Abstract 抽象的

Background. We recently published and validated the new serum creatinine (Scr)-based full-age-spectrum equation (FAScrea) for estimating the glomerular filtration rate (GFR) for healthy and kidney–diseased subjects of all ages. The equation was based on the concept of normalized Scr and shows equivalent to superior prediction performance to the currently recommended equations for children, adolescents, adults and older adults.
背景。我们最近发布并验证了新的基于血清肌酐 (Scr) 的全年龄谱方程 (FAS crea ),用于估计所有年龄段的健康和肾病受试者的肾小球滤过率 (GFR)。该方程基于标准化 Scr 的概念,与目前推荐的儿童、青少年、成人和老年人方程相比,其预测性能相当出色。

Methods. Based on an evaluation of the serum cystatin C (ScysC) distribution, we defined normalization constants for ScysC (QcysC =0.82 mg/L for ages <70 years and QcysC =0.95 mg/L for ages ≥70 years). By replacing Scr/Qcrea in the FAScrea equation with ScysC/QcysC, or with the average of both normalized biomarkers, we obtained new ScysC-based (FAScysC) and combined Scr-/ScysC-based FAS equations (FAScombi). To validate the new FAScysC and FAScombi we collected data on measured GFR, Scr, ScysC, age, gender, height and weight from 11 different cohorts including n = 6132 unique white subjects (368 children, aged ≤18 years, 4295 adults and 1469 older adults, aged ≥70 years).
方法。根据对血清胱抑素 C (ScysC) 分布的评估,我们定义了 ScysC 的标准化常数(年龄 <70 岁的 Q cysC  =0.82 mg/L,Q cysC  =0.95 mg/L(年龄≥70岁)。通过用 ScysC/Q cysC 替换 FAS crea 方程中的 Scr/Q crea ,或者用两个归一化生物标志物的平均值,我们获得了新的基于 ScysC 的(FAS cysC ) 和基于 Scr/ScysC 的组合 FAS 方程 (FAS combi )。为了验证新的 FAS cysC 和 FAS combi ,我们收集了 11 个不同队列的测量 GFR、Scr、ScysC、年龄、性别、身高和体重的数据,其中包括 n = 6132 名独特的白人受试者(368 名儿童,年龄≤18 岁,4295 名成人,1469 名老年人,年龄≥70 岁)。

Results. In children and adolescents, the new FAScysC equation showed significantly better performance [percentage of patients within 30% of mGFR (P30) = 86.1%] than the Caucasian Asian Paediatric Adult Cohort equation (P30 = 76.6%; P < 0.0001), or the ScysC-based Schwartz equation (P30 = 68.8%; P < 0.0001) and the FAScombi equation outperformed all equations with P30 = 92.1% (P < 0.0001). In adults, the FAScysC equation (P30 = 82.6%) performed equally as well as the Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPIcysC) (P30 = 80.4%) and the FAScombi equation (P30 = 89.9%) was also equal to the combined CKD-EPI equation (P30 = 88.2%). In older adults, FAScysC was superior (P30 = 88.2%) to CKD-EPIcysC (P30 = 84.4%; P < 0.0001) and the FAScombi equation (P30 = 91.2%) showed significantly higher performance than the combined CKD-EPI equation (P30 = 85.6%) (P < 0.0001).
结果。在儿童和青少年中,新的 FAS cysC 方程显示出比高加索亚洲儿童成人队列方程(P30 = 76.6%;mGFR 30% 以内的患者百分比 (P30) = 86.1%)显着更好的性能。 P < 0.0001),或基于 ScysC 的 Schwartz 方程 (P30 = 68.8%;P < 0.0001) 和 FAS combi 方程优于所有方程,P30 = 92.1% (P < 0.0001)。在成人中,FAS cysC 方程 (P30 = 82.6%) 与慢性肾病流行病学协作方程 (CKD-EPI cysC ) (P30 = 80.4%) 表现相同,并且FAS combi 方程 (P30 = 89.9%) 也等于组合的 CKD-EPI 方程 (P30 = 88.2%)。在老年人中,FAS cysC 优于 (P30 = 88.2%) CKD-EPI cysC (P30 = 84.4%; P < 0.0001) 和 FAS combi 方程 (P30 = 91.2%) 显示出比组合 CKD-EPI 方程 (P30 = 85.6%) 显着更高的性能 (P < 0.0001)。

Conclusion. The FAS equation is not only applicable to all ages, but also for all recommended renal biomarkers and their combinations.
结论。 FAS 方程不仅适用于所有年龄段,而且适用于所有推荐的肾脏生物标志物及其组合。

INTRODUCTION 介绍

Serum creatinine (Scr)-based estimating glomerular filtration rate (eGFR) equations are commonly used and reported when Scr is measured. Despite the worldwide acceptance of isotope dilution mass spectrometry (IDMS)-standardized Scr assays, Scr-based eGFR equations are still relatively imprecise [1]. Also, different equations are proposed for children, adults and older adults as most equations lack continuity and accuracy across the full age spectrum.
在测量 Scr 时,通常使用和报告基于血清肌酐 (Scr) 的估算肾小球滤过率 (eGFR) 方程。尽管全世界都接受同位素稀释质谱 (IDMS) 标准化 Scr 测定,但基于 Scr 的 eGFR 方程仍然相对不精确 [1]。此外,针对儿童、成人和老年人提出了不同的方程,因为大多数方程在整个年龄范围内缺乏连续性和准确性。

We recently published a Scr-based full-age-spectrum (FAScrea) equation [2] that has been validated in a large number of healthy and kidney–diseased white individuals (n = 6870) including 735 children, 4371 adults and 1764 older adults against measured GFR (mGFR) and using IDMS-equivalent Scr. The FAScrea equation showed improved validity and continuity across the full age spectrum and was less biased and more accurate than the currently recommended Scr-based eGFR equations.
我们最近发表了一个基于 Scr 的全年龄谱 (FAS crea ) 方程 [2],该方程已在大量健康和患有肾病的白人 (n = 6870) 中得到验证,其中包括 735 名儿童、4371 名成人和 1764 名老年人对照测量的 GFR (mGFR) 并使用 IDMS 等效 Scr。 FAS crea 方程在整个年龄范围内显示出更高的有效性和连续性,并且比目前推荐的基于 Scr 的 eGFR 方程偏差更小、更准确。

The FAS equation is based on three fundamental assumptions:
FAS 方程基于三个基本假设:

  • The average GFR for healthy populations (children, adolescents and young adults) is equal to a value of 107.3 mL/min/1.73 m2 after kidney function matures (around 2 years of age) until the age of 40 years. This assumption is also supported by the results of a recent meta-analysis in living kidney donors [3].
    健康人群(儿童、青少年和青壮年)肾功能成熟后(2岁左右)至40岁期间的平均GFR等于107.3 mL/min/1.73 m 2 年。最近对活体肾脏捐赠者进行的荟萃分析结果也支持了这一假设[3]。

  • The age decline of GFR begins at around 40 years.
    GFR 的年龄下降从 40 岁左右开始。

  • GFR and population-normalized Scr (Scr/Qcrea) are inversely related (Qcrea being the mean or median Scr concentration of the corresponding age-/sex-matched healthy population).
    GFR 和人群标准化 Scr (Scr/Q crea ) 呈负相关(Q crea 是相应年龄/性别匹配的健康人群的平均或中位 Scr 浓度)。

These three assumptions have led to the construction of the simple age-knotted FAScrea equation, which takes the form [2]:
这三个假设导致了简单的年龄结 FAS crea 方程的构造,其形式为 [2]:
FAScrea= 107.3SCrQcrea×[0.988Age-40 when age>40 years].

