透析患者残余肾功能估计方程的开发与验证
Development and Validation of Residual Kidney Function Estimating Equations in Dialysis Patients.
作者信息
Steubl Dominik, Fan Li, Michels Wieneke M, Inker Lesley A, Tighiouart Hocine, Dekker Friedo W, Krediet Raymond T, Simon Andrew L, Foster Meredith C, Karger Amy B, Eckfeldt John H, Li Hongyan, Tang Jiamin, He Yongcheng, Xie Minyan, Xiong Fei, Li Hongbo, Zhang Hao, Hu Jing, Liao Yunhua, Ye Xudong, Shafi Tariq, Chen Wei, Yu Xueqing, Levey Andrew S
机构信息
Division of Nephrology, Tufts Medical Center, Boston, MA.
Abteilung für Nephrologie, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
出版信息
Kidney Med. 2019 May 11;1(3):104-114. doi: 10.1016/j.xkme.2019.04.002. eCollection 2019 May-Jun.
RATIONALE & OBJECTIVE: Measurement of residual kidney function is recommended for the adjustment of the dialysis prescription, but timed urine collections are difficult and prone to errors. Equations to calculate residual kidney function from serum concentrations of endogenous filtration markers and demographic parameters would simplify monitoring of residual kidney function. However, few equations to estimate residual kidney function using serum concentrations of small solutes and low-molecular-weight proteins have been developed and externally validated.
STUDY DESIGN
Study of diagnostic test accuracy.
SETTING & PARTICIPANTS: 823 Chinese peritoneal dialysis (PD) patients (development cohort) and 826 PD and hemodialysis patients from the Netherlands NECOSAD study (validation cohort).
TESTS COMPARED
Equations to estimate residual kidney function (estimated clearance [eCl]) using serum creatinine, urea nitrogen, cystatin C, β-microglobulin (B2M), β-trace protein (BTP), and combinations, as well as demographic variables (age, sex, height, and weight). Equations were developed using multivariable linear regression analysis in the development cohort and then tested in the validation cohort. Equations were compared with published validated equations.
OUTCOMES
Residual kidney function measured as urinary clearance (mCl) of urea nitrogen (mCl) and average of creatinine and urea nitrogen clearance (mCl).
RESULTS
In external validation, bias (difference between mCl and eCl) was within ± 1.0 unit for all equations. Accuracy (percent of differences within ± 2.0 units) was significantly better for eCl, eCl, and eCl than eCl for both mCl (78%, 80%, and 81% vs 72%; < 0.05 for all) and mCl (72%, 78%, and 79% vs 68%; < 0.05 for all). The area under the curve for predicting mCl > 2.0 mL/min was highest for eCl (0.853) and eCl (0.848). Results were similar for other validated equations.
LIMITATIONS
Development cohort only consisted of PD patients, no gold-standard method for residual kidney function measurement.
CONCLUSIONS
These results confirm the validity and extend the generalizability of residual kidney function estimating equations from serum concentrations of low-molecular-weight proteins without urine collection.
原理与目的
建议测量残余肾功能以调整透析处方，但定时收集尿液困难且容易出错。根据内源性滤过标志物的血清浓度和人口统计学参数计算残余肾功能的公式将简化对残余肾功能的监测。然而，很少有使用小分子溶质和低分子量蛋白质的血清浓度来估计残余肾功能的公式得到开发和外部验证。
研究设计
诊断试验准确性研究。
设置与参与者
823名中国腹膜透析（PD）患者（开发队列）以及来自荷兰NECOSAD研究的826名PD和血液透析患者（验证队列）。
比较的测试
使用血清肌酐、尿素氮、胱抑素C、β-微球蛋白（B2M）、β-微量蛋白（BTP）及其组合以及人口统计学变量（年龄、性别、身高和体重）来估计残余肾功能（估计清除率[eCl]）的公式。公式在开发队列中使用多变量线性回归分析得出，然后在验证队列中进行测试。将这些公式与已发表的经过验证的公式进行比较。
结果
以尿素氮的尿清除率（mCl）以及肌酐和尿素氮清除率的平均值（mCl）来衡量残余肾功能。
结果
在外部验证中，所有公式的偏差（mCl与eCl之间的差异）均在±1.0单位以内。对于mCl（78%、80%和81%对72%；所有P<0.05）和mCl（72%、78%和79%对68%；所有P<0.05），eCl、eCl和eCl的准确性（差异在±2.0单位内的百分比）显著优于eCl。预测mCl>2.0 mL/min时，eCl（0.853）和eCl（0.848）的曲线下面积最高。其他经过验证的公式结果相似。
局限性
开发队列仅由PD患者组成，没有测量残余肾功能的金标准方法。
结论
这些结果证实了无需收集尿液即可根据低分子量蛋白质的血清浓度来估计残余肾功能的公式的有效性，并扩展了其通用性。
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