国专家共识(2019版) [J]. 中国胸心血管外科临床杂志, 2020, 27(2): 117-125. |
[5] | 陈伟钢, 张昊, 武文斌, 等. 单向式与常规单孔胸腔镜肺叶切除术后早期拔除胸腔引流管的回顾性队列研究[J]. 中国胸心血管外科临床杂志, 2023, 30(1): 71-77. |
[6] | Wittenberg, P. (2022) Modeling the Patient Mix for Risk-Adjusted CUSUM Charts. Statistical Methods in Medical Research, 31, 779-800. https://doi.org/10.1177/09622802211053205 |
[7] | Camirand Lemyre, F., Chalifoux, K., Desharnais, B. and Mireault, P. (2021) Squaring Things up with R2: What It Is and What It Can (and Cannot) Tell You. Journal of Analytical Toxicology, 46, 443-448. https://doi.org/10.1093/jat/bkab036 |
[8] | Liu, Q. and Wang, L. (2020) T-Test and ANOVA for Data with Ceiling and/or Floor Effects. Behavior Research Methods, 53, 264-277. https://doi.org/10.3758/s13428-020-01407-2 |
[9] | Yoshino, I., Hashizume, M., Shimada, M., Tomikawa, M., Tomiyasu, M., Suemitsu, R., et al. (2001) Thoracoscopic Thymomectomy with the Da Vinci Computer-Enhanced Surgical System. The Journal of Thoracic and Cardiovascular Surgery, 122, 783-785. https://doi.org/10.1067/mtc.2001.115231 |
[10] | Marulli, G., Rea, F., Melfi, F., et al. (2012) Robot-Aided Thoracoscopic Thymectomy for Early-Stage Thymoma: A Multicenter European Study. The Journal of Thoracic and Cardiovascular Surgery, 144, 1125-1132. |
[11] | Bodner, J., Wykypiel, H., Greiner, A., Kirchmayr, W., Freund, M.C., Margreiter, R., et al. (2004) Early Experience with Robot-Assisted Surgery for Mediastinal Masses. The Annals of Thoracic Surgery, 78, 259-265. https://doi.org/10.1016/j.athoracsur.2004.02.006 |
[12] | 丁仁泉, 童向东, 许世广, 等. 达芬奇机器人手术系统与电视胸腔镜在胸内纵隔疾病手术治疗中的对比研究[J]. 中国肺癌杂志, 2014, 17(7): 557-562. |
[13] | 杨煜, 章雪飞, 茅腾, 等. 达芬奇机器人辅助纵隔肿瘤切除术339例近期疗效分析: 一项单中心回顾性病例对照研究[J]. 中华胸心血管外科杂志, 2020, 36(11): 660-663. |
[14] | Bolsin, S. (2000) The Use of the CUSUM Technique in the Assessment of Trainee Competence in New Procedures. International Journal for Quality in Health Care, 12, 433-438. https://doi.org/10.1093/intqhc/12.5.433 |
[15] | Biswas, P. and Kalbfleisch, J.D. (2008) A Risk‐Adjusted CUSUM in Continuous Time Based on the Cox Model. Statistics in Medicine, 27, 3382-3406. https://doi.org/10.1002/sim.3216 |
[16] | Shi, Y., Wang, W., Qiu, W., Zhao, S., Wang, J., Weng, Y., et al. (2019) Learning Curve from 450 Cases of Robot-Assisted Pancreaticoduocectomy in a High-Volume Pancreatic Center: Optimization of Operative Procedure and a Retrospective Study. Annals of Surgery, 274, e1277-e1283. https://doi.org/10.1097/sla.0000000000003664 |
[17] | 张素伟, 宫迎迎, 王云飞, 等. 单中心单人机器人辅助腹腔镜子宫内膜癌分期手术学习曲线及临床分析[J]. 机器人外科学杂志(中英文), 2022, 3(6): 471-476. |
[18] | 秦倩, 张磊, 时飞宇, 等. 达芬奇机器人手术系统辅助直肠癌根治术学习曲线研究[J]. 中国实用外科杂志, 2022, 42(8): 920-924. |
[19] | 刘博, 汪明敏, 许世广, 等. 达芬奇机器人纵隔肿瘤切除术的学习曲线[J]. 中国胸心血管外科临床杂志, 2017, 24(2): 127-131. |