The equation is simple and intuitive and can be easily explained: when Scr/Qcrea deviates from ‘1’, the eGFR will deviate from the average value of 107.3 mL/min/1.73 m2. Scr/Qcrea, for every healthy age-/sex-matched population, is normally distributed (Gaussian distribution) around the mean of ‘1’ (a consequence of the definition of Qcrea). It has been shown that the 2.5th percentile (Pct) = 0.67 and the 97.5th Pct = 1.33, or, equivalently, the standard deviation (SD) is 0.1683 [2].
该方程简单直观,易于解释:当 Scr/Q crea 偏离“1”时,eGFR 将偏离平均值 107.3 mL/min/1.73 m 2 呈正态分布(高斯分布),均值“1”(Q crea ).研究表明,第 2.5 个百分位数 (Pct) = 0.67,第 97.5 个百分位数 = 1.33,或者等效地,标准差 (SD) 为 0.1683 [2]。

Serum cystatin C (ScysC) is considered to be a potential alternative to Scr for estimating GFR [4], especially since a certified reference cystatin C material became available in 2010, allowing standardization of ScysC assays [5].
血清胱抑素 C (ScysC) 被认为是估算 GFR 时 Scr 的潜在替代品 [4],特别是自 2010 年获得认证的参考胱抑素 C 材料问世以来,ScysC 测定得以标准化 [5]。

In this article, we demonstrate that the last assumption (that GFR is inversely related to the normalized Scr biomarker) also applies to ScysC, if properly normalized. We show that the FAScrea equation can be transformed into a ScysC-based FAS equation (FAScysC) and a combined Scr-/ScysC-based FAS equation (FAScombi), by simply replacing the normalized Scr (Scr/Qcrea) by ScysC/QcysC or by the combination of Scr/Qcrea and ScysC/QcysC [i.e. the (weighted) average of Scr/Qcrea and ScysC/QcysC], where QcysC is the normalization factor for ScysC.
在本文中,我们证明了最后一个假设(GFR 与标准化 Scr 生物标志物成反比)也适用于 ScysC(如果正确标准化)。我们证明 FAS crea 方程可以转换为基于 ScysC 的 FAS 方程 (FAS cysC ) 和基于 Scr/ScysC 的组合 FAS 方程 (FAS combi 或 Scr/Q crea 的组合替换标准化 Scr (Scr/Q crea ) 和ScysC/Q cysC [即Scr/Q crea 和 ScysC/Q cysC 的(加权)平均值],其中 Q cysC 是 ScysC 的归一化因子。

In the first part of this study, we give a rationale for choosing the normalization factor QcysC for ScysC. Next, we validate the FAScysC and FAScombi equation against mGFR and compare the performance of these equations with the currently recommended and most used eGFR equations (SchwartzcysC [6], Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPIcysC) [4], Caucasian Asian Paediatric Adult Cohort equation (CAPA) [7], combined CKD-EPIcombi [4] and BIS2 [8]). Finally, we evaluate the performance of all FAS equations (by varying the weighting factors for the normalized biomarkers) in all age groups.
在本研究的第一部分中,我们给出了为 ScysC 选择归一化因子 Q cysC 的基本原理。接下来,我们根据 mGFR 验证 FAS cysC 和 FAS combi 方程,并将这些方程的性能与当前推荐和最常用的 eGFR 方程进行比较 (Schwartz cysC [6]、慢性肾脏病流行病学协作方程(CKD-EPI cysC )[4]、高加索亚裔儿科成人队列方程(CAPA)[7]、组合 CKD-EPI combi [ 4] 和 BIS2 [ 8])。最后,我们评估了所有年龄组中所有 FAS 方程的表现(通过改变标准化生物标志物的权重因子)。

MATERIALS AND METHODS 材料和方法

Overview of study design and participants
研究设计和参与者概述

We collected data from 11 cohorts, forming a representative sample of the general population and renal disease patients. For the same six cohorts (Saint-Etienne, Tromsø, Rochester and Minnesota for adults; Kent and Berlin for older adults [2]) that were used for the validation of the FAScrea equation, we additionally collected the ScysC results. The other cohorts used in the previous validation did not have ScysC data available, and, therefore, we collected data of new cohorts. For children, adolescents and young adults (<21 years), one cohort came from the University Hospital in Leuven (n = 114), and one from Lyon (n = 695). Both cohorts contained children and adolescents with established renal pathologies. The data from Leuven contained single-time point measurements per child and the data from Lyon (n = 695) were from 259 children with serial measurements over a period of several years, but we used the first measurement only. We further collected data from a cohort of healthy and renal disease adults from Paris (n = 603), from Lyon (n = 598) and from the Chronic Renal Insufficiency Cohort (CRIC; n = 3939) [9], which we restricted to whites only (n = 1824) and to the first visit where all required variables were available (n = 674). All datasets were centralized by the first author for data analysis. This retrospective non-interventional study was approved by the Institutional Ethical Board of the University Hospital of Leuven, Belgium.
我们收集了 11 个队列的数据,形成了一般人群和肾病患者的代表性样本。对于用于验证 FAS crea 方程的相同六个队列(成年人为圣艾蒂安、特罗姆瑟、罗切斯特和明尼苏达;老年人为肯特和柏林 [2]),我们还收集了ScysC 结果。先前验证中使用的其他队列没有可用的 ScysC 数据,因此,我们收集了新队列的数据。对于儿童、青少年和年轻人(<21 岁),一组来自鲁汶大学医院 (n = 114),一组来自里昂 (n = 695)。两个队列均包含患有明确肾脏病变的儿童和青少年。鲁汶的数据包含每个儿童的单时间点测量结果,里昂的数据 (n = 695) 来自 259 名儿童,在几年内进行了连续测量,但我们仅使用了第一次测量。我们进一步收集了来自巴黎(n = 603)、里昂(n = 598)和慢性肾功能不全队列(CRIC;n = 3939)的健康和肾脏疾病成年人队列的数据[9],我们将其限制为仅白人 (n = 1824) 和第一次访问时所有必需的变量均可用 (n = 674)。所有数据集均由第一作者集中进行数据分析。这项回顾性非干预性研究得到了比利时鲁汶大学医院机构伦理委员会的批准。

In total, we collected data on mGFR, Scr, ScysC, age, gender, height and weight for n = 6132 participants (n = 368 children aged between 1 and 18 years; n = 4295 adults aged between 18 and 70 years and n = 1469 older adults aged ≥70 years).
总的来说,我们收集了 n = 6132 名参与者的 mGFR、Scr、ScysC、年龄、性别、身高和体重的数据(n = 368 名 1 至 18 岁的儿童;n = 4295 名 18 至 70 岁的成年人,n = 1469 名年龄≥70 岁的老年人)。

We further used a separate cohort (n = 1333) from the Berlin Initiative Study [8] of apparently healthy older subjects to study the age dependency of the ScysC distribution. This cohort was obtained from 2069 subjects (2069 baseline samples and 1693 follow-up samples) aged >70 years (Berlin residents), which we reduced to a subset of 1333 individuals who were defined as apparently healthy; i.e. no history of myocardial infarction, no history of stroke, not on dialysis, not deceased between first and second follow-up study visit, no albuminuria (ACR <30 mg/g), arterial blood pressure <160/90 mmHg.
我们进一步使用来自柏林倡议研究 [8] 的一个单独的队列 (n = 1333),该队列由明显健康的老年受试者组成,以研究 ScysC 分布的年龄依赖性。该队列来自年龄 > 70 岁(柏林居民)的 2069 名受试者(2069 名基线样本和 1693 名随访样本),我们将其减少到 1333 名被定义为表面健康的个体的子集;即无心肌梗塞病史、无中风病史、未进行透析、在第一次和第二次随访研究访视期间未死亡、无蛋白尿(ACR <30 mg/g)、动脉血压<160/90 mmHg。

