James Fotheringham1, Michael J Campbell2, Damian G Fogarty3, Meguid El Nahas4, Timothy Ellam5. 1. Sheffield Kidney Institute, Sheffield, United Kingdom; School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom. 2. School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom. 3. Queen's University Belfast, Belfast, United Kingdom. 4. Sheffield Kidney Institute, Sheffield, United Kingdom; Global Kidney Academy, Sheffield, United Kingdom. 5. Sheffield Kidney Institute, Sheffield, United Kingdom; Department of Cardiovascular Science, University of Sheffield, Sheffield, United Kingdom. Electronic address: t.ellam@sheffield.ac.uk.
Abstract
BACKGROUND: Glomerular filtration rate estimation equations use demographic variables to account for predicted differences in creatinine generation rate. In contrast, assessment of albuminuria from urine albumin-creatinine ratio (ACR) does not account for these demographic variables, potentially distorting albuminuria prevalence estimates and clinical decision making. STUDY DESIGN: Polynomial regression was used to derive an age-, sex-, and race-based equation for estimation of urine creatinine excretion rate, suitable for use in automated estimated albumin excretion rate (eAER) reporting. SETTING & PARTICIPANTS: The MDRD (Modification of Diet in Renal Disease) Study cohort (N=1,693) was used for equation derivation. Validation populations were the CRIC (Chronic Renal Insufficiency Cohort; N=3,645) and the DCCT (Diabetes Control and Complications Trial; N=1,179). INDEX TEST: eAER, calculated by multiplying ACR by estimated creatinine excretion rate, and ACR. REFERENCE TEST: Measured albumin excretion rate (mAER) from timed 24-hour urine collection. RESULTS: eAER estimated mAER more accurately than ACR; the percentages of CRIC participants with eAER within 15% and 30% of mAER were 33% and 60%, respectively, versus 24% and 39% for ACR. Equivalent proportions in DCCT were 52% and 86% versus 15% and 38%. The median bias of ACR was -20.1% and -37.5% in CRIC and DCCT, respectively, whereas that of eAER was +3.8% and -9.7%. Performance of eAER also was more consistent across age and sex categories than ACR. LIMITATIONS: Single timed urine specimens used for mAER, ACR, and eAER. CONCLUSIONS: Automated eAER reporting potentially is a useful approach to improve the accuracy and consistency of clinical albuminuria assessment.
BACKGROUND: Glomerular filtration rate estimation equations use demographic variables to account for predicted differences in creatinine generation rate. In contrast, assessment of albuminuria from urine albumin-creatinine ratio (ACR) does not account for these demographic variables, potentially distorting albuminuria prevalence estimates and clinical decision making. STUDY DESIGN: Polynomial regression was used to derive an age-, sex-, and race-based equation for estimation of urine creatinine excretion rate, suitable for use in automated estimated albumin excretion rate (eAER) reporting. SETTING & PARTICIPANTS: The MDRD (Modification of Diet in Renal Disease) Study cohort (N=1,693) was used for equation derivation. Validation populations were the CRIC (Chronic Renal Insufficiency Cohort; N=3,645) and the DCCT (Diabetes Control and Complications Trial; N=1,179). INDEX TEST: eAER, calculated by multiplying ACR by estimated creatinine excretion rate, and ACR. REFERENCE TEST: Measured albumin excretion rate (mAER) from timed 24-hour urine collection. RESULTS: eAER estimated mAER more accurately than ACR; the percentages of CRIC participants with eAER within 15% and 30% of mAER were 33% and 60%, respectively, versus 24% and 39% for ACR. Equivalent proportions in DCCT were 52% and 86% versus 15% and 38%. The median bias of ACR was -20.1% and -37.5% in CRIC and DCCT, respectively, whereas that of eAER was +3.8% and -9.7%. Performance of eAER also was more consistent across age and sex categories than ACR. LIMITATIONS: Single timed urine specimens used for mAER, ACR, and eAER. CONCLUSIONS: Automated eAER reporting potentially is a useful approach to improve the accuracy and consistency of clinical albuminuria assessment.
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