The Influence of Surgical Complexity and Center Experience on Postoperative Morbidity After Minimally Invasive Surgery in Gynecologic Oncology: Lessons Learned from the ROBOGYN-1004 Trial.

Conventional laparoscopy Gynecologic oncology Morbidity Prognostics factors Randomized phase III trial Robotic-assisted laparoscopy

Journal

Annals of surgical oncology
ISSN: 1534-4681
Titre abrégé: Ann Surg Oncol
Pays: United States
ID NLM: 9420840

Informations de publication

Date de publication:
15 Apr 2024
Historique:
received: 04 04 2023
accepted: 22 03 2024
medline: 15 4 2024
pubmed: 15 4 2024
entrez: 14 4 2024
Statut: aheadofprint

Résumé

This study was a secondary analysis of the ROBOGYN-1004 trial conducted between 2010 and 2015. The study aimed to identify factors that affect postoperative morbidity after either robot-assisted laparoscopy (RL) or conventional laparoscopy (CL) in gynecologic oncology. The study used two-level logistic regression analyses to evaluate the prognostic and predictive value of patient, surgery, and center characteristics in predicting severe postoperative morbidity 6 months after surgery. This analysis included 368 patients. Severe morbidity occurred in 49 (28 %) of 176 patients who underwent RL versus 41 (21 %) of 192 patients who underwent CL (p = 0.15). In the multivariate analysis, after adjustment for the treatment group (RL vs CL), the risk of severe morbidity increased significantly for patients who had poorer performance status, with an odds ratio (OR) of 1.62 for the 1-point difference in the WHO performance score (95 % CI 1.06-2.47; p = 0.027) and according to the type of surgery (p < 0.001). A focus on complex surgical acts showed significant more morbidity in the RL group than in the CL group at the less experienced centers (OR, 3.31; 95 % CI 1.0-11; p = 0.05) compared with no impact at the experienced centers (OR, 0.87; 95 % CI 0.38-1.99; p = 0.75). The findings suggest that the center's experience may have an impact on the risk of morbidity for patients undergoing complex robot-assisted surgical procedures.

Sections du résumé

BACKGROUND BACKGROUND
This study was a secondary analysis of the ROBOGYN-1004 trial conducted between 2010 and 2015. The study aimed to identify factors that affect postoperative morbidity after either robot-assisted laparoscopy (RL) or conventional laparoscopy (CL) in gynecologic oncology.
METHODS METHODS
The study used two-level logistic regression analyses to evaluate the prognostic and predictive value of patient, surgery, and center characteristics in predicting severe postoperative morbidity 6 months after surgery.
RESULTS RESULTS
This analysis included 368 patients. Severe morbidity occurred in 49 (28 %) of 176 patients who underwent RL versus 41 (21 %) of 192 patients who underwent CL (p = 0.15). In the multivariate analysis, after adjustment for the treatment group (RL vs CL), the risk of severe morbidity increased significantly for patients who had poorer performance status, with an odds ratio (OR) of 1.62 for the 1-point difference in the WHO performance score (95 % CI 1.06-2.47; p = 0.027) and according to the type of surgery (p < 0.001). A focus on complex surgical acts showed significant more morbidity in the RL group than in the CL group at the less experienced centers (OR, 3.31; 95 % CI 1.0-11; p = 0.05) compared with no impact at the experienced centers (OR, 0.87; 95 % CI 0.38-1.99; p = 0.75).
CONCLUSION CONCLUSIONS
The findings suggest that the center's experience may have an impact on the risk of morbidity for patients undergoing complex robot-assisted surgical procedures.

Identifiants

pubmed: 38616209
doi: 10.1245/s10434-024-15265-1
pii: 10.1245/s10434-024-15265-1
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Institut National Du Cancer
ID : INCa-PHRC-2010

Informations de copyright

© 2024. The Author(s).

Références

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Auteurs

Eric Lambaudie (E)

Paoli Calmettes Institute, Marseille, France.

Emilie Bogart (E)

Oscar Lambret Cancer Center, Lille, France.

Marie-Cécile Le Deley (MC)

Oscar Lambret Cancer Center, Lille, France.
Université Paris-Sud, UVSQ, CESP, INSERM, Université Paris-Saclay, Villejuif, France.

Houssein El Hajj (H)

Paoli Calmettes Institute, Marseille, France. Houssein-elhajj@outlook.com.
Oscar Lambret Cancer Center, Lille, France. Houssein-elhajj@outlook.com.

Tristan Gauthier (T)

University Hospital-Limoges, Limoges, France.

Thomas Hebert (T)

University Hospital-Tours, Tours, France.

Pierre Collinet (P)

University Hospital-Lille, Lille, France.

Jean Marc Classe (JM)

Institut de Cancérologie de l'Ouest-Nantes, Nantes, France.

Fabrice Lecuru (F)

Curie Institute, Paris, France.

Stephanie Motton (S)

University Hospital-Toulouse, Toulouse, France.

Vanessa Conri (V)

University Hospital-Bordeaux, Bordeaux, France.

Catherine Ferrer (C)

University Hospital-Nîmes, Nîmes, France.

Frederic Marchal (F)

CRAN, UMR 7039, CNRS Institut de Cancérologie de Lorraine Vandoeuvre les-Nancy, Université de Lorraine, Nancy, France.

Gwenael Ferron (G)

Institut Claudius Regaud Cancer Center-Toulouse, Toulouse, France.

Alicia Probst (A)

Oscar Lambret Cancer Center, Lille, France.

Camille Jauffret (C)

Paoli Calmettes Institute, Marseille, France.

Fabrice Narducci (F)

Oscar Lambret Cancer Center, Lille, France.

Classifications MeSH