ASN's Mission

ASN leads the fight to prevent, treat, and cure kidney diseases throughout the world by educating health professionals and scientists, advancing research and innovation, communicating new knowledge, and advocating for the highest quality care for patients.

learn more

Contact ASN

1401 H St, NW, Ste 900, Washington, DC 20005

email@asn-online.org

202-640-4660

The Latest on Twitter

Kidney Week

Abstract: FR-PO645

A Simple Equation to Estimate Urinary Flow Rate Using Urine Creatinine

Session Information

Category: Fluid and Electrolytes

  • 902 Fluid and Electrolytes: Clinical

Authors

  • Webster, Luke, UCSD, San Diego, California, United States
  • Gassman, Jennifer J., Cleveland Clinic, Cleveland, Ohio, United States
  • Bullen, Alexander, UCSD, San Diego, California, United States
  • Weisbord, Steven D., University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States
  • Palevsky, Paul M., University of Pittsburgh/VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, United States
  • Fried, Linda F., VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, United States
  • Raphael, Kalani L., VA Salt Lake City Health Care System, Salt Lake City, Utah, United States
  • Isakova, Tamara, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States
  • Ix, Joachim H., UCSD, San Diego, California, United States
Background

Accurate assessment of urine flow is critical to clinical care, but challenging to estimate. We hypothesized we could dervie an equation that would accurately estimate urine flow rate.

Methods

We derived a new equation to estimate urine flow rate (eV) using the Cockcroft-Gault and the measured creatinine clearance (UV/P) equations. Accuracy was evaluated by comparing eV to measured urine flow rate (V) in persons with CKD who participated in the AASK and COMBINE trials. Participants with concordant estimated and measured creatinine excretion rates were included to define a subset with highly accurate 24 hour urine volumes.

Results

The eV equation required only urine creatinine, age, sex, and weight data. In AASK, we evaluated 570 participants who had mean GFR of 46.7 ± 15.1 ml/min/1.73m2 and measured urine flow rate (V) 94.9 ± 34.2 ml/hour over 24 hours. A high correlation was found between eV and V (r = 0.91, p< 0.001), however Bland Altman plots showed that eV was 9.6 ml/hour lower than V, on average, in AASK. Thus a correction factor was added to the eV equation and externally evaluated in COMBINE, wherein 123 participants had mean eGFR of 34 ± 8 ml/min/1.73m2. EV and V were highly correlated (r = 0.91, p< 0.001) and bias was improved (5.3 ml/hr). Overall, 80% of individuals had eV that was within 20% of V.

Conclusion

A simple equation using urine creatinine and demographics can accurately predict urine flow rate and may have clinical utility in situations where the accuracy of timed collections is uncertain.

Figure 1 shows the correlation of measured vs. estimated flow rate using eV equation (top of graph) in AASK participants who had measured creatinine excretion rates within 20% of estimated excretion rates (N=570). r = 0.91, p< 0.001.

Funding

  • NIDDK Support