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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
Summary
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.
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.
