Top

Published in:

01-03-2021 | Pancreatectomy | Original Scientific Report

Overcoming the Arduous Transition for Robotic Hepatopancreatobiliary Cases: A Multi-Procedure Learning Curve Study Utilizing CUSUM Analysis

Authors: Joseph S. Lim, Terence Jackson, James Kurtz, Edward E. Cho, Shyam Vedantam, Kei Nagatomo, Houssam Osman, Dhiresh Rohan Jeyarajah

Published in: World Journal of Surgery | Issue 3/2021

Abstract

Background/objective

Quick optimization and mastery of a new technique is an important part of procedural medicine, especially in the field of minimally invasive surgery. Complex surgeries such as robotic pancreaticoduodenectomies (RPD) and robotic distal pancreatectomies (RDP) have a steep learning curve; therefore, findings that can help expedite the burdensome learning process are extremely beneficial. This single-surgeon study aims to report the learning curves of RDP, RPD, and robotic Heller myotomy (RHM) and to review the results’ implications for the current state of robotic hepatopancreaticobiliary (HPB) surgery.

Study design

This is a retrospective case series of a prospectively maintained database at a non-university tertiary care center. Total of 175 patients underwent either RDP, RPD, or RHM with the surgeon (DRJ) from January 2014 to January 2020.

Results

Statistical significance of operating room time (ORT) was noted after 47 cases for RDP (p < 0.05), 51 cases for RPD (p < 0.0001), and 18 cases for RHM (p < 0.05). Mean ORT after the statistical mastery of the procedure for RDP, RPD, and RHM was 124, 232, 93 min, respectively. No statistical significance was noted for estimated blood loss or length of stay.

Conclusions

Robotic HPB procedures have significantly higher learning curves compared to non-HPB procedures, even for an experienced HPB surgeon with extensive laparoscopic experience. Our RPD curve, however, is quicker than the literature average. We suggest that this is because of the simultaneous implementation of HPB (RDP and RPD) and non-HPB robotic surgeries with a shorter learning curve—especially foregut procedures such as RHM—into an experienced surgeon’s practice. This may accelerate the learning process without compromising patient safety and outcomes.

Strijker MCV, Santvoort HG, Besselink MV et al (2013) Robot-assisted pancreatic surgery: a systematic review of the literature. Hpb 15(1):1–10. https://doi.org/10.1111/j.1477-2574.2012.00589.xCrossRefPubMed

Giulianotti PC, Mangano A, Bustos RE et al (2018) Operative technique in robotic pancreaticoduodenectomy (RPD) at University of Illinois at Chicago (UIC): 17 steps standardized technique. Surg Endosc 32:4329–4336. https://doi.org/10.1007/s00464-018-6228-7CrossRefPubMedPubMedCentral

Orti-Rodríguez RJ, Rahman SH (2014) A comparative review between laparoscopic and robotic pancreaticoduodenectomies. Surg Laparosc, Endosc and Percutaneous Tech 24(2):103–108. https://doi.org/10.1097/sle.0b013e3182a2f0adCrossRef

Subramonian K, Muir G (2014) The learning curve in surgery: what is it, how do we measure it and can we influence it? BJU Int 93(9):1173–1174. https://doi.org/10.1111/j.1464-410x.2004.04891.xCrossRef

Lim TO, Soraya A, Ding LM et al (2002) Assessing doctors’ competence: application of CUSUM technique in monitoring doctors’ performance. Int J Qual Health Care 14(3):251–258. https://doi.org/10.1093/oxfordjournals.intqhc.a002616CrossRefPubMed

Wohl H (1977) The cusum plot: its utility in the analysis of clinical data. N Engl J Med 296(18):1044–1045. https://doi.org/10.1056/nejm197705052961806CrossRefPubMed

Cho E, Pagkratis S, Osman H et al (2020) Robotic pancreatoduodenectomy. minimally invasive surgical techniques for cancers of the gastrointestinal tract. A Step-by-Step Approach Chapter 13:p123-132. https://doi.org/10.1007/978-3-030-18740-8_13CrossRef

Shakir M, Boone BA, Polanco PM et al (2015) The learning curve for robotic distal pancreatectomy: an analysis of outcomes of the first 100 consecutive cases at a high-volume pancreatic centre. Hpb 17(7):580–586. https://doi.org/10.1111/hpb.12412CrossRefPubMedPubMedCentral

Yamaguchi T, Kinugasa Y, Shiomi A et al (2014) Learning curve for robotic-assisted surgery for rectal cancer: use of the cumulative sum method. Surg Endosc 29(7):1679–1685. https://doi.org/10.1007/s00464-014-3855-5CrossRefPubMed

10.

