Abstract

Surgeons anecdotally report awareness of nontactile sensory cues that compensate for absent haptic feedback in robot-assisted surgery. This study investigates this poorly understood adaptive process by evaluating frequency of in vivo suture damage. Consecutive cases of children undergoing robot-assisted dismembered pyeloplasty were examined. Suture damage was defined as incomplete (i.e., fraying) or complete (i.e., broken) loss of thread integrity and prospectively recorded with clinical data. Suture technique, size, and robotic instruments used for suturing were subjected to post hoc analysis. Statistical analysis was undertaken using appropriate nonparametric tests. Overall frequency of suture damage was 2.6% among 1135 sutures used in 52 patients. The mean number of sutures used for cases in this series was 22 (standard deviation±6). There was a significant inverse trend between surgeon experience and suture damage frequency (P=0.014), implying that greater surgeon experience was associated with less suture damage. The impact of experience on suture damage was most apparent when comparing the earliest quartile subgroup (Q1) with the later three quartile subgroups (Q2-Q4) (P<0.001). Plateau of suture damage frequency was seen after approximately 28 cases. Continuous sutures had significantly higher damage frequency compared with interrupted sutures (P=0.022). Significantly higher frequency of suture damage was seen with cases in which forceps instruments were used for suturing compared with paired needle drivers (1.4% vs 7.1%, P<0.001). All events of inadvertent tissue injury involved damage to exposed edges of the renal pelvis (n=5). Suture damage is likely to be encountered during the learning curve of robot-assisted surgery but decreases with surgeon experience. Preferential use of larger suture size, interrupted sutures, and paired needle driver instruments may help to minimize suture damage. Experience-related perceptual skills that compensate for haptic loss are likely to be acquirable in a preclinical simulation environment.

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