Introduction: The aim of this study was to compare the outcomes of on-clamp and off-clamp robotic partial nephrectomy (RPN).
Materials And Methods: The charts of all patients who underwent an RPN at 8 institutions between 2010 and 2014 were retrospectively reviewed. The patients who underwent an off-clamp RPN were matched to on-clamp RPN in a 1-4 fashion according to the following variables: RENAL score, tumor size and surgeon's experience. Pre-, intra-, and postoperative data were compared between both groups.
Results: Among 525 RPN, 26 were performed off-clamp (5%). They were matched to 104 on-clamp RPN. The complications rate (15.5 vs. 7.7%, p = 0.53), major complications rate (4.9 vs. 3.9%; p = 0.82), and transfusions rate (0 vs. 4.9%; p = 0.58) did not differ significantly between the clamped and unclamped groups. Conversely, estimated blood loss was higher in the off-clamp group (266.4 vs. 284.6 mL, p = 0.048) and so was the rate of conversion to radical nephrectomy (0 vs. 7.7%, p = 0.04). Postoperative preservation of renal function was comparable in both groups.
Conclusion: Off-clamp RPN is feasible for a small subgroup of renal tumors without increased risk of postoperative complications but at the cost of higher estimated blood loss and increased risk of conversion to radical nephrectomy.
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http://dx.doi.org/10.1159/000471772 | DOI Listing |
Urology
January 2025
Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH. Electronic address:
Objectives: To develop a predictive tool to assist in predicting the risk of Acute Kidney Injury (AKI) following robot-assisted partial nephrectomy (RAPN).
Methods: A retrospective review was performed on the prospectively maintained, IRB-approved database to identify all consecutive patients who underwent RAPN between 2008 and 2023. Patients with end-stage kidney disease (ESKD), horseshoe kidneys, solitary kidneys, and previous renal transplant recipients were excluded.
J Minim Access Surg
January 2025
Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
The synchronous occurrence of pancreatic neuroendocrine neoplasm (PNEN) and clear cell renal cell carcinoma (ccRCC) in one patient is extremely rare. Synchronous resection of both tumours is preferred over a two-stage procedure if possible. The robotic da Vinci Xi platform allows for multi-quadrant surgery with oncological outcomes comparable to those of laparoscopic or open surgery.
View Article and Find Full Text PDFGeriatr Nurs
January 2025
School of Nursing, Fudan University, Shanghai 200032, China. Electronic address:
Objective: To explore the network structure of common geriatric syndromes and conditions in physically disabled older adults.
Methods: We chose fourteen common geriatric syndromes and conditions from the dataset and estimated networks with the partial correlation network method. We tested the stability and accuracy of the network using the package "bootnet" in R software.
Sensors (Basel)
January 2025
Space Robotics Research Group (SpaceR), Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg, Luxembourg.
Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification.
View Article and Find Full Text PDFFront Robot AI
January 2025
Department of Materials and Production, Aalborg University, Aalborg, Denmark.
Object pose estimation is essential for computer vision applications such as quality inspection, robotic bin picking, and warehouse logistics. However, this task often requires expensive equipment such as 3D cameras or Lidar sensors, as well as significant computational resources. Many state-of-the-art methods for 6D pose estimation depend on deep neural networks, which are computationally demanding and require GPUs for real-time performance.
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