Napoli N, Kauffmann EF, Perrone VG et al (2015) The learning curve in robotic distal pancreatectomy. Updates Surg 67:257–264. https://doi.org/10.1007/s13304-015-0299-yCrossRefPubMed

11.

Boone BA, Zenati M, Hogg ME et al (2015) Assessment of quality outcomes for robotic pancreaticoduodenectomy: identification of the learning curve. JAMA Surg 150(5):416–422. https://doi.org/10.1001/jamasurg.2015.17CrossRefPubMed

12.

Shi Y, Jin J, Qiu W et al (2020) Short-term outcomes after robot-assisted vs open pancreaticoduodenectomy after the learning curve. JAMA Surg 155(5):372–455. https://doi.org/10.1001/jamasurg.2020.0021CrossRef

13.

Shyr B-U, Chen S-C, Shyr Y-M et al (2018) Learning curves for robotic pancreatic surgery-from distal pancreatectomy to pancreaticoduodenectomy. Medicine 97(45):e13000. https://doi.org/10.1097/md.0000000000013000CrossRefPubMedPubMedCentral

14.

Enayati N, Momi ED, Ferrigno G (2016) Haptics in robot-assisted surgery: challenges and benefits. IEEE Rev Biomed Eng 9:49–65. https://doi.org/10.1109/rbme.2016.2538080CrossRefPubMed

15.

Ross SB, Kurian TJ, Luberice K et al (2011) Defining the learning curve of laparo-endoscopic single site (less) heller myotomy. Gastroenterology 140(5):72–77. https://doi.org/10.1016/s0016-5085(11)64295-9CrossRef

16.

Yano F, Omura N, Tsuboi K et al (2017) Learning curve for laparoscopic Heller myotomy and Dor fundoplication for achalasia. Plos One 12(7):e0180515. https://doi.org/10.1371/journal.pone.0180515CrossRefPubMedPubMedCentral

17.

Jackson T, Cho EE, Nagatomo K et al (2020) Teacher and trainee learning together-dual console and the 3 arms. J Surg Edu S1931–7204(20):30013–30021. https://doi.org/10.1016/j.jsurg.2020.01.013CrossRef

18.

van der Schans EM, Hiep MAJ, Consten ECJ et al (2020) From Da Vinci Si to Da Vinci Xi: realistic times in draping and docking the robot. J Robotic Surg. https://doi.org/10.1007/s11701-020-0CrossRef

Title: Overcoming the Arduous Transition for Robotic Hepatopancreatobiliary Cases: A Multi-Procedure Learning Curve Study Utilizing CUSUM Analysis
Authors: Joseph S. Lim
Terence Jackson
James Kurtz
Edward E. Cho
Shyam Vedantam
Kei Nagatomo
Houssam Osman
Dhiresh Rohan Jeyarajah
Publication date: 01-03-2021
Publisher: Springer International Publishing
Keyword: Pancreatectomy
Published in: World Journal of Surgery / Issue 3/2021
Print ISSN: 0364-2313
Electronic ISSN: 1432-2323
DOI: https://doi.org/10.1007/s00268-020-05861-z

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Overcoming the Arduous Transition for Robotic Hepatopancreatobiliary Cases: A Multi-Procedure Learning Curve Study Utilizing CUSUM Analysis

Abstract

Background/objective

Study design

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background/objective

Study design

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 3/2021

We Need More Data: Choosing the Optimal Treatment Strategy for Patients with Resectable Metachronous Resectable Colorectal Liver Metastases

Peri-operative Mortality Following Cholecystectomy in Australia: Potential Preventability of Adverse Events

Description of the Vascular Anatomy of Livers with Absence of the Portal Bifurcation

Perceptual Assessment and Acoustic Voice Analysis as Screening Tests for Vocal Fold Paresis After Thyroid or Parathyroid Surgery

Clinical and Organizational Impact of the AIRSEAL® Insufflation System During Laparoscopic Surgery: A Systematic Review

Video-Assisted Thoracoscopic Sleeve Lobectomy for Centrally Located Non-small Cell Lung Cancer: A Meta-analysis