Specific learning curve for port placement and docking of da Vinci® Surgical System: one surgeon’s experience in robotic-assisted radical prostatectomy

The Combination of Laparoscopic and Robotic Surgery: First Experience with the Dexter Robotic System™ in Visceral Surgery

INTRODUCTION

For over two decades, abdominal surgical procedures have been safely performed robotically. After the first patent expiration, alternative robotic systems entered the market. The Dexter Robotic System™ is a small-format, modular, and robotic platform consisting of a surgeon's console, two patient carts with instrument arms, and one endoscope arm. We report our initial experiences with Dexter since its installation at our visceral surgery department.

METHODS

The system and surgical setup are described. Demographic and perioperative data of all operated patients as well as the system docking times were analyzed.

RESULTS

From 56 procedures performed with Dexter, the most common ones included cholecystectomy (n = 15), inguinal hernia repair (TAPP; unilateral n = 15; bilateral n = 3), and right oncologic hemicolectomy (n = 15). The median docking time was 6 min (2-16 min) and was reduced to 4 min in the last tertile of procedures performed.

CONCLUSIONS

In our experience, Dexter can be implemented without any major challenges, and visceral surgical procedures of simple to medium complexity can be performed safely. The simplicity and accessibility of the system along with the ease of switching between robotics and laparoscopy could be particularly suitable for beginners in robotic surgery.

Robotic docking time with the Hugo™ RAS system in gynecologic surgery: a procedure independent learning curve using the cumulative summation analysis (CUSUM)

Robot-assisted surgery has been proven to offer improvements in term of surgical learning curve and feasibility of minimally invasive surgery, but has often been criticized for its longer operative times compared to conventional laparoscopy. Additional times can be split into time required for system set-up, robotic arms docking and calibration of robotic instruments; secondly, surgeon's learning curve. One of the newest systems recently launched on the market is the Hugo™ RAS (MEDTRONIC Inc, United States). As some of the earliest adopters of the Hugo™ RAS system technology, we present our data on robotic docking learning curve for the first 192 gynecologic robotic cases performed at our institution. Our data indicates that robotic set-up and docking with the new Hugo™ RAS robotic surgical system can be performed time-effectively and that the specific robotic docking learning curve is comparable to preexisting data for other platforms. This preliminary insights into this recently released system may be worthwhile for other centers which may soon adopt this new technology and may need some relevant information on topics such as OR times. Further studies are necessary to assess the different features of the Hugo™ RAS considering other technical and surgical aspects, to fully become familiar with this novel technology.

Learning curves in laparoscopic and robot-assisted prostate surgery: a systematic search and review

PURPOSE

To perform a systematic search and review of the available literature on the learning curves (LCs) in laparoscopic and robot-assisted prostate surgery.

METHODS

Medline was systematically searched from 1946 to January 2021 to detect all studies in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement, reporting on the LC in laparoscopic radical prostatectomy (LRP), laparoscopic simple prostatectomy (LSP), robot-assisted radical prostatectomy (RARP) and robot-assisted simple prostatectomy (RSP).

RESULTS

In total, 47 studies were included for qualitative synthesis evaluating a single technique (LRP, RARP, LSP, RSP; 45 studies) or two techniques (LRP and RARP; 2 studies). All studies evaluated outcomes on real patients. RARP was the most widely investigated technique (30 studies), followed by LRP (17 studies), LSP (1 study), and RSP (1 study). In LRP, the reported LC based on operative time; estimated blood loss; length of hospital stay; positive surgical margin; biochemical recurrence; overall complication rate; and urinary continence rate ranged 40-250, 80-250, 58-200, 50-350, 110-350, 55-250, 70-350 cases, respectively. In RARP, the corresponding ranges were 16-300, 20-300, 25-200, 50-400, 40-100, 20-250, 30-200, while LC for potency rates was 80-90 cases.

CONCLUSIONS

The definition of LC for laparoscopic and robot-assisted prostate surgery is not well defined with various metrics used among studies. Nevertheless, LCs appear to be steep and continuous. Implementation of training programs/standardization of the techniques is necessary to improve outcomes.

Can better surgical outcomes be obtained in the learning process of robotic rectal cancer surgery? A propensity score-matched comparison between learning phases

BACKGROUND

Although studies of robotic rectal cancer surgery have demonstrated the effects of learning on operation time, comparisons have failed to demonstrate differences in clinicopathological outcomes between unadjusted learning phases. This study aimed to investigate the learning curve of robotic rectal cancer surgery for clinicopathological outcomes and compare surgical outcomes between adjusted learning phases. Study design We enrolled 506 consecutive patients with rectal adenocarcinoma who underwent robotic resection by a single surgeon between 2007 and 2018. Risk-adjusted cumulative sum (RA-CUSUM) for surgical failure was used to analyze the learning curve. Surgical failure was defined as the occurrence of any of the following: conversion to open surgery, severe complications (Clavien-Dindo grade ≥ 3a), insufficient number of harvested lymph nodes (LNs), or R1 resection. Comparisons between learning phases analyzed by RA-CUSUM were performed before and after propensity score matching.

RESULTS

In RA-CUSUM analysis, the learning curve was divided into two learning phases: phase 1 (1st-177th cases, n = 177) and phase 2 (178th-506th cases, n = 329). Before matching, patients in phase 2 had deeper tumor invasion and higher rates of positive LNs on pretreatment images and preoperative chemoradiotherapy. After matching, phase 1 (n = 150) and phase 2 (n = 150) patients exhibited similar clinical characteristics. Phase 2 patients had lower rates of surgical failure overall and these components: conversion to open surgery, severe complications, and insufficient harvested LNs.

CONCLUSIONS

For robotic rectal cancer surgery, surgical outcomes improved after the 177th case. Further studies by other robotic surgeons are required to validate our results.