Methods 方法

The new FAS equation(s)
新的 FAS 方程

The form of the FAScrea equation was maintained, but Scr/Qcrea is replaced by ScysC/QcysC:
FAS crea 方程的形式保持不变,但 Scr/Q crea 替换为 S cysC /Q cysC
FAScysC=107.3ScysCQcysC×[0.988Age-40 when age>40 years].
By extending the same concept, we used the weighted average of the two normalized biomarkers Scr/Qcrea and ScysC/QcysC, leading to the general form of:
通过扩展相同的概念,我们使用两个标准化生物标志物 Scr/Q crea 和 ScysC/Q cysC 的加权平均值,得到以下一般形式:
FAScombi=107.3α×ScrQcrea+1-α×ScysCQcysC×[0.988Age-40 when age>40 years].
The coefficient ‘α’ in the denominator may be considered as a weighting factor for the normalized renal biomarkers. In case α = 1, the FAS equation depends entirely on Scr/Qcrea and equals the FAScrea equation; in case α = 0, the FAS equation becomes the ScysC-based FAScysC equation. In all other situations for 0 < α < 1, the equation is a combined Scr/ScysC equation. For α = 0.5, the denominator is equal to the average of both normalized biomarkers. We further discuss the influence of α in the ‘Results’ section.
分母中的系数“α”可被视为标准化肾脏生物标志物的加权因子。当α = 1时,FAS方程完全取决于Scr/Q crea 并且等于FAS crea 方程;当 α = 0 时,FAS 方程变为基于 ScysC 的 FAS cysC 方程。在 0 < α < 1 的所有其他情况下,该方程是 Scr/ScysC 组合方程。当 α = 0.5 时,分母等于两个归一化生物标志物的平均值。我们在“结果”部分进一步讨论 α 的影响。

mGFR, Scr and cystatin C assays
mGFR、Scr 和胱抑素 C 检测

A summary of the methods used in the different collaborating centres is given in Tables 1 and 2. Direct GFR measurements were obtained with different reference methods as described previously [2, 10]. Scr was measured with an enzymatic assay, equivalent to IDMS, or directly with IDMS, or recalculated to the enzymatic assay, in all centres. ScysC was measured with the calibrated particle-enhanced nephelometric (PENIA) method of Siemens in Saint-Etienne, Berlin and partially in Lyon. The ScysC measurements for the CRIC Study were done with the non-calibrated PENIA assay of Siemens, but calculated back to the certified reference material, as previously described [4]. The non-calibrated PENIA assay of Siemens was also used in Rochester, Kent, and partially in Lyon, and the results were recalculated to the certified reference standard, using the multiplication factor in Rochester [11] and in Lyon and Kent [12], according to the manufacturer’s specifications. Tromsø used the non-calibrated (with back calculation) and Leuven used the calibrated particle-enhanced turbidimetric (PETIA, Tina quant®) assay of Roche (Tables 1 and 2).
表 1 和表 2 总结了不同合作中心使用的方法。直接 GFR 测量是使用先前所述的不同参考方法获得的 [2, 10]。在所有中心,Scr 均采用相当于 IDMS 的酶测定法进行测量,或直接使用 IDMS 进行测量,或根据酶测定法重新计算。 ScysC 采用西门子在圣艾蒂安、柏林和里昂的校准颗粒增强散射比浊法 (PENIA) 进行测量。 CRIC 研究的 ScysC 测量是使用西门子的非校准 PENIA 测定完成的,但计算回经过认证的参考材料,如前所述 [4]。西门子的非校准 PENIA 测定法也在罗切斯特、肯特以及里昂的部分地区使用,并且使用罗切斯特 [11] 以及里昂和肯特 [12] 的倍增因子将结果重新计算为经过认证的参考标准,根据制造商的规格。特罗姆瑟使用未校准(带反算),鲁汶使用罗氏的校准颗粒增强比浊法(PETIA、Tina quant®)测定法(表 1 和表 2)。

Table 1

Overview of the methods used in this study for mGFR and Scr


表 1 本研究中 mGFR 和 Scr 的方法概述
Origin 起源mGFR 肾小球滤过率Scr
Leuven, Belgium 比利时鲁汶51Cr-EDTA (4 points)
51 Cr-EDTA (4 分)
Creatinine Plus, Roche enzym.
肌酐Plus,罗氏酶。
Lyon, France 法国里昂Inulina or Iohexol (3 points)
菊粉 a 或碘海醇 (3 分)
Creatinine Plus, Roche enzym.
肌酐Plus,罗氏酶。
Saint-Etienne, France 法国圣艾蒂安Iohexol (2 points) 碘海醇 (2 分)Enzymatic, Orthoclinical Diagn.
酶学、正临床诊断。
Tromsø, Norway 挪威特罗姆瑟Iohexol (1 point) 碘海醇 (1 分)Creatinine Plus, Roche enzym.
肌酐Plus,罗氏酶。
Rochester, MN, USA 美国明尼苏达州罗切斯特IothalamateaCreatinine Plus, Roche enzym.
肌酐Plus,罗氏酶。
Berlin, Germany 柏林,德国Iohexol (8 points) 碘海醇 (8 分)Creatinine Plus, Roche enzym.
肌酐Plus,罗氏酶。
Kent, UK 英国肯特Iohexol (3 points) 碘海醇 (3 分)IDMS
Paris, France 法国巴黎51Cr-EDTAaEnzymatic, Siemens, standardized to IDMS
酶促,西门子,标准化至 IDMS
CRIC, USA 审评委,美国125I-Iothalamateacalculated back to Creatinine Plus, Roche enzym.
计算回肌酐 Plus、罗氏酶。
OriginmGFRScr
Leuven, Belgium51Cr-EDTA (4 points)Creatinine Plus, Roche enzym.
Lyon, FranceInulina or Iohexol (3 points)Creatinine Plus, Roche enzym.
Saint-Etienne, FranceIohexol (2 points)Enzymatic, Orthoclinical Diagn.
Tromsø, NorwayIohexol (1 point)Creatinine Plus, Roche enzym.
Rochester, MN, USAIothalamateaCreatinine Plus, Roche enzym.
Berlin, GermanyIohexol (8 points)Creatinine Plus, Roche enzym.
Kent, UKIohexol (3 points)IDMS
Paris, France51Cr-EDTAaEnzymatic, Siemens, standardized to IDMS
CRIC, USA125I-Iothalamateacalculated back to Creatinine Plus, Roche enzym.

