Establishing validity evidence for device-assisted advanced laparoscopic suturing tasks using simulation

Background: Laparoscopic suturing (LS) is an essential technique required for a wide range of procedures, and it is one of the most challenging for surgical trainees to master. We designed and collected validity evidence for advanced LS tasks using an automated suturing device and evaluated the perceived educational value of the tasks. Methods: This project was a multicentre prospective study involving McGill University, University of Toronto (UofT), and Louisiana State University (LSU) Health New Orleans. Novice (NS) and experienced (ES) surgeons performed suturing under tension (UT) and continuous suturing (CS) tasks. ES performed the tasks twice to establish proficiency benchmarks, and they were interviewed to develop formative feedback tools (FFT). Participants were assessed on completion time, error, Global Operative Assessment of Laparoscopic Skills (GOALS), and FFT. Data were analyzed using descriptive and inferential statistical methods. Results: Twenty-seven participants (13 ES, 14 NS, median age 34 years; 85% male) completed the study. Eight were attending surgeons, 7 fellows, 4 PGY5, 5 PGY4, and 3 PGY3 (18 from McGill, 5 UofT, and 4 LSU). Comparing ES and NS, for UT task, ES had significantly greater task scores (570 [563–648] vs 323 [130–464], p value 0.00036) and GOALS scores (14 [13–16] vs 10 [8–12], p value 0.0038). Similarly, for CS, ES had significantly greater task scores (976 [959–1010] vs 785 [626–856], p value 0.00009) and GOALS scores (16 [12–17] vs 12.5 [8.25–15], p value 0.028). After FFTs were developed, comparing ES and NS, for both UT and CS tasks, ES had significantly greater FFT scores (UT 25 [24–26] vs 17 [14–20], p value 0.0016 and CS 30 [27–32] vs 22[17.2–25.8], p value 0.00061). Conclusion: In conclusion, preliminary validity evidence was provided for the tasks. Once further validity evidence is established, incorporating the tasks into the training curricula could improve trainee skills and help to meet the need for better advanced suturing models.