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      A Simple Equation to Estimate Urinary Flow Rate Using Urine Creatinine

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          Background: Accurate assessment of urine flow remains challenging in both inpatient and outpatient settings. We hypothesized we could derive an equation that would accurately estimate urine flow rate (eV) through derivation from other existing equations commonly used in nephrology clinical practice. Methods: The eV equation was derived using the Cockcroft-Gault and the measured creatinine clearance (CrCl = U<sub>Cr</sub>V/P<sub>Cr</sub>) equations. Within the African American Study of Kidney Disease and Hypertension (AASK; n = 570) and COMBINE ( n = 133) clinical trials, we identified participants with concordant estimated and measured creatinine excretion rates to define a subset with highly accurate 24-h urine collections, to assure a reliable gold standard. We then compared eV to measured 24-h urine flow rates in these trials. Results: In AASK, we found a high correlation between eV and measured urine flow rate (V; r = 0.91, p < 0.001); however, Bland-Altman plots showed that eV was 9.5 mL/h lower than V, on average. Thus, we added a correction factor to the eV equation and externally validated the new equation in COMBINE. eV and V were again highly correlated ( r = 0.91, p < 0.001), and bias was improved (mean difference 5.3 mL/h). Overall, 80% of individuals had eV that was within 20% of V. Conclusions: A simple equation using urine creatinine, demographics, and body weight can accurately predict urine flow rate and may have clinical utility in situations where it is difficult to accurately measure the urine flow rate.

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          Author and article information

          Am J Nephrol
          American Journal of Nephrology
          S. Karger AG
          May 2020
          09 March 2020
          : 51
          : 5
          : 395-400
          aDivision of Nephrology-Hypertension, Department of Medicine, University of California San Diego, San Diego, California, USA
          bDepartment of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA
          cRenal Section, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA
          dDivision of Nephrology, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
          eVeterans Affairs Salt Lake City Healthcare System, Salt Lake City, Utah, USA
          fDivision of Nephrology and Hypertension, University of Utah, Salt Lake City, Utah, USA
          gDivision of Nephrology and Hypertension, Department of Medicine and Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
          hNephrology Section, Veterans Affairs San Diego Healthcare System, La Jolla, California, USA
          iDivision of Preventive Medicine, Department of Family Medicine and Public Health, University of California San Diego, San Diego, California, USA
          Author notes
          *Joachim H. Ix, MD, MAS, Division of Nephrology-Hypertension, University of California San Diego, 3350 La Jolla Village Drive, Mail Code 9111-H, San Diego, CA 92161 (USA),
          506728 Am J Nephrol 2020;51:395–400
          © 2020 S. Karger AG, Basel

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          Page count
          Figures: 5, Tables: 1, Pages: 6
          Patient-Oriented, Translational Research: Research Article

          Cardiovascular Medicine, Nephrology

          Kidney disease, Prediction, Creatinine, Urine volume


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