Microvascular Anastomosis: Proposition of a Learning Curve

Learning to perform a microvascular anastomosis is one of the most difficult tasks in cerebrovascular surgery. Previous studies offer little regarding the optimal protocols to maximize learning efficiency. This failure stems mainly from lack of knowledge about the learning curve of this task.

To delineate this learning curve and provide information about its various features including acquisition, improvement, consistency, stability, and recall.

Five neurosurgeons with an average surgical experience history of 5 yr and without any experience in bypass surgery performed microscopic anastomosis on progressively smaller-caliber silastic tubes (Biomet, Palm Beach Gardens, Florida) during 24 consecutive sessions. After a 1-, 2-, and 8-wk retention interval, they performed recall test on 0.7-mm silastic tubes. The anastomoses were rated based on anastomosis patency and presence of any leaks.

Improvement rate was faster during initial sessions compared to the final practice sessions. Performance decline was observed in the first session of working on a smaller-caliber tube. However, this rapidly improved during the following sessions of practice. Temporary plateaus were seen in certain segments of the curve. The retention interval between the acquisition and recall phase did not cause a regression to the prepractice performance level.

Learning the fine motor task of microvascular anastomosis adapts to the basic rules of learning such as the "power law of practice." Our results also support the improvement of performance during consecutive sessions of practice. The objective evidence provided may help in developing optimized learning protocols for microvascular anastomosis.