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著者: Robert G Weaver, Matthew T James, Pietro Ravani, Colin G W Weaver, Edmund J Lamb, Marcello Tonelli, Braden J Manns, Robert R Quinn, Min Jun, Brenda R Hemmelgarn
雑誌名: J Am Soc Nephrol. 2020 Mar;31(3):591-601. doi: 10.1681/ASN.2019060605. Epub 2020 Feb 5.
Abstract/Text
BACKGROUND: Urine albumin-to-creatinine ratio (ACR) and protein-to-creatinine ratio (PCR) are used to measure urine protein. Recent guidelines endorse ACR use, and equations have been developed incorporating ACR to predict risk of kidney failure. For situations in which PCR only is available, having a method to estimate ACR from PCR as accurately as possible would be useful.
METHODS: We used data from a population-based cohort of 47,714 adults in Alberta, Canada, who had simultaneous assessments of urine ACR and PCR. After log-transforming ACR and PCR, we used cubic splines and quantile regression to estimate the median ACR from a PCR, allowing for modification by specified covariates. On the basis of the cubic splines, we created models using linear splines to develop equations to estimate ACR from PCR. In a subcohort with eGFR<60 ml/min per 1.73 m2, we then used the kidney failure risk equation to compare kidney failure risk using measured ACR as well as estimated ACR that had been derived from PCR.
RESULTS: We found a nonlinear association between log(ACR) and log(PCR), with the implied albumin-to-protein ratio increasing from <30% in normal to mild proteinuria to about 70% in severe proteinuria, and with wider prediction intervals at lower levels. Sex was the most important modifier of the relationship between ACR and PCR, with men generally having a higher albumin-to-protein ratio. Estimates of kidney failure risk were similar using measured ACR and ACR estimated from PCR.
CONCLUSIONS: We developed equations to estimate the median ACR from a PCR, optionally including specified covariates. These equations may prove useful in certain retrospective clinical or research applications where only PCR is available.
Copyright © 2020 by the American Society of Nephrology.
PMID 32024663 J Am Soc Nephrol. 2020 Mar;31(3):591-601. doi: 10.1681/ASN.2019060605. Epub 2020 Feb 5.
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