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DOI: 10.1055/s-0037-1602760
Development and Evaluation of a Simulation Model for Microvascular Anastomosis Training
Publication History
01 November 2016
23 March 2017
Publication Date:
04 May 2017 (online)
Abstract
Background Many plastic surgery training programs have implemented microvascular preparatory courses. However, these courses vary in length across institutions, lack formal assessment, and trainees receive certificates of completion rather than competency. In addition, many institutions use animate tissues as practice models which may not be readily available, require special treatment or storage, and lack consistency across vessel segments. In this study, we developed a proficiency-based training microvascular anastomosis curriculum using a synthetic model. In addition, we developed and validated a scoring rubric and patency testing apparatus.
Methods Proficiency benchmarks were developed by evaluating four plastic surgeons performing interrupted end-to-end anastomoses on synthetic vessels mounted superficially and at depth. Using a pretest–posttest design, seven plastic surgery residents from two institutions were asked to train to proficiency on the superficial exercise. Skills transfer was evaluated using a vessel mounted at depth. Each anastomosis was scored on 11 metrics of mechanics, completion time, stenosis, and leakage.
Results Experts outperformed residents prior to engaging in the training curriculum, confirming construct validity. Residents' skills significantly improved on 10 of 14 metrics after training, confirming curriculum effectiveness. Only one resident was able to achieve all proficiency benchmarks on two consecutive training trials. Skills learned on the superficially mounted vessel moderately transferred to the vessel mounted at depth as evidenced by significant pre- to posttest learning gains for 4 of the 14 metrics.
Conclusion The proficiency goals may have been overly stringent; however, residents improved microvascular anastomosis skills on the majority of metrics by engaging in simulation-based training using a readily available synthetic model.
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