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Tundra lists 3 Urosepsis clinical trials. Each listing includes eligibility criteria, study locations, and direct links to research sites in the Tundra directory.
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NCT07826325
Effect of Emergency Urinary Drainage on Subsequent PCNL Outcomes in Obstructive Urosepsis
Obstructive urolithiasis complicated by urosepsis is a life-threatening medical emergency requiring urgent urinary tract decompression. Emergency drainage is typically achieved by either retrograde ureteral stent (RUS) insertion or percutaneous nephrostomy (PCN). Following clinical stabilization and resolution of infection, percutaneous nephrolithotomy (PCNL) is commonly performed as definitive stone treatment. This prospective comparative study aims to evaluate and compare the outcomes of subsequent PCNL between patients who underwent emergency drainage via retrograde ureteral stent and those who underwent percutaneous nephrostomy. The main outcomes to be compared include stone-free rates, operative time, blood loss, postoperative complications, length of hospital stay, and the need for auxiliary procedures.
Gender: All
Ages: 18 Years - Any
Updated: 2026-09-17
1 state
NCT04669886
Serum Endotoxin Assay to Predict the Development of Postoperative Infectious Complications and Systemic Inflammatory Response Following Percutaneous Nephrolithotomy.
The aim of the study is to establish an infectious risk stratification system based on pre-and post-operative blood endotoxin profile.
Gender: All
Ages: 18 Years - Any
Updated: 2026-08-27
1 state
NCT06412900
Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence
Kidney stone disease causes significant morbidity, and stones obstructing the ureter can have serious consequences. Imaging diagnostics with computed tomography (CT) are crucial for diagnosis, treatment selection, and follow-up. Segmentation of CT images can provide objective data on stone burden and signs of obstruction. Artificial intelligence (AI) can automate such segmentation but can also be used for the diagnosis of stone disease and obstruction. In this project, the aim is to investigate if: Manual segmentation of CT scans can provide more accurate information about kidney stone disease compared to conventional interpretation. AI segmentation yields valid results compared to manual segmentation. AI can detect ureteral stones and obstruction or predict spontaneous passage.
Gender: All
Ages: 18 Years - Any
Updated: 2025-08-11