An initial experience with 85 consecutive robotic-assisted rectal dissections: improved operating times and lower costs with experience

Data are limited about the robotic platform in rectal dissections, and its use may be perceived as prohibitively expensive or difficult to learn. We report our experience with the initial robotic-assisted rectal dissections performed by a single surgeon, assessing learning curve and cost.

Following IRB approval, a retrospective chart review was conducted of the first 85 robotic-assisted rectal dissections performed by a single surgeon between 9/1/2010 and 12/31/2012. Patient demographic, clinicopathologic, procedure, and outcome data were gathered. Cost data were obtained from the University HealthSystem Consortium (UHC) database. The first 43 cases (Time 1) were compared to the next 42 cases (Time 2) using multivariate linear and logistic regression models.

Indications for surgery were cancer for 51 patients (60 %), inflammatory bowel disease for 18 (21 %), and rectal prolapse for 16 (19 %). The most common procedures were low anterior resection (n = 25, 29 %) and abdominoperineal resection (n = 21, 25 %). The patient body mass index (BMI) was statistically different between the two patient groups (Time 1, 26.1 kg/m² vs. Time 2, 29.4 kg/m², p = 0.02). Complication and conversion rates did not differ between the groups. Mean operating time was significantly shorter for Time 2 (267 min vs. 224 min, p = 0.049) and remained significant in multivariate analysis. Though not reaching statistical significance, the mean observed direct hospital cost decreased ($17,349 for Time 1 vs. $13,680 for Time 2, p = 0.2). The observed/expected cost ratio significantly decreased (1.47 for Time 1 vs. 1.05 for Time 2, p = 0.007) but did not remain statistically significant in multivariate analyses.

Over the series, we demonstrated a significant improvement in operating times. Though not statistically significant, direct hospital costs trended down over time. Studies of larger patient groups are needed to confirm these findings and to correlate them with procedure volume to better define the learning curve process.