@Article{cju.2026.081579,
AUTHOR = {Yuka Sugizaki, Takanobu Utsumi, Rino Ikeda, Naoki Ishitsuka, Yuta Suzuki, Takahide Noro, Shota Iijima, Takatoshi Somoto, Ryo Oka, Takumi Endo, Naoto Kamiya, Hiroyoshi Suzuki},
TITLE = {Urosepsis prediction in endoscopic stone surgery: from models to practice},
JOURNAL = {Canadian Journal of Urology},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CJU/online/detail/27277},
ISSN = {1488-5581},
ABSTRACT = {Sepsis remains the most serious infectious complication across the endoscopic stone surgery pathway, spanning the spectrum from infected obstructive presentation to the perioperative course of ureteroscopy. This structured narrative review synthesizes clinically actionable evidence across four recurrent decision points: initial presentation, pre-ureteroscopy optimization, intraoperative management, and early postoperative surveillance. The most consistently identified predictors of infectious deterioration include comorbidity burden, a positive preoperative urine culture, ureteral stent exposure and prolonged dwell time, hydronephrosis, stone burden, operative duration, and surrogate markers of intrarenal pressure. Scenario-specific prediction tools are available for emergency triage, post-decompression reassessment, and postoperative risk estimation; however, most have been developed retrospectively, employ heterogeneous endpoints, report calibration inconsistently, and lack robust external validation. Accordingly, evidence remains limited as to whether currently available models are sufficiently calibrated, safe, or effective for routine clinical implementation, and no prospective trials have demonstrated benefit in real-world practice. At present, these tools are best regarded as structured adjuncts to clinical judgment within safety-oriented perioperative pathways rather than as stand-alone determinants of management. Future work should prioritize harmonized outcome definitions, comprehensive calibration reporting, external validation, prospective impact assessment, and model updating as workflows, devices, and case mix continue to evolve.},
DOI = {10.32604/cju.2026.081579}
}