Gynaecological robotic surgery at a state hospital - our own experience

In recent years, the rapid development of minimally invasive surgical methods, including robotic surgery, has resulted in a marked decline of the traditional methods in gynaecological surgery. The aim of the study was to share our experience with robotic surgery at a state hospital. A total of 40 pa...

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Published in:Ginekologia polska Vol. 89; no. 9; pp. 495 - 499
Main Authors: Kadioglu, Berrin Göktuğ, Kumtepe, Yakup, Baran, Firdevs Sekerci
Format: Journal Article
Language:English
Published: Poland Wydawnictwo Via Medica 01-01-2018
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Summary:In recent years, the rapid development of minimally invasive surgical methods, including robotic surgery, has resulted in a marked decline of the traditional methods in gynaecological surgery. The aim of the study was to share our experience with robotic surgery at a state hospital. A total of 40 patients, who underwent robotic gynaecological surgery (GS) between 2015 and 2017, were included. Age, BMI, previous abdominal operations (PAO), operation indications (OI), operative time (OT), pathological evaluation, uterine weight (UW), blood loss during surgery (BL), complications, and duration of the hospitalization (DoH) were analyzed. The Da Vinci XI was used during surgery. A total of 40 patients were analyzed. Mean values were as follows: age - 48 years, BMI - 28, and PAO - 12%. The most common OI included uterine fibroids (52%) and abnormal uterine bleeding (45%). Mean OT, docking time and console time values were 166 min, 15 min, and 123 min, respectively. Mean BL was 93 mL. Mean UW was 256 gr, and DoH was 4 days. Perioperative and postoperative complications were observed in 10% and 20% of the cases, respectively. Robotic-assisted surgery is invaluable in gynaecology, especially in the case of endometriosis, extensive adhesion, and in some oncological patients, as it allows for better visualization and higher maneuverability. In order for a surgeon to prepare for such cases, the use of the robot in benign cases is necessary to complete the learning curve and gain speed.
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ISSN:0017-0011
2543-6767
DOI:10.5603/GP.a2018.0084