Luke Webster1, Brett Larive2, Jennifer Gassman2, Alexander Bullen1, Steven D Weisbord3,4, Paul M Palevsky3,4, Linda F Fried3,4, Kalani Raphael5,6, Tamara Isakova7, Joachim H Ix8,9,10. 1. Division of Nephrology-Hypertension, Department of Medicine, University of California San Diego, San Diego, California, USA. 2. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA. 3. Renal Section, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA. 4. Division of Nephrology, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. 5. Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, Utah, USA. 6. Division of Nephrology and Hypertension, University of Utah, Salt Lake City, Utah, USA. 7. Division 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. 8. Division of Nephrology-Hypertension, Department of Medicine, University of California San Diego, San Diego, California, USA, joeix@health.ucsd.edu. 9. Nephrology Section, Veterans Affairs San Diego Healthcare System, La Jolla, California, USA, joeix@health.ucsd.edu. 10. Division of Preventive Medicine, Department of Family Medicine and Public Health, University of California San Diego, San Diego, California, USA, joeix@health.ucsd.edu.
Abstract
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 = UCrV/PCr) 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.
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 = UCrV/PCr) 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.
Authors: Tamara Isakova; Joachim H Ix; Stuart M Sprague; Kalani L Raphael; Linda Fried; Jennifer J Gassman; Dominic Raj; Alfred K Cheung; John W Kusek; Michael F Flessner; Myles Wolf; Geoffrey A Block Journal: J Am Soc Nephrol Date: 2015-05-12 Impact factor: 10.121
Authors: Joachim H Ix; Tamara Isakova; Brett Larive; Kalani L Raphael; Dominic S Raj; Alfred K Cheung; Stuart M Sprague; Linda F Fried; Jennifer J Gassman; John P Middleton; Michael F Flessner; Geoffrey A Block; Myles Wolf Journal: J Am Soc Nephrol Date: 2019-05-13 Impact factor: 10.121
Authors: Joachim H Ix; Christina L Wassel; Lesley A Stevens; Gerald J Beck; Marc Froissart; Gerjan Navis; Roger Rodby; Vicente E Torres; Yaping Lucy Zhang; Tom Greene; Andrew S Levey Journal: Clin J Am Soc Nephrol Date: 2010-10-21 Impact factor: 8.237
Authors: Lawrence J Appel; John Middleton; Edgar R Miller; Michael Lipkowitz; Keith Norris; Lawrence Y Agodoa; George Bakris; Janice G Douglas; Jeanne Charleston; Jennifer Gassman; Tom Greene; Kenneth Jamerson; John W Kusek; Julia A Lewis; Robert A Phillips; Stephen G Rostand; Jackson T Wright Journal: J Am Soc Nephrol Date: 2003-07 Impact factor: 10.121