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A comparison of cystatin C- and creatinine-based prediction equations for the estimation of glomerular filtration rate in black South Africans

机译:基于胱抑素C和肌酐的预测方程式在估计南非黑人肾小球滤过率中的比较

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摘要

>Background. Serum creatinine (S-Cr)-based prediction equations are commonly used for estimating glomerular filtration rate (GFR). However, S-Cr concentration is also affected by other factors such as tubular secretion, muscle mass, diet, gender and age. Serum cystatin C (S-Cys C)-based prediction equations have been proposed as an improved potential alternative as S-Cys C levels are not influenced by many of the factors that affect creatinine concentration other than GFR. This may be of great benefit to patients with low muscle mass such as those infected with human immunodeficiency virus who are at increased risk for the development of renal impairment. The aim of this study was to develop and evaluate a S-Cys C-based prediction equation for different stages of renal disease in black South Africans.>Methods. One hundred patients with varying degrees of renal function were enrolled in the study. The plasma clearance of 51Cr-EDTA, a gold standard method, was used to measure GFR (mGFR). In addition, serum was analysed for S-Cr and S-Cys C on each participant. This dataset was split into a development dataset (n = 50) and a test dataset (n = 50). The development dataset was used to formulate a S-Cys C- and S-Cr-based prediction equation using multiple linear regression analysis. These equations together with the four-variable MDRD and CKD-EPI equation were then tested on the test dataset.>Results. In the test dataset, accuracy within 15% of measured GFR was 68% for the S-Cys C equation and 48% for the S-Cr equation. Root mean square error for S-Cr eGFR was 10.7 mL/min/1.73 m2 for those patients with mGFR < 60 mL/min/1.73 m2 and 25.5 mL/min/1.73 m2 for those patients with mGFR > 60 mL/min/1.73 m2. Root mean square error for S-Cys C eGFR was 10.2 mL/min/1.73 m2 for those patients with mGFR < 60 mL/min/1.73 m2 and 11.9 mL/min/1.73 m2 for those patients with mGFR > 60 mL/min/1.73 m2.>Conclusions. In this study, S-Cys C-based prediction equations appear to be more precise than those of S-Cr for those patients with mGFR > 60 mL/min/1.73 m2 and may therefore be of benefit in the earlier detection of renal impairment.
机译:>背景。基于血清肌酐(S-Cr)的预测方程式通常用于估计肾小球滤过率(GFR)。但是,S-Cr的浓度也受其他因素的影响,例如肾小管分泌物,肌肉质量,饮食,性别和年龄。已经提出了基于血清胱抑素C(S-Cys C)的预测方程作为改进的潜在替代方案,因为S-Cys C水平不受除GFR以外的许多影响肌酐浓度的因素的影响。这对于低肌肉量的患者(例如那些感染了人类免疫缺陷病毒的患者)有很大的益处,这些患者的肾功能损害发生风险增加。这项研究的目的是开发和评估基于S-Cys C的黑人南非人不同阶段肾脏疾病的预测方程。>方法。。本研究招募了100名不同程度肾功能的患者。在研究中。金标准方法 51 Cr-EDTA的血浆清除率用于测量GFR(mGFR)。另外,分析每个参与者的血清中的S-Cr和S-CysC。将该数据集分为开发数据集(n = 50)和测试数据集(n = 50)。使用多元线性回归分析,将开发数据集用于公式化基于S-Cys C和S-Cr的预测方程。然后在测试数据集上测试这些方程以及四变量MDRD和CKD-EPI方程。>结果。在测试数据集中,对于S-,G-值在15%以内的准确度为68% Cys C方程,S-Cr方程为48%。 mGFR <60 mL / min / 1.73 m 2 和25.5 mL / s的患者S-Cr eGFR的均方根误差为10.7 mL / min / 1.73m 2 mGFR> 60 mL / min / 1.73 m 2 的患者的min / 1.73 m 2 。对于mGFR <60 mL / min / 1.73 m 2 和11.9 mL的患者,S-Cys C eGFR的均方根误差为10.2 mL / min / 1.73 m 2 /min/1.73 m 2 对于mGFR> 60 mL / min / 1.73 m 2 的患者。>结论。。在本研究中,S-基于Cys C的预测方程对于mGFR> / 60 mL / min / 1.73 m 2 的患者似乎比S-Cr更为精确,因此可能有利于早期发现肾脏损害。

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