For mGFR, arenal clearance, all other methods are plasma clearance methods. mGFR is indexed for body surface area using the Dubois formula.
对于mGFR, a 肾清除率,所有其他方法均为血浆清除率方法。 mGFR 使用 Dubois 公式对体表面积进行索引。

Table 1

Overview of the methods used in this study for mGFR and Scr


表 1 本研究中 mGFR 和 Scr 的方法概述
Origin 起源mGFR 肾小球滤过率Scr
Leuven, Belgium 比利时鲁汶51Cr-EDTA (4 points)
51 Cr-EDTA (4 分)
Creatinine Plus, Roche enzym.
肌酐Plus,罗氏酶。
Lyon, France 法国里昂Inulina or Iohexol (3 points)
菊粉 a 或碘海醇 (3 分)
Creatinine Plus, Roche enzym.
肌酐Plus,罗氏酶。
Saint-Etienne, France 法国圣艾蒂安Iohexol (2 points) 碘海醇 (2 分)Enzymatic, Orthoclinical Diagn.
酶学、正临床诊断。
Tromsø, Norway 挪威特罗姆瑟Iohexol (1 point) 碘海醇 (1 分)Creatinine Plus, Roche enzym.
肌酐Plus,罗氏酶。
Rochester, MN, USA 美国明尼苏达州罗切斯特IothalamateaCreatinine Plus, Roche enzym.
肌酐Plus,罗氏酶。
Berlin, Germany 柏林,德国Iohexol (8 points) 碘海醇 (8 分)Creatinine Plus, Roche enzym.
肌酐Plus,罗氏酶。
Kent, UK 英国肯特Iohexol (3 points) 碘海醇 (3 分)IDMS
Paris, France 法国巴黎51Cr-EDTAaEnzymatic, Siemens, standardized to IDMS
酶促,西门子,标准化至 IDMS
CRIC, USA 审评委,美国125I-Iothalamateacalculated back to Creatinine Plus, Roche enzym.
计算回肌酐 Plus、罗氏酶。
OriginmGFRScr
Leuven, Belgium51Cr-EDTA (4 points)Creatinine Plus, Roche enzym.
Lyon, FranceInulina or Iohexol (3 points)Creatinine Plus, Roche enzym.
Saint-Etienne, FranceIohexol (2 points)Enzymatic, Orthoclinical Diagn.
Tromsø, NorwayIohexol (1 point)Creatinine Plus, Roche enzym.
Rochester, MN, USAIothalamateaCreatinine Plus, Roche enzym.
Berlin, GermanyIohexol (8 points)Creatinine Plus, Roche enzym.
Kent, UKIohexol (3 points)IDMS
Paris, France51Cr-EDTAaEnzymatic, Siemens, standardized to IDMS
CRIC, USA125I-Iothalamateacalculated back to Creatinine Plus, Roche enzym.

For mGFR, arenal clearance, all other methods are plasma clearance methods. mGFR is indexed for body surface area using the Dubois formula.
对于mGFR, a 肾清除率,所有其他方法均为血浆清除率方法。 mGFR 使用 Dubois 公式对体表面积进行索引。

Table 2

Overview of the methods for ScysC


表2ScysC方法概述
Origin, time of measurement
原产地、测量时间
ScysC assay ScysC检测Calibration to reference (ERM®-DA471/IFCC)
校准参考 (ERM®-DA471/IFCC)
Automate 自动化CV (%) 简历 (%)
Leuven, 2015 鲁汶, 2015Roche PETIA (Tina quant® Gen2)
罗氏 PETIA(Tina Quant® Gen2)
YesIntegra 400 Plus2.6, 1.2, 1.0 at 0.503, 2.98, 6.11 mg/L
2.6、1.2、1.0 0.503、2.98、6.11mg/L
Lyon, 2010–15 里昂,2010–15Siemens N-Latex® PENIA 西门子 N-Latex® PENIANo, recalculation by MF = 1.11 before April 2011; yes after April 2011
否,2011年4月之前按MF = 1.11重新计算;是的,2011 年 4 月之后
BN Prospec analyser BN Prospec 分析仪3.5 at 2.3 mg/L 2.3mg/L 时为 3.5
Saint-Etienne, 2012 圣艾蒂安, 2012Siemens PENIA [13] 西门子PENIA [13]YesBN Prospec analyser BN Prospec 分析仪2.9, 2.1 at 1.03, 1.93 mg/L
2.9, 2.1 1.03, 1.93mg/L
Tromsø, 2007–08 特罗姆瑟,2007–08Roche PETIA [14] (Tina quant® Gen1)
罗氏 PETIA [14](Tina Quant® Gen1)
No, recalculation using −0.064 + ScysC × 0.998
否,使用 −0.064 + ScysC × 0.998 重新计算
Gentian reagents, Modular P800 analyser
龙胆试剂、模块化 P800 分析仪
3.2
Rochester, 2001–11 罗切斯特,2001–11Siemens PENIA [11] 西门子PENIA [11]No, recalculation by MF = 1.14
否,按MF = 1.14重新计算
Dade Behring BN II Nephelometer
Dade Behring BN II 浊度计
3.5
Berlin, 2011 柏林,2011 年Siemens N-Latex® PENIA [8]
西门子 N-Latex® PENIA [8]
YesBN Prospec1.5, 3.5, 2.4 at 0.8, 2.3, 7.4 mg/L
1.5、3.5、2.4 0.8、2.3、7.4mg/L
Kent, 2008–11 肯特郡,2008–11Siemens PENIA [12] 西门子PENIA [12]No, recalculation by MF = 1.11
否,按MF = 1.11重新计算
BN Prospec analyser BN Prospec 分析仪3.5 at 2.3 mg/L 2.3mg/L 时为 3.5
Paris, 2013 巴黎,2013Siemens PENIA 西门子PENIAYesDimension Vista 维斯塔≤3.5
CRIC [9], 2003–08 审评委[9], 2003–08Siemens N-Latex® PENIA [4]
西门子 N-Latex® PENIA [4]
No, recalculation by authors = 1.12 × (0.083 + 0.789 × (0.039 + 1.061 × CRICcysC)
否,作者重新计算 = 1.12 × (0.083 + 0.789 × (0.039 + 1.061 × CRIC cysC )
BN Prospec analyser BN Prospec 分析仪4.9
Origin, time of measurementScysC assayCalibration to reference (ERM®-DA471/IFCC)AutomateCV (%)
Leuven, 2015Roche PETIA (Tina quant® Gen2)YesIntegra 400 Plus2.6, 1.2, 1.0 at 0.503, 2.98, 6.11 mg/L
Lyon, 2010–15Siemens N-Latex® PENIANo, recalculation by MF = 1.11 before April 2011; yes after April 2011BN Prospec analyser3.5 at 2.3 mg/L
Saint-Etienne, 2012Siemens PENIA [13]YesBN Prospec analyser2.9, 2.1 at 1.03, 1.93 mg/L
Tromsø, 2007–08Roche PETIA [14] (Tina quant® Gen1)No, recalculation using −0.064 + ScysC × 0.998Gentian reagents, Modular P800 analyser3.2
Rochester, 2001–11Siemens PENIA [11]No, recalculation by MF = 1.14Dade Behring BN II Nephelometer3.5
Berlin, 2011Siemens N-Latex® PENIA [8]YesBN Prospec1.5, 3.5, 2.4 at 0.8, 2.3, 7.4 mg/L
Kent, 2008–11Siemens PENIA [12]No, recalculation by MF = 1.11BN Prospec analyser3.5 at 2.3 mg/L
Paris, 2013Siemens PENIAYesDimension Vista≤3.5
CRIC [9], 2003–08Siemens N-Latex® PENIA [4]No, recalculation by authors = 1.12 × (0.083 + 0.789 × (0.039 + 1.061 × CRICcysC)BN Prospec analyser4.9

