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Robotic Assisted Transplant Nephrectomy: Case Series and Training Model for Improving Adoption.
- Source :
-
JSLS : Journal of the Society of Laparoendoscopic Surgeons [JSLS] 2023 Jan-Mar; Vol. 27 (1). - Publication Year :
- 2023
-
Abstract
- Introduction: Open transplant nephrectomy for failed renal allograft is an invasive procedure associated with significant perioperative morbidity and mortality. Minimally invasive surgical approaches have improved a variety of patient outcomes for many surgeries. Thus, robotic assisted transplant nephrectomy (RATN) potentially offers significant patient benefit. Although previously reported, there remains a paucity of data on RATN outcomes and techniques.<br />Methods: Four perfused, high-fidelity hydrogel models were created using previously described techniques and used for simulated RATN. Subsequently performed institutional cases were included for analysis. Intra- and postoperative variables along with patient demographics were retrospectively obtained through parsing of patient records.<br />Results: Simulated nephrectomy time was 67.33 minutes (35.75 - 98.91). Five patients underwent RATN. There were four male and one female patients. The average age was 47 years. The most common indication was abdominal pain secondary to rejection (3/5). Mean blood loss was 188 mL; mean operative time was 243 minutes, and mean length of stay was 4.5 days. Intraoperatively there were two incidences of small cystotomies. One patient was readmitted within 30 days for intraabdominal abscess.<br />Conclusion: This study adds to the growing literature around RATN, demonstrating the feasibility of the technique and reporting good outcomes for this cohort.<br />Competing Interests: Conflict of interests: none.<br /> (© 2023 by SLS, Society of Laparoscopic & Robotic Surgeons.)
Details
- Language :
- English
- ISSN :
- 1938-3797
- Volume :
- 27
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- JSLS : Journal of the Society of Laparoendoscopic Surgeons
- Publication Type :
- Report
- Accession number :
- 36818765
- Full Text :
- https://doi.org/10.4293/JSLS.2022.00079