Guida Lelio, Sebök Martina, Oliveira Marcelo Magaldi, van Niftrik Christiaan Hendrik Bas, Charbel Fady T, Cenzato Marco, Regli Luca, Esposito Giuseppe
Department of Pediatric Neurosurgery, Assistance Pubilque Hôpitaux de Paris, Hôpital Necker Enfants Malades, Université de Paris Cité, 75015 Paris, France.
Departement of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, 8091 Zurich, Switzerland.
Brain Sci. 2024 Oct 17;14(10):1031. doi: 10.3390/brainsci14101031.
The literature lacks a combined analysis of neurosurgical microvascular anastomosis training models. We performed a systematic literature search to provide an overview of the existing models and proposed a classification system based on the level of simulation and reproducibility of the microvascular anastomosis.
The systematic literature search followed the PRISMA guidelines. We consulted MEDLINE, Web of Knowledge, and EMBASE independently for papers about bypass training models. Every training model was analyzed according to six tasks supposed to esteem their fidelity to the real operative setting by using a scoring system from zero to two. Finally, authors classified the models into five classes, from A to E, by summing the individual scores.
This study included 109 papers for analysis. Training models were grouped into synthetic tubes, ex vivo models (animal vessels, fresh human cadavers, human placentas) and in vivo simulators (live animals-rats, rabbits, pigs). By applying the proposed classification system, live animals and placentas obtained the highest scores, falling into class A (excellent simulators). Human cadavers and animal vessels (ex vivo) were categorized in class B (good simulators), followed by synthetic tubes (class C, reasonable simulators).
The proposed classification system helps the neurosurgeon to analyze the available training models for microvascular anastomosis critically, and to choose the most appropriate one according to the skills they need to improve.
目前的文献中缺乏对神经外科微血管吻合训练模型的综合分析。我们进行了一项系统的文献检索，以概述现有的模型，并基于微血管吻合的模拟水平和可重复性提出了一种分类系统。
系统文献检索遵循PRISMA指南。我们独立查阅了MEDLINE、Web of Knowledge和EMBASE，以查找有关搭桥训练模型的论文。通过使用从0到2的评分系统，根据六个任务对每个训练模型进行分析，以评估它们与实际手术场景的逼真度。最后，作者通过对各个分数求和，将模型分为从A到E的五个类别。
本研究纳入了109篇论文进行分析。训练模型分为合成管、离体模型（动物血管、新鲜人体尸体、人胎盘）和体内模拟器（活体动物——大鼠、兔子、猪）。通过应用所提出的分类系统，活体动物和胎盘获得了最高分，属于A类（优秀模拟器）。人体尸体和动物血管（离体）被归类为B类（良好模拟器），其次是合成管（C类，合理模拟器）。
所提出的分类系统有助于神经外科医生批判性地分析现有的微血管吻合训练模型，并根据他们需要提高的技能选择最合适的模型。