MF, multiplication factor; CV, coefficient of variation.
MF, 倍增因子; CV, 变异系数。

Table 2

Overview of the methods for ScysC


表2ScysC方法概述
Origin, time of measurement
原产地、测量时间
ScysC assay ScysC检测Calibration to reference (ERM®-DA471/IFCC)
校准参考 (ERM®-DA471/IFCC)
Automate 自动化CV (%) 简历 (%)
Leuven, 2015 鲁汶, 2015Roche PETIA (Tina quant® Gen2)
罗氏 PETIA(Tina Quant® Gen2)
YesIntegra 400 Plus2.6, 1.2, 1.0 at 0.503, 2.98, 6.11 mg/L
2.6、1.2、1.0 0.503、2.98、6.11mg/L
Lyon, 2010–15 里昂,2010–15Siemens N-Latex® PENIA 西门子 N-Latex® PENIANo, recalculation by MF = 1.11 before April 2011; yes after April 2011
否,2011年4月之前按MF = 1.11重新计算;是的,2011 年 4 月之后
BN Prospec analyser BN Prospec 分析仪3.5 at 2.3 mg/L 2.3mg/L 时为 3.5
Saint-Etienne, 2012 圣艾蒂安, 2012Siemens PENIA [13] 西门子PENIA [13]YesBN Prospec analyser BN Prospec 分析仪2.9, 2.1 at 1.03, 1.93 mg/L
2.9, 2.1 1.03, 1.93mg/L
Tromsø, 2007–08 特罗姆瑟,2007–08Roche PETIA [14] (Tina quant® Gen1)
罗氏 PETIA [14](Tina Quant® Gen1)
No, recalculation using −0.064 + ScysC × 0.998
否,使用 −0.064 + ScysC × 0.998 重新计算
Gentian reagents, Modular P800 analyser
龙胆试剂、模块化 P800 分析仪
3.2
Rochester, 2001–11 罗切斯特,2001–11Siemens PENIA [11] 西门子PENIA [11]No, recalculation by MF = 1.14
否,按MF = 1.14重新计算
Dade Behring BN II Nephelometer
Dade Behring BN II 浊度计
3.5
Berlin, 2011 柏林,2011 年Siemens N-Latex® PENIA [8]
西门子 N-Latex® PENIA [8]
YesBN Prospec1.5, 3.5, 2.4 at 0.8, 2.3, 7.4 mg/L
1.5、3.5、2.4 0.8、2.3、7.4mg/L
Kent, 2008–11 肯特郡,2008–11Siemens PENIA [12] 西门子PENIA [12]No, recalculation by MF = 1.11
否,按MF = 1.11重新计算
BN Prospec analyser BN Prospec 分析仪3.5 at 2.3 mg/L 2.3mg/L 时为 3.5
Paris, 2013 巴黎,2013Siemens PENIA 西门子PENIAYesDimension Vista 维斯塔≤3.5
CRIC [9], 2003–08 审评委[9], 2003–08Siemens N-Latex® PENIA [4]
西门子 N-Latex® PENIA [4]
No, recalculation by authors = 1.12 × (0.083 + 0.789 × (0.039 + 1.061 × CRICcysC)
否,作者重新计算 = 1.12 × (0.083 + 0.789 × (0.039 + 1.061 × CRIC cysC )
BN Prospec analyser BN Prospec 分析仪4.9
Origin, time of measurementScysC assayCalibration to reference (ERM®-DA471/IFCC)AutomateCV (%)
Leuven, 2015Roche PETIA (Tina quant® Gen2)YesIntegra 400 Plus2.6, 1.2, 1.0 at 0.503, 2.98, 6.11 mg/L
Lyon, 2010–15Siemens N-Latex® PENIANo, recalculation by MF = 1.11 before April 2011; yes after April 2011BN Prospec analyser3.5 at 2.3 mg/L
Saint-Etienne, 2012Siemens PENIA [13]YesBN Prospec analyser2.9, 2.1 at 1.03, 1.93 mg/L
Tromsø, 2007–08Roche PETIA [14] (Tina quant® Gen1)No, recalculation using −0.064 + ScysC × 0.998Gentian reagents, Modular P800 analyser3.2
Rochester, 2001–11Siemens PENIA [11]No, recalculation by MF = 1.14Dade Behring BN II Nephelometer3.5
Berlin, 2011Siemens N-Latex® PENIA [8]YesBN Prospec1.5, 3.5, 2.4 at 0.8, 2.3, 7.4 mg/L
Kent, 2008–11Siemens PENIA [12]No, recalculation by MF = 1.11BN Prospec analyser3.5 at 2.3 mg/L
Paris, 2013Siemens PENIAYesDimension Vista≤3.5
CRIC [9], 2003–08Siemens N-Latex® PENIA [4]No, recalculation by authors = 1.12 × (0.083 + 0.789 × (0.039 + 1.061 × CRICcysC)BN Prospec analyser4.9

MF, multiplication factor; CV, coefficient of variation.
MF, 倍增因子; CV, 变异系数。

eGFR equations eGFR 方程

The new FAScysC equation and the FAScombi equation were compared and validated against mGFR and against the currently available and recommended eGFR equations listed in Table 3.
新的 FAS cysC 方程和 FAS combi 方程针对 mGFR 以及表 3 中列出的当前可用和推荐的 eGFR 方程进行了比较和验证。

Table 3

eGFR equations for the performance comparisons


表 3 用于性能比较的 eGFR 方程
Scr-based equations 基于 Scr 的方程
Schwartzcrea [15] 施瓦茨 crea [ 15]eGFR = 0.413 × Ht/Scr
CKD-EPI [16] CKD-EPI [16]eGFR = 141 × min(Scr/κ)α × max(Scr/κ)−1.209 × 0.993Age × (1.018 if female) κ = 0.7 for females and 0.9 for males; α = −0.329 for females and −0.411 for males
eGFR = 141 × min(Scr/κ) α × max(Scr/κ) −1.209 × 0.993 Age ×(如果是女性,则为 1.018)κ = 0.7女性为 0.9,男性为 0.9;女性 α = -0.329,男性 α = -0.411
ScysC-based equations 基于ScysC的方程
SchwartzcysC [6] 施瓦茨 cysC [ 6]eGFR = 40.6 (1.8/ScysC)0.93
eGFR = 40.6(1.8/ScysC) 0.93
CAPA [7] 卡帕 [7]eGFR = 130 × ScysC−1.069 × Age−0.117 – 7
eGFR = 130 × ScysC −1.069 × 年龄 −0.117 – 7
CKD-EPIcysc [4]eGFR = 133 × min(ScysC/0.8,1)−0.499 × max(ScysC/0.8,1)−1.328 × 0.996Age × (0.932 if female)
eGFR = 133 × min(ScysC/0.8,1) −0.499 × max(ScysC/0.8,1) −1.328 × 0.996 Age ×(女性为 0.932)
Combined equations 组合方程
CKD-EPIcrea,cysc [4]eGFR = 135 × min(Scr/κ,1)α × max(Scr/κ,1)−0.601 × min(ScysC/0.8,1)−0.375 × max(ScysC/0.8,1)−0.711 × 0.995Age × (0.969 if female) (κ = 0.7 for females, 0.9 for males, α  = −0.248 for females and −0.207 for males)
eGFR = 135 × min(Scr/κ,1) α × max(Scr/κ,1) −0.601 × min(ScysC/0.8,1) −0.375 × max(ScysC/0.8,1) −0.711 × 0.995 Age ×(女性为 0.969)(女性为 κ = 0.7,男性为 0.9,女性为 α = -0.248,男性-0.207)
BIS2 [8] 国际清算银行2 [8]eGFR = 767 × ScysC−0.61 × Scr−0.40 × Age−0.57 × (0.87 if female)
eGFR = 767 × ScysC −0.61 × Scr −0.40 × 年龄 −0.57 ×(如果是女性,则为 0.87)
Scr-based equations
Schwartzcrea [15]eGFR = 0.413 × Ht/Scr
CKD-EPI [16]eGFR = 141 × min(Scr/κ)α × max(Scr/κ)−1.209 × 0.993Age × (1.018 if female) κ = 0.7 for females and 0.9 for males; α = −0.329 for females and −0.411 for males
ScysC-based equations
SchwartzcysC [6]eGFR = 40.6 (1.8/ScysC)0.93
CAPA [7]eGFR = 130 × ScysC−1.069 × Age−0.117 – 7
CKD-EPIcysc [4]eGFR = 133 × min(ScysC/0.8,1)−0.499 × max(ScysC/0.8,1)−1.328 × 0.996Age × (0.932 if female)
Combined equations
CKD-EPIcrea,cysc [4]eGFR = 135 × min(Scr/κ,1)α × max(Scr/κ,1)−0.601 × min(ScysC/0.8,1)−0.375 × max(ScysC/0.8,1)−0.711 × 0.995Age × (0.969 if female) (κ = 0.7 for females, 0.9 for males, α  = −0.248 for females and −0.207 for males)
BIS2 [8]eGFR = 767 × ScysC−0.61 × Scr−0.40 × Age−0.57 × (0.87 if female)

