Sheffield Kidney Institute, Sheffield, United Kingdom; School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom.
School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom.
Am J Kidney Dis. 2014 Mar;63(3):405-14. doi: 10.1053/j.ajkd.2013.08.009. Epub 2013 Sep 29.
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.
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).
eAER, calculated by multiplying ACR by estimated creatinine excretion rate, and ACR.
Measured albumin excretion rate (mAER) from timed 24-hour urine collection.
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.
Single timed urine specimens used for mAER, ACR, and eAER.
Automated eAER reporting potentially is a useful approach to improve the accuracy and consistency of clinical albuminuria assessment.
肾小球滤过率估算方程使用人口统计学变量来解释肌酐生成率的预测差异。相比之下,尿白蛋白与肌酐比值(ACR)评估白蛋白尿时并不考虑这些人口统计学变量,这可能会扭曲白蛋白尿的患病率估计值并影响临床决策。
本研究采用多项式回归方法,推导出了一个基于年龄、性别和种族的尿肌酐排泄率估算方程,适用于自动估算白蛋白排泄率(eAER)报告。
本研究使用 MDRD(肾脏病饮食改良研究)研究队列(N=1693)进行方程推导。验证人群为 CRIC(慢性肾脏不全队列研究;N=3645)和 DCCT(糖尿病控制与并发症试验;N=1179)。
eAER 通过将 ACR 乘以估算的肌酐排泄率计算得出,同时检测 ACR。
通过 24 小时尿液收集测量得到的白蛋白排泄率(mEAR)。
eAER 比 ACR 更准确地估算 mAER;CRIC 参与者中 eAER 与 mAER 相差 15%和 30%的比例分别为 33%和 60%,而 ACR 为 24%和 39%。DCCT 中的相应比例为 52%和 86%,而 ACR 为 15%和 38%。在 CRIC 和 DCCT 中,ACR 的中位数偏差分别为-20.1%和-37.5%,而 eAER 的中位数偏差为+3.8%和-9.7%。与 ACR 相比,eAER 的性能在年龄和性别类别之间也更加一致。
仅使用单次 24 小时尿液样本进行 mAER、ACR 和 eAER 的检测。
自动 eAER 报告可能是一种改善临床白蛋白尿评估准确性和一致性的有用方法。