Investigative and Clinical Urology (Mar 2019)

Quick Sequential (Sepsis Related) Organ Failure Assessment: A high performance rapid prognostication tool in patients having acute pyelonephritis with upper urinary tract calculi

  • Siddharth Pandey,
  • Satya Narayan Sankhwar,
  • Apul Goel,
  • Manoj Kumar,
  • Ajay Aggarwal,
  • Deepanshu Sharma,
  • Samarth Agarwal,
  • Tushar Pandey

DOI
https://doi.org/10.4111/icu.2019.60.2.120
Journal volume & issue
Vol. 60, no. 2
pp. 120 – 126

Abstract

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Purpose: To analyze the utility of quick Sequential Organ Failure Assessment (qSOFA) in patients with uro-sepsis due to acute pyelonephritis (APN) with upper urinary tract calculi, we conducted this study. The role of qSOFA as a tool for rapid prognostication in patients with sepsis is emerging. But there has been a great debate on its utility. Literature regarding utility of qSOFA in uro-sepsis is scarce. Materials and Methods: Ours was a retrospective study including 162 consecutive patients who were admitted for APN with upper urinary tract calculi over a 3 and half years (total 42 months) period. We evaluated the accuracy of qSOFA in predicting inhospital mortality and intensive care unit (ICU) admissions and compared this with the predictive accuracy of systemic inflammatory response syndrome (SIRS). We used the Area Under Curve (AUC) of the Receiver Operator Characteristic curve to calculate it and also calculated the optimum cut off for qSOFA score. Results: The overall mortality and ICU admission rates were 7.4% and 12.9%, respectively. qSOFA had a higher predictive accuracy for in-hospital mortality (AUC, 0.981; 95% confidence interval [CI], 0.962–1.000) and ICU admissions (AUC, 0.977; 95% CI, 0.955–0.999) than SIRS. A qSOFA score of ≥2 was an optimum cut off for predicting prognosis. In a multivariate model qSOFA ≥2 was a highly significant predictor of in-hospital mortality and ICU admissions (p<0.001). Conclusions: qSOFA is a reliable and rapid bedside tool in patients with sepsis with accuracy more than SIRS in predicting inhospital mortality and ICU admissions.

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