Scr, serum creatinine (mg/dL); ScysC, serum cystatin C (mg/L); Ht, height in cm.
Scr, 血清肌酐(mg/dL); ScysC, 血清胱抑素C(mg/L); Ht,高度(厘米)。

Table 3

eGFR equations for the performance comparisons


表 3 用于性能比较的 eGFR 方程
Scr-based equations 基于 Scr 的方程
Schwartzcrea [15] 施瓦茨 crea [ 15]eGFR = 0.413 × Ht/Scr
CKD-EPI [16] CKD-EPI [16]eGFR = 141 × min(Scr/κ)α × max(Scr/κ)−1.209 × 0.993Age × (1.018 if female) κ = 0.7 for females and 0.9 for males; α = −0.329 for females and −0.411 for males
eGFR = 141 × min(Scr/κ) α × max(Scr/κ) −1.209 × 0.993 Age ×(如果是女性,则为 1.018)κ = 0.7女性为 0.9,男性为 0.9;女性 α = -0.329,男性 α = -0.411
ScysC-based equations 基于ScysC的方程
SchwartzcysC [6] 施瓦茨 cysC [ 6]eGFR = 40.6 (1.8/ScysC)0.93
eGFR = 40.6(1.8/ScysC) 0.93
CAPA [7] 卡帕 [7]eGFR = 130 × ScysC−1.069 × Age−0.117 – 7
eGFR = 130 × ScysC −1.069 × 年龄 −0.117 – 7
CKD-EPIcysc [4]eGFR = 133 × min(ScysC/0.8,1)−0.499 × max(ScysC/0.8,1)−1.328 × 0.996Age × (0.932 if female)
eGFR = 133 × min(ScysC/0.8,1) −0.499 × max(ScysC/0.8,1) −1.328 × 0.996 Age ×(女性为 0.932)
Combined equations 组合方程
CKD-EPIcrea,cysc [4]eGFR = 135 × min(Scr/κ,1)α × max(Scr/κ,1)−0.601 × min(ScysC/0.8,1)−0.375 × max(ScysC/0.8,1)−0.711 × 0.995Age × (0.969 if female) (κ = 0.7 for females, 0.9 for males, α  = −0.248 for females and −0.207 for males)
eGFR = 135 × min(Scr/κ,1) α × max(Scr/κ,1) −0.601 × min(ScysC/0.8,1) −0.375 × max(ScysC/0.8,1) −0.711 × 0.995 Age ×(女性为 0.969)(女性为 κ = 0.7,男性为 0.9,女性为 α = -0.248,男性-0.207)
BIS2 [8] 国际清算银行2 [8]eGFR = 767 × ScysC−0.61 × Scr−0.40 × Age−0.57 × (0.87 if female)
eGFR = 767 × ScysC −0.61 × Scr −0.40 × 年龄 −0.57 ×(如果是女性,则为 0.87)
Scr-based equations
Schwartzcrea [15]eGFR = 0.413 × Ht/Scr
CKD-EPI [16]eGFR = 141 × min(Scr/κ)α × max(Scr/κ)−1.209 × 0.993Age × (1.018 if female) κ = 0.7 for females and 0.9 for males; α = −0.329 for females and −0.411 for males
ScysC-based equations
SchwartzcysC [6]eGFR = 40.6 (1.8/ScysC)0.93
CAPA [7]eGFR = 130 × ScysC−1.069 × Age−0.117 – 7
CKD-EPIcysc [4]eGFR = 133 × min(ScysC/0.8,1)−0.499 × max(ScysC/0.8,1)−1.328 × 0.996Age × (0.932 if female)
Combined equations
CKD-EPIcrea,cysc [4]eGFR = 135 × min(Scr/κ,1)α × max(Scr/κ,1)−0.601 × min(ScysC/0.8,1)−0.375 × max(ScysC/0.8,1)−0.711 × 0.995Age × (0.969 if female) (κ = 0.7 for females, 0.9 for males, α  = −0.248 for females and −0.207 for males)
BIS2 [8]eGFR = 767 × ScysC−0.61 × Scr−0.40 × Age−0.57 × (0.87 if female)

Scr, serum creatinine (mg/dL); ScysC, serum cystatin C (mg/L); Ht, height in cm.
Scr, 血清肌酐(mg/dL); ScysC, 血清胱抑素C(mg/L); Ht,高度(厘米)。

Statistical analysis 统计分析

The performance statistics are presented as constant bias (mean of eGFR–mGFR) and proportional bias (mean of eGFR/mGFR), root mean square error (RMSE) of prediction, Lin’s concordance correlation coefficient (Lin’s CCC, which is a measure of both correlation and agreement as it evaluates the degree to which pairs of observations fall on the identity line), P10 and P30 (the percentage of subjects within 10% and 30% of mGFR), for the different age groups, total and in subgroups according to mGFR <60 and ≥60 mL/min/1.73 m2. McNemar’s test is used to compare P30 among equations.
性能统计数据以恒定偏差(eGFR–mGFR 的平均值)和比例偏差(eGFR/mGFR 的平均值)、预测均方根误差 (RMSE)、Lin 的一致性相关系数 (Lin 的 CCC,这是两者的度量) 的形式呈现。相关性和一致性,因为它评估了观察对落在同一线上的程度)、P10 和 P30(mGFR 10% 和 30% 范围内的受试者百分比),对于不同年龄组、总数和亚组,根据mGFR <60 且≥60 mL/min/1.73 m 2 。 McNemar 检验用于比较方程之间的 P30。

RESULTS 结果

Description of the cohorts
群组描述

Summary statistics for the patient characteristics of the 11 cohorts are given in Tables 4 and 5, and are described in Supplementary data.
表 4 和表 5 给出了 11 个队列的患者特征的汇总统计数据,并在补充数据中进行了描述。

Table 4

Patient characteristics in the different cohorts from young age to old age (mean ± SD) (in years)


