Technical Skill Impacts the Success of Sequential Robotic Suturing Substeps

Robotic surgical performance, in particular suturing, has been associated with postoperative clinical outcomes. Suturing can be deconstructed into substep components (needle positioning, needle entry angle, needle driving, and needle withdrawal) allowing for the provision of more specific feedback while teaching suturing and more precision when evaluating suturing technical skill and prediction of clinical outcomes. This study evaluates if the technical skill required for particular substeps of the suturing process is associated with the execution of subsequent substeps in terms of technical skill, accuracy, and efficiency. Training and expert surgeons completed standardized sutures on the Mimic™ Flex virtual reality robotic simulator. Video recordings were deidentified, time annotated, and provided technical skill scores for each of the four suturing substeps. Hierarchical Poisson regression with generalized estimating equation was used to examine the association of technical skill rating categories between substeps. Twenty-two surgeons completed 428 suturing attempts with 1669 individual technical skill assessments made. Technical skill scores between substeps of the suturing process were found to be significantly associated. When needle positioning was ideal, needle entry angle was associated with a significantly greater chance of being ideal (risk ratio [RR] = 1.12, p = 0.05). In addition, ideal needle entry angle and needle driving technical skill scores were each significantly associated with ideal needle withdrawal technical skill scores (RR = 1.27, p = 0.03; RR = 1.3, p = 0.03, respectively). Our study determined that ideal technical skill was associated with increased accuracy and efficiency of select substeps. Our study found significant associations in the technical skill required for completing substeps of suturing, demonstrating inter-relationships within the suturing process. Together with the known association between technical skill and clinical outcomes, training surgeons should focus on mastering not just the overall suturing process, but also each substep involved. Future machine learning efforts can better evaluate suturing, knowing that these inter-relationships exist.