Robotic Anatomical Segmentectomy: An Analysis of the Learning Curve.
Journal
The Annals of thoracic surgery
ISSN: 1552-6259
Titre abrégé: Ann Thorac Surg
Pays: Netherlands
ID NLM: 15030100R
Informations de publication
Date de publication:
05 2019
05 2019
Historique:
received:
30
06
2018
revised:
08
11
2018
accepted:
19
11
2018
pubmed:
24
12
2018
medline:
19
12
2019
entrez:
23
12
2018
Statut:
ppublish
Résumé
Robotic segmentectomy has been suggested as a safe and effective management for early lung cancer and benign lung diseases. However, no large case series have documented the learning curve for this technically demanding procedure. We conducted a retrospective study for robotic segmentectomy performed by the same surgeon between June 2015 and November 2017. The learning curve was initially analyzed using the cumulative sum (CUSUM) method to assess changes in the total operative times across the case sequence. Subsequently, an in-depth learning curve was generated using the risk-adjusted CUMSUM method, which considered perioperative risk factors and surgical failure. This study included 104 cases, and 87 were malignant. The median operative time was 145 minutes (interquartile range [IQR], 120 to 180) and the median blood loss was 100 mL (IQR, 50 to 100). The median length of stay was 4 days (IQR, 3 to 5). Based on the CUSUM and risk-adjusted CUSUM analyses, the learning curve could be divided into 3 different phases: phase I, the initial learning period (first to 21st operation); phase II, the consolidation period (22nd to 46th operation); and phase III, the experienced period (47th to 104th operation). The operative time and intraoperative blood loss tended to decrease after the initial learning phase. Other perioperative outcomes were not significantly different among the 3 phases. The learning curve of robotic segmentectomy consisted of 3 phases. The technical competency for assuring feasible perioperative outcomes was achieved in phase II at the 40th operation.
Sections du résumé
BACKGROUND
Robotic segmentectomy has been suggested as a safe and effective management for early lung cancer and benign lung diseases. However, no large case series have documented the learning curve for this technically demanding procedure.
METHODS
We conducted a retrospective study for robotic segmentectomy performed by the same surgeon between June 2015 and November 2017. The learning curve was initially analyzed using the cumulative sum (CUSUM) method to assess changes in the total operative times across the case sequence. Subsequently, an in-depth learning curve was generated using the risk-adjusted CUMSUM method, which considered perioperative risk factors and surgical failure.
RESULTS
This study included 104 cases, and 87 were malignant. The median operative time was 145 minutes (interquartile range [IQR], 120 to 180) and the median blood loss was 100 mL (IQR, 50 to 100). The median length of stay was 4 days (IQR, 3 to 5). Based on the CUSUM and risk-adjusted CUSUM analyses, the learning curve could be divided into 3 different phases: phase I, the initial learning period (first to 21st operation); phase II, the consolidation period (22nd to 46th operation); and phase III, the experienced period (47th to 104th operation). The operative time and intraoperative blood loss tended to decrease after the initial learning phase. Other perioperative outcomes were not significantly different among the 3 phases.
CONCLUSIONS
The learning curve of robotic segmentectomy consisted of 3 phases. The technical competency for assuring feasible perioperative outcomes was achieved in phase II at the 40th operation.
Identifiants
pubmed: 30578780
pii: S0003-4975(18)31841-1
doi: 10.1016/j.athoracsur.2018.11.041
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1515-1522Informations de copyright
Copyright © 2019 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.