表4 不同队列从年轻到老年的患者特征(平均值±±SD)(单位:年)
Data origin 数据来源nAgemGFR 肾小球滤过率ScrScysC 半胱氨酸C
Leuven (Belgium) 鲁汶(比利时)1148.8  ± 5.5 8.8 ± 5.589.2 ± 21.5 89.2±21.50.58 ± 0.36 0.58±0.361.00 ± 0.35 1.00±0.35
Lyon (France) 里昂(法国)25911.1  ± 3.6 11.1 ± 3.688.8 ± 33.5 88.8±33.50.68 ± 0.30 0.68±0.301.22 ± 0.43 1.22±0.43
Saint-Etienne (France) 圣艾蒂安(法国)20348.7  ± 10.3 48.7 ± 10.394.7 ± 24.4 94.7±24.40.87 ± 0.19 0.87±0.190.90 ± 0.26 0.90±0.26
Paris (France) 法国巴黎)60350.3  ± 13.1 50.3 ± 13.167.1 ± 27.2 67.1±27.21.29 ± 0.74 1.29±0.741.41 ± 0.81 1.41±0.81
Lyon (France) 里昂(法国)59854.6  ± 13.7 54.6 ± 13.774.9 ± 30.9 74.9±30.91.13 ± 0.57 1.13±0.571.24 ± 0.58 1.24±0.58
CRIC (USA) 审评委(美国)67456.9  ± 12.5 56.9 ± 12.550.7 ± 21.8 50.7±21.81.60 ± 0.50 1.60±0.501.43 ± 0.51 1.43±0.51
Tromsø (Norway) 特罗姆瑟(挪威)162758.1  ± 3.8 58.1 ± 3.891.7 ± 14.4 91.7±14.40.76 ± 0.14 0.76±0.140.73 ± 0.12 0.73±0.12
Rochester CKD (USA) 罗切斯特 CKD(美国)68764.8  ± 8.8 64.8 ± 8.880.4 ± 21.3 80.4±21.30.85 ± 0.23 0.85±0.230.87 ± 0.24 0.87±0.24
Rochester KFC (USA) 罗切斯特肯德基(美国)40665.9  ± 9.2 65.9 ± 9.279.5 ± 20.7 79.5±20.70.84 ± 0.18 0.84±0.180.83 ± 0.18 0.83±0.18
Berlin (Germany) 柏林,德国)56778.5  ± 6.2 78.5 ± 6.260.3 ± 16.4 60.3±16.40.99 ± 0.37 0.99±0.371.14 ± 0.38 1.14±0.38
Kent (UK) 肯特(英国)39480.4  ± 4.6 80.4 ± 4.651.5 ± 18.8 51.5±18.81.30 ± 0.66 1.30±0.661.45 ± 0.61 1.45±0.61
Total613258.2  ± 17.6 58.2 ± 17.675.5 ± 26.5 75.5±26.51.01 ± 0.51 1.01±0.511.06 ± 0.52 1.06±0.52
Data originnAgemGFRScrScysC
Leuven (Belgium)1148.8  ± 5.589.2 ± 21.50.58 ± 0.361.00 ± 0.35
Lyon (France)25911.1  ± 3.688.8 ± 33.50.68 ± 0.301.22 ± 0.43
Saint-Etienne (France)20348.7  ± 10.394.7 ± 24.40.87 ± 0.190.90 ± 0.26
Paris (France)60350.3  ± 13.167.1 ± 27.21.29 ± 0.741.41 ± 0.81
Lyon (France)59854.6  ± 13.774.9 ± 30.91.13 ± 0.571.24 ± 0.58
CRIC (USA)67456.9  ± 12.550.7 ± 21.81.60 ± 0.501.43 ± 0.51
Tromsø (Norway)162758.1  ± 3.891.7 ± 14.40.76 ± 0.140.73 ± 0.12
Rochester CKD (USA)68764.8  ± 8.880.4 ± 21.30.85 ± 0.230.87 ± 0.24
Rochester KFC (USA)40665.9  ± 9.279.5 ± 20.70.84 ± 0.180.83 ± 0.18
Berlin (Germany)56778.5  ± 6.260.3 ± 16.40.99 ± 0.371.14 ± 0.38
Kent (UK)39480.4  ± 4.651.5 ± 18.81.30 ± 0.661.45 ± 0.61
Total613258.2  ± 17.675.5 ± 26.51.01 ± 0.511.06 ± 0.52

n, number of patients; mGFR, measured glomerular filtration rate (mL/min/1.73 m2); Scr, serum creatinine (mg/dL); ScysC, serum cystatin C (mg/L).
n,患者人数; mGFR,测量的肾小球滤过率(mL/min/1.73m 2 ); Scr,血清肌酐(mg/dL); ScysC,血清胱抑素C(mg/L)。

Table 4

Patient characteristics in the different cohorts from young age to old age (mean ± SD) (in years)


表4 不同队列从年轻到老年的患者特征(平均值±±SD)(单位:年)
Data origin 数据来源nAgemGFR 肾小球滤过率ScrScysC 半胱氨酸C
Leuven (Belgium) 鲁汶(比利时)1148.8  ± 5.5 8.8 ± 5.589.2 ± 21.5 89.2±21.50.58 ± 0.36 0.58±0.361.00 ± 0.35 1.00±0.35
Lyon (France) 里昂(法国)25911.1  ± 3.6 11.1 ± 3.688.8 ± 33.5 88.8±33.50.68 ± 0.30 0.68±0.301.22 ± 0.43 1.22±0.43
Saint-Etienne (France) 圣艾蒂安(法国)20348.7  ± 10.3 48.7 ± 10.394.7 ± 24.4 94.7±24.40.87 ± 0.19 0.87±0.190.90 ± 0.26 0.90±0.26
Paris (France) 法国巴黎)60350.3  ± 13.1 50.3 ± 13.167.1 ± 27.2 67.1±27.21.29 ± 0.74 1.29±0.741.41 ± 0.81 1.41±0.81
Lyon (France) 里昂(法国)59854.6  ± 13.7 54.6 ± 13.774.9 ± 30.9 74.9±30.91.13 ± 0.57 1.13±0.571.24 ± 0.58 1.24±0.58
CRIC (USA) 审评委(美国)67456.9  ± 12.5 56.9 ± 12.550.7 ± 21.8 50.7±21.81.60 ± 0.50 1.60±0.501.43 ± 0.51 1.43±0.51
Tromsø (Norway) 特罗姆瑟(挪威)162758.1  ± 3.8 58.1 ± 3.891.7 ± 14.4 91.7±14.40.76 ± 0.14 0.76±0.140.73 ± 0.12 0.73±0.12
Rochester CKD (USA) 罗切斯特 CKD(美国)68764.8  ± 8.8 64.8 ± 8.880.4 ± 21.3 80.4±21.30.85 ± 0.23 0.85±0.230.87 ± 0.24 0.87±0.24
Rochester KFC (USA) 罗切斯特肯德基(美国)40665.9  ± 9.2 65.9 ± 9.279.5 ± 20.7 79.5±20.70.84 ± 0.18 0.84±0.180.83 ± 0.18 0.83±0.18
Berlin (Germany) 柏林,德国)56778.5  ± 6.2 78.5 ± 6.260.3 ± 16.4 60.3±16.40.99 ± 0.37 0.99±0.371.14 ± 0.38 1.14±0.38
Kent (UK) 肯特(英国)39480.4  ± 4.6 80.4 ± 4.651.5 ± 18.8 51.5±18.81.30 ± 0.66 1.30±0.661.45 ± 0.61 1.45±0.61
Total613258.2  ± 17.6 58.2 ± 17.675.5 ± 26.5 75.5±26.51.01 ± 0.51 1.01±0.511.06 ± 0.52 1.06±0.52
Data originnAgemGFRScrScysC
Leuven (Belgium)1148.8  ± 5.589.2 ± 21.50.58 ± 0.361.00 ± 0.35
Lyon (France)25911.1  ± 3.688.8 ± 33.50.68 ± 0.301.22 ± 0.43
Saint-Etienne (France)20348.7  ± 10.394.7 ± 24.40.87 ± 0.190.90 ± 0.26
Paris (France)60350.3  ± 13.167.1 ± 27.21.29 ± 0.741.41 ± 0.81
Lyon (France)59854.6  ± 13.774.9 ± 30.91.13 ± 0.571.24 ± 0.58
CRIC (USA)67456.9  ± 12.550.7 ± 21.81.60 ± 0.501.43 ± 0.51
Tromsø (Norway)162758.1  ± 3.891.7 ± 14.40.76 ± 0.140.73 ± 0.12
Rochester CKD (USA)68764.8  ± 8.880.4 ± 21.30.85 ± 0.230.87 ± 0.24
Rochester KFC (USA)40665.9  ± 9.279.5 ± 20.70.84 ± 0.180.83 ± 0.18
Berlin (Germany)56778.5  ± 6.260.3 ± 16.40.99 ± 0.371.14 ± 0.38
Kent (UK)39480.4  ± 4.651.5 ± 18.81.30 ± 0.661.45 ± 0.61
Total613258.2  ± 17.675.5 ± 26.51.01 ± 0.511.06 ± 0.52

n, number of patients; mGFR, measured glomerular filtration rate (mL/min/1.73 m2); Scr, serum creatinine (mg/dL); ScysC, serum cystatin C (mg/L).
n,患者人数; mGFR,测量的肾小球滤过率(mL/min/1.73m 2 ); Scr,血清肌酐(mg/dL); ScysC,血清胱抑素C(mg/L)。

