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

Background: Supermicrosurgical simulators and experimental models promote test viability, a faster learning curve, technical innovations, and improvements of the surgical dexterities. The authors aimed to present a systematic review and meta-analysis of preclinical experimental models and simulation platforms used for supermicrosurgery.

Methods: An electronic search was conducted across the PubMed MEDLINE, Embase, Web of Science, and Scopus databases in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Data collection included the types of experimental models and outcomes. Pooled estimates were calculated with a random-effect meta-analysis using the DerSimonian-Laird model.

Results: Thirty-eight articles were incorporated in the qualitative synthesis. Twenty-three articles reported the use of in vivo models (60.5%), 12 used ex vivo models (31.5%), and three used synthetic models (7.9%). The superficial inferior epigastric system of rats was the most common in vivo model, whereas chicken wings and hindlimbs were the most common methods used in ex vivo models. The most common methods to evaluate patency of anastomoses were gross inspection, passage of nylon thread into the lumen, and intravascular flow of an injected dye. Nineteen studies were incorporated in the meta-analysis. The overall rate of a successful anastomosis was 94.9% (95% CI, 92.3 to 97.5%). The success rate of in vivo models using rats was 92.5% (95% CI, 88.8 to 96.3%). The success rate of ex vivo models was 97.7% (95% CI, 94.6 to >99%).

Conclusion: Simulators that have high fidelity concerning the dissection of the vascular pedicle, flap elevation, supermicrovascular anastomosis, and adequate assessment of a successful anastomosis possess adequate predictive validation to evaluate and simulate the supermicrosurgical technique.

Clinical relevance statement: Supermicrosurgical simulators are designed to reproduce specific clinical scenarios; therefore, these should be implemented sequentially to develop specific competencies. Supermicrosurgical models must be regarded as mutually inclusive learning platforms to optimize the learning curve.