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机器人神经外科手术的学习曲线:一项系统综述。

Learning curves in robotic neurosurgery: a systematic review.

作者信息

Shlobin Nathan A, Huang Jonathan, Wu Chengyuan

机构信息

Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, 676 N. St. Clair Street, Suite 2210, Chicago, IL, 60611, USA.

Department of Neurological Surgery, Thomas Jefferson University Hospitals, Philadelphia, PA, USA.

出版信息

Neurosurg Rev. 2022 Dec 12;46(1):14. doi: 10.1007/s10143-022-01908-y.

Abstract

The transition to performing procedures robotically generally entails a period of adjustment known as a learning curve as the surgeon develops a familiarity with the technology. However, no study has comprehensively examined robotic learning curves across the field of neurosurgery. We conducted a systematic review to characterize the scope of literature on robotic learning curves in neurosurgery, assess operative parameters that may involve a learning curve, and delineate areas for future investigation. PubMed, Embase, and Scopus were searched. Following deduplication, articles were screened by title and abstract for relevance. Remaining articles were screened via full text for final inclusion. Bibliographic and learning curve data were extracted. Of 746 resultant articles, 32 articles describing 3074 patients were included, of which 23 (71.9%) examined spine, 4 (12.5%) pediatric, 4 (12.5%) functional, and 1 (3.1%) general neurosurgery. The parameters assessed for learning curves were heterogeneous. In total, 8 (57.1%) of 14 studies found reduced operative time with increased cases, while the remainder demonstrated no learning curve. Six (60.0%) of 10 studies reported reduced operative time per component with increased cases, while the remainder indicated no learning curve. Radiation time, radiation time per component, robot time, registration time, setup time, and radiation dose were assessed by ≤ 4 studies each, with 0-66.7% of studies demonstrated a learning curve. Four (44.4%) of 9 studies on accuracy showed improvement over time, while the others indicated no improvement over time. The number of cases required to reverse the learning curve ranged from 3 to 75. Learning curves are common in robotic neurosurgery. However, existing studies demonstrate high heterogeneity in assessed parameters and the number of cases that comprise the learning curve. Future studies should seek to develop strategies to reduce the number of cases required to reach the learning curve.

摘要

向机器人辅助手术的转变通常需要一段被称为学习曲线的调整期,因为外科医生需要逐渐熟悉这项技术。然而,尚无研究全面考察神经外科领域的机器人学习曲线。我们进行了一项系统综述,以描述神经外科机器人学习曲线的文献范围,评估可能涉及学习曲线的手术参数,并确定未来的研究方向。我们检索了PubMed、Embase和Scopus数据库。在去除重复文献后,通过标题和摘要筛选文章以确定其相关性。其余文章通过全文筛选以确定最终纳入情况。提取了文献目录和学习曲线数据。在746篇所得文章中,纳入了32篇描述3074例患者的文章,其中23篇(71.9%)研究脊柱手术,4篇(12.5%)研究小儿手术,4篇(12.5%)研究功能神经外科手术,1篇(3.1%)研究普通神经外科手术。评估学习曲线的参数具有异质性。在14项研究中,共有8项(57.1%)发现随着病例数增加手术时间减少,而其余研究未显示学习曲线。在10项研究中,6项(60.0%)报告随着病例数增加每个组件的手术时间减少,而其余研究未显示学习曲线。分别有≤4项研究评估了放射时间、每个组件的放射时间、机器人操作时间、注册时间、设置时间和放射剂量,其中0 - 66.7%的研究显示存在学习曲线。在9项关于准确性的研究中,4项(44.4%)显示随着时间推移准确性有所提高,而其他研究未显示随时间有改善。扭转学习曲线所需的病例数范围为3至75例。学习曲线在机器人神经外科手术中很常见。然而,现有研究表明,评估参数和构成学习曲线的病例数存在高度异质性。未来的研究应寻求制定策略,以减少达到学习曲线所需的病例数。

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