Suturing maneuvers and technical insufficiencies in microvascular anastomosis dry laboratory training analyzed with the skill decomposition approach

Background: This study applied a skill decomposition approach to identify and quantify technical nuances of performing subtasks of a microvascular anastomosis by analyzing suturing maneuvers, the duration of each step, and technical insufficiencies in a controlled setting. Methods: Four microneurosurgery trainees performed 16 end-to-side microanastomoses, alternating between the interrupted technique (IT) and the continuous technique (CT) of suturing on standardized artificial vessels. At the subtask level, 24 reproducible suturing techniques and 42 technical insufficiencies were identified and analyzed. Results: Significant contributors to faster suture included biting both walls in a single move, dominant-hand suturing, and suturing directed toward the participant. Total anastomosis time correlated negatively with the sequential attempt order ( p < 0.05) and correlated positively with the total number of technical insufficiencies ( p < 0.05). The CT was 19% faster than the IT (mean [standard deviation (SD)], 15.7 [3.7] vs. 19.1 [4.7] min, p = 0.02). The IT was associated with 40% more technical insufficiencies per anastomosis than the CT (mean [SD], 37 [23.3] vs. 26.5 [12.2], p = 0.04). The CT throws were associated with insufficiencies related to controlling the suture thread, whereas the IT was associated with insufficiencies related to knot tying. Conclusion: By converting well-known microsurgical concepts into measurable performance subtasks, the skill decomposition approach offers a foundation for objective skill assessment and targeted training intervention. Identified maneuvers and insufficiencies that influenced the anastomosis subtasks form the foundation for evidence-based microsurgery training. A better understanding of the most effective microsurgical suturing techniques will contribute to their refinement, improving anastomosis quality and decreasing performance time.