Minimally invasive sacrocolpopexy: efficiency of robotic assistance compared to standard laparoscopy

Abstract

Minimally invasive abdominal sacrocolpopexy (SC) is the treatment of choice for symptomatic, high-grade, apical or multi-compartmental pelvic organ prolapse (POP), in terms of anatomical correction and treatment durability. Robot-assisted sacrocolpopexy (RASC) could be an attractive alternative to the gold standard laparoscopic sacrocolpopexy (LSC), for its ergonomic advantages in such a technically demanding procedure. However, it has not yet proven its superiority, consequently raising cost-effectiveness issues. Our primary objective was to assess if RASC can achieve better overall operative time (OOT) over LSC, with at least equivalent perioperative results. This was a single-center retrospective study including 100 patients (58 consecutive RASC cases and 42 LSC within the same time-period), with primary endpoint the OOT in both groups. Secondary results included complication rate, hospital stay, short-term anatomic results and OOT within and beyond the RASC learning curve. A multivariate linear regression was carried out for our primary outcome. The groups had comparable characteristics, except for BMI, which was lower in RASC group. The mean OOT was significantly lower in the RASC group (188 vs. 217 min, p ≤ 0.01), even after adjusting for possible confounders. Short-term anatomic results, complication rate, and blood loss were similar in the two groups. Mean hospital stay was significantly longer in the RASC group. Average RASC OOT was significantly shorter after the first 20 cases realized. This study demonstrated a significant reduction of OOT for RASC compared to LSC, with similar perioperative results, encouraging further use of the robotic technology for this indication.

Keywords: Da Vinci; Overall operative time; Robotic assistance; Sacrocolpopexy.

Fig. 1

Box and whisker plot representing OOT difference between the two study arms as well as in different subgroups of interest

Fig. 2

Forest plot based on the results of multivariate analysis of the factors associated with OOT differences

Fig. 3

Forest plot of multivariate analysis of OOT for possible confounding factors, RASC group

Fig. 4

Box and whisker plot showing longer OOT within the learning curve first 20 RASC cases (a), b scatter plot representing this relationship, c OOT distribution for RASC cases

Fig. 5

Forest plot of multivariate analysis of OOT difference within the learning curve, RASC group