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| http://dx.doi.org/10.1038/s41433-024-03217-8 | DOI Listing |
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Ureteral access sheath or percutaneous nephrostomy during flexible ureteroscopy: which is better?
Urolithiasis
January 2025
Urology Department, Benha University, Benha, Qalubia, Egypt.
Studies in literature discussed the drawbacks of the ureteral access sheath use in flexible ureteroscopy and in the same time mentioned the benefits of ureteral access sheath in decreasing the incidence of urosepsis and better stone free rate. In the current study we aim to compare between percutaneous nephrostomy tube (PCN) insertion before flexible ureteroscopy and conventional ureteral access sheath (UAS) flexible ureteroscopy in terms of safety, efficacy and perioperative outcomes. In all, 100 Patients aged 20 to 67 years with upper ureteric stones and mild hydronephrosis or renal pelvic stones less than 20 mm with mild hydronephrosis were randomized into 2 groups; patients undergoing PCN insertion before flexible ureteroscopy, and patients undergoing the conventional UAS flexible ureteroscopy.
View Article and Find Full Text PDFComparison of Efficacy of Suprapatellar and Infrapatellar Approaches for Intramedullary Interlocking Nailing of Tibia in Patients With Tibial Fracture.
Cureus
November 2024
Information Technology, Mandayam Osuri Parthasarathi Vaishnav College for Women, Chennai, IND.
Introduction: Intramedullary interlocking nailing is a common surgical procedure for tibial fractures, enabling early patient mobilization. Traditionally, the infrapatellar approach has been used for intramedullary interlocking nailing of tibial fractures, but the suprapatellar approach is gaining attention for its potential benefits. This randomized controlled study aimed to compare the duration of the surgery, intra-operative blood loss, and fluoroscopy time between the suprapatellar and infrapatellar approaches.
View Article and Find Full Text PDFPredicting lack of clinical improvement following varicose vein ablation using machine learning.
J Vasc Surg Venous Lymphat Disord
December 2024
Department of Surgery, University of Toronto, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Canada; Institute of Medical Science, University of Toronto, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Canada; Department of Surgery, King Faisal Specialist Hospital and Research Center, Saudi Arabia. Electronic address:
Objective: Varicose vein ablation is generally indicated in patients with active/healed venous ulcers. However, patient selection for intervention in individuals without venous ulcers is less clear. Tools that predict lack of clinical improvement (LCI) following vein ablation may help guide clinical decision-making but remain limited.
View Article and Find Full Text PDFDevelopment and validation of artificial intelligence models for early detection of postoperative infections (PERISCOPE): a multicentre study using electronic health record data.
Lancet Reg Health Eur
February 2025
Intensive Care Unit, Leiden University Medical Centre, Leiden, the Netherlands.
Background: Postoperative infections significantly impact patient outcomes and costs, exacerbated by late diagnoses, yet early reliable predictors are scarce. Existing artificial intelligence (AI) models for postoperative infection prediction often lack external validation or perform poorly in local settings when validated. We aimed to develop locally valid models as part of the PERISCOPE AI system to enable early detection, safer discharge, and more timely treatment of patients.
View Article and Find Full Text PDFDevelopment and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.
Front Cardiovasc Med
December 2024
Chief of Cardiac Surgery, Peking Union Medical College Hospital, Beijing, China.
Introduction: Acute kidney injury (AKI) is notably prevalent after cardiac surgery for patients with active infective endocarditis. This study aims to create a machine learning model to predict AKI in this high-risk group, improving upon existing models by focusing specifically on endocarditis-related surgeries.
Methods: We analyzed medical records from 527 patients who underwent cardiac surgery for active infective endocarditis from January 2012 to December 2023.
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