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Experimental Models and Practical Simulators for Supermicrosurgery: An Updated Systematic Review and Meta-Analysis.

Joseph M Escandón1, Pedro Ciudad2, Samuel O Poore3

  • 1From the Division of Plastic and Reconstructive Surgery, Strong Memorial Hospital, University of Rochester Medical Center.

Plastic and Reconstructive Surgery
|February 2, 2023
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Summary
This summary is machine-generated.

Supermicrosurgery training models, including in vivo and ex vivo systems, significantly improve surgical skills and learning curves. High-fidelity simulators offer predictive validation for supermicrosurgical techniques, enhancing overall competency.

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Area of Science:

  • Microsurgery
  • Surgical Simulation
  • Preclinical Research

Background:

  • Supermicrosurgery training relies on simulators and experimental models to enhance surgical dexterity and accelerate learning.
  • This study systematically reviews and meta-analyzes preclinical models and simulation platforms for supermicrosurgery.

Approach:

  • A comprehensive electronic search was performed across major databases (PubMed MEDLINE, Embase, Web of Science, Scopus).
  • Data on experimental models and outcomes were collected, with pooled estimates calculated using random-effect meta-analysis.
  • Thirty-eight articles were included in the qualitative synthesis, and 19 in the meta-analysis.

Key Points:

  • In vivo models (60.5%) and ex vivo models (31.5%) were predominantly used, with rat epigastric systems and chicken wings/hindlimbs being common.
  • Common patency assessment methods included gross inspection, nylon thread passage, and dye injection.
  • The overall successful anastomosis rate was 94.9%, with in vivo rat models at 92.5% and ex vivo models at 97.7%.

Conclusions:

  • High-fidelity supermicrosurgical simulators demonstrate predictive validation for evaluating and simulating techniques like vascular pedicle dissection and anastomosis.
  • Sequential implementation of simulators tailored to specific clinical scenarios is recommended for competency development.
  • Supermicrosurgical models should be viewed as complementary learning platforms to optimize the surgical learning curve.