Literature DB >> 36066661

Learning curve and influencing factors of performing microsurgical anastomosis: a laboratory prospective study.

Etienne Lefevre1, Mario Ganau2, Ismail Zaed3, Guaracy de Macedo Machado-Filho2, Antonino Scibilia2, Charles-Henry Mallereau2, Damien Bresson4, Julien Todeschi2, Helene Cebula2, Francois Proust2, Jean-Luc Vignes5, Alain-Charles Masquelet5,6, Sybille Facca7, Philippe Livernaux7, Alex Alfieri8, Taise Cruz Mosso Ramos2, Marcelo Magaldi9, Carmen Bruno10, Salvatore Chibbaro2.   

Abstract

Despite being a critical component of any cerebrovascular procedure, acquiring skills in microsurgical anastomosis is challenging for trainees. In this context, simulation models, especially laboratory training, enable trainees to master microsurgical techniques before performing real surgeries. The objective of this study was to identify the factors influencing the learning curve of microsurgical training. A prospective observational study was conducted during a 7-month diploma in microsurgical techniques carried out in the anatomy laboratory of the school of surgery. Training focused on end-to-end (ETE) and end-to-side (ETS) anastomoses performed on the abdominal aorta, vena cava, internal carotid and jugular vein, femoral artery and vein, caudal artery, etc. of Wistar strain rats under supervision of 2 expert anatomical trainers. Objective and subjective data were collected after each training session. The 44 microsurgical trainees enrolled in the course performed 1792 anastomoses (1577 ETE, 88%, vs. 215 ETS, 12%). The patency rate of 41% was independent from the trainees' surgical background and previous experience. The dissection and the temporary clamping time both significantly decreased over the months (p < 0.001). Technical mistakes were independently associated with thrombosis of the anastomoses, as assessed by the technical mistakes score (p < 0.01). The training duration (in weeks) at time of each anastomosis was the only significant predictor of permeability (p < 0.001). Training duration and technical mistakes constituted the two major factors driving the learning curve. Future studies should try and investigate other factors (such as access to wet laboratory, dedicated fellowships, mentoring during early years as junior consultant/attending) influencing the retention of surgical skills for our difficult and challenging discipline.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Entities:  

Keywords:  Anastomosis; Learning curve; Microsurgery; Microsurgical training; Surgical education

Mesh:

Year:  2022        PMID: 36066661     DOI: 10.1007/s10143-022-01856-7

Source DB:  PubMed          Journal:  Neurosurg Rev        ISSN: 0344-5607            Impact factor:   2.800


  12 in total

1.  Extracranial-intracranial bypass surgery for stroke prevention in hemodynamic cerebral ischemia: the Carotid Occlusion Surgery Study randomized trial.

Authors:  William J Powers; William R Clarke; Robert L Grubb; Tom O Videen; Harold P Adams; Colin P Derdeyn
Journal:  JAMA       Date:  2011-11-09       Impact factor: 56.272

2.  Learning curves in surgical practice.

Authors:  A N Hopper; M H Jamison; W G Lewis
Journal:  Postgrad Med J       Date:  2007-12       Impact factor: 2.401

3.  Neurosurgical digital teaching in low-middle income countries: beyond the frontiers of traditional education.

Authors:  Federico Nicolosi; Zefferino Rossini; Ismail Zaed; Angelos G Kolias; Maurizio Fornari; Franco Servadei
Journal:  Neurosurg Focus       Date:  2018-10       Impact factor: 4.047

4.  Preclinical Experience Using a New Robotic System Created for Microsurgery.

Authors:  Tom J M van Mulken; Clint A E M Boymans; Rutger M Schols; Raimondo Cau; Ferry B F Schoenmakers; Lisette T Hoekstra; Shan S Qiu; Jesse C Selber; René R W J van der Hulst
Journal:  Plast Reconstr Surg       Date:  2018-11       Impact factor: 4.730

5.  Learning of supermicrosurgical vascular anastomosis: MicroChirSim® procedural simulator versus Anastomosis Training Kit® procedural simulator.

Authors:  C Galmiche; J J Hidalgo Diaz; P Vernet; S Facca; G Menu; P Liverneaux
Journal:  Hand Surg Rehabil       Date:  2017-12-08       Impact factor: 0.969

6.  Robotic-assisted microvascular surgery: skill acquisition in a rat model.

Authors:  Nicholas S Clarke; Johnathan Price; Travis Boyd; Stefano Salizzoni; Kenton J Zehr; Alejandro Nieponice; Pietro Bajona
Journal:  J Robot Surg       Date:  2017-08-10

7.  Tracking the learning curve in microsurgical skill acquisition.

Authors:  Jesse C Selber; Edward I Chang; Jun Liu; Hiroo Suami; David M Adelman; Patrick Garvey; Matthew M Hanasono; Charles E Butler
Journal:  Plast Reconstr Surg       Date:  2012-10       Impact factor: 4.730

8.  Unfavorable outcomes in microsurgery: possibilities for improvement.

Authors:  Paolo Cariati; Almudena Cabello Serrano; Fernando Monsalve Iglesias; Maria Roman Ramos; Jose Fernandez Solis; Ildefonso Martinez Lara
Journal:  J Plast Surg Hand Surg       Date:  2019-05-08

9.  Transfer of Learning from Practicing Microvascular Anastomosis on Silastic Tubes to Rat Abdominal Aorta.

Authors:  Pooneh Mokhtari; Ali Tayebi Meybodi; Michael T Lawton; Andre Payman; Arnau Benet
Journal:  World Neurosurg       Date:  2017-09-01       Impact factor: 2.104

Review 10.  Measuring the surgical 'learning curve': methods, variables and competency.

Authors:  Nuzhath Khan; Hamid Abboudi; Mohammed Shamim Khan; Prokar Dasgupta; Kamran Ahmed
Journal:  BJU Int       Date:  2013-07-02       Impact factor: 5.588

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