Table 5

Patient characteristics in the different age groups (mean ± SD)


表5 不同年龄组患者特征(平均值±±SD)
Group 团体nNo. of males 男性人数No. of females 女性人数mGFR 肾小球滤过率ScrScysC 半胱氨酸C
Children ≤18 years ≤18岁儿童36819317589.2 ± 30.4 89.2±30.40.65 ± 0.31 0.65±0.311.15 ± 0.42 1.15±0.42
Adults 18–70 years 18-70 岁成人42952301199480.2 ± 25.6 80.2±25.61.00 ± 0.50 1.00±0.500.99 ± 0.51 0.99±0.51
Older adults ≥70 years ≥70岁的老年人146977169858.5 ± 20.0 58.5±20.01.13 ± 0.52 1.13±0.521.24 ± 0.51 1.24±0.51
Total613232652867
GroupnNo. of malesNo. of femalesmGFRScrScysC
Children ≤18 years36819317589.2 ± 30.40.65 ± 0.311.15 ± 0.42
Adults 18–70 years42952301199480.2 ± 25.61.00 ± 0.500.99 ± 0.51
Older adults ≥70 years146977169858.5 ± 20.01.13 ± 0.521.24 ± 0.51
Total613232652867

n, number of patients; mGFR, measured glomerular filtration rate (mL/min/1.73 m2); Scr, serum creatinine (mg/dL); ScysC, serum cystatin C (mg/L).
n,患者人数; mGFR,测量的肾小球滤过率(mL/min/1.73m 2 ); Scr,血清肌酐(mg/dL); ScysC,血清胱抑素C(mg/L)。

Table 5

Patient characteristics in the different age groups (mean ± SD)


表5 不同年龄组患者特征(平均值±±SD)
Group 团体nNo. of males 男性人数No. of females 女性人数mGFR 肾小球滤过率ScrScysC 半胱氨酸C
Children ≤18 years ≤18岁儿童36819317589.2 ± 30.4 89.2±30.40.65 ± 0.31 0.65±0.311.15 ± 0.42 1.15±0.42
Adults 18–70 years 18-70 岁成人42952301199480.2 ± 25.6 80.2±25.61.00 ± 0.50 1.00±0.500.99 ± 0.51 0.99±0.51
Older adults ≥70 years ≥70岁的老年人146977169858.5 ± 20.0 58.5±20.01.13 ± 0.52 1.13±0.521.24 ± 0.51 1.24±0.51
Total613232652867
GroupnNo. of malesNo. of femalesmGFRScrScysC
Children ≤18 years36819317589.2 ± 30.40.65 ± 0.311.15 ± 0.42
Adults 18–70 years42952301199480.2 ± 25.61.00 ± 0.500.99 ± 0.51
Older adults ≥70 years146977169858.5 ± 20.01.13 ± 0.521.24 ± 0.51
Total613232652867

n, number of patients; mGFR, measured glomerular filtration rate (mL/min/1.73 m2); Scr, serum creatinine (mg/dL); ScysC, serum cystatin C (mg/L).
n,患者人数; mGFR,测量的肾小球滤过率(mL/min/1.73m 2 ); Scr,血清肌酐(mg/dL); ScysC,血清胱抑素C(mg/L)。

Rationale for QcysC values for ScysC
ScysC 的 Q cysC 值的基本原理

To define normalization factors for ScysC we searched the literature for normal reference ranges and we investigated whether these ranges depend on age or gender differences. We realized that the literature before the year 2010 was based on non-standardized cystatin C assays, but, in general, ScysC is independent of age (up to age 70 years) and gender in children, adolescents and adults [14–16], although there might be small differences between sexes and races [17]. We used the value of 0.82 mg/L as the normalization factor, as it is the middle of the normal reference interval for children, adolescents and adults up to ∼70 years (and in line with the manufacturer’s information on reference ranges) [8, 18]. The ScysC-based CKD-EPI equation normalized ScysC by 0.80 for both males and females [4], a value that is close to the proposed value of 0.82 in this study. The new CAPA equation does not have a gender factor in the equation, suggesting that the same QcysC normalization constant can be used for both sexes [7]. For older adults, we could not find normal reference ranges in the literature. In our dataset of 1333 apparently healthy older persons aged >70 years from the Berlin Initiative Study, we modelled QcysC as a linear function of age: QcysC =0.01704 × Age – 0.3384 = 0.863 + 0.01704 × (Age – 70) (R2 =0.919; see Figure 1). At the age of 67.5 years, the corresponding value of QcysC =0.82. Therefore, we defined the QcysC normalization factor for cystatin C as 0.82 mg/L until the age of 70 years and then QcysC gradually (and linearly) increases (Figure 1). The mode of the ScysC distribution (Figure 2) of the n = 1333 apparently healthy subjects was 0.95 mg/L. Based on this analysis, and for the sake of simplicity, we fixed QcysC to 0.82 mg/L for all ages <70 years and to 0.95 mg/L for older ages.
为了定义 ScysC 的标准化因子,我们在文献中搜索了正常参考范围,并研究了这些范围是否取决于年龄或性别差异。我们意识到 2010 年之前的文献基于非标准化的半胱氨酸蛋白酶抑制剂 C 测定,但一般来说,ScysC 与儿童、青少年和成人的年龄(最高 70 岁)和性别无关[14-16],尽管性别和种族之间可能存在细微差异[17]。我们使用 0.82 mg/L 的值作为标准化因子,因为它是 70 岁以下儿童、青少年和成人正常参考区间的中间值(并且与制造商关于参考范围的信息一致)[8, 18]。基于 ScysC 的 CKD-EPI 方程将男性和女性的 ScysC 归一化为 0.80 [4],该值接近本研究中建议的 0.82 值。新的 CAPA 方程中没有性别因素,这表明相同的 Q cysC 归一化常数可用于两种性别 [7]。对于老年人,我们在文献中找不到正常的参考范围。在柏林倡议研究中 1333 名年龄 > 70 岁、表面健康的老年人的数据集中,我们将 Q cysC 建模为年龄的线性函数:Q cysC  =0.01704 × 年龄 – 0.3384 = 0.863 + 0.01704 ×(年龄 – 70)(R 2  =0.919;见图 1)。 67.5岁时,对应的Q cysC  值=0.82。因此,我们将胱抑素 C 的 Q cysC 标准化因子定义为 0.82 mg/L,直至 70 岁,然后 Q cysC 逐渐(线性)增加(图 1)。 n = 1333 名表面健康受试者的 ScysC 分布众数(图 2)为 0.95 mg/L。 基于此分析,为了简单起见,我们将所有年龄 <70 岁的 Q cysC 固定为 0.82 mg/L,将较大年龄的 Q cysC 固定为 0.95 mg/L。
FIGURE 1 图1
(a) The linear relationship between median (solid black circles) ScysC and age for the n = 1333 apparently healthy Berlin Initiative Study participants (grey circles). (b) Histogram for ScysC measurements of n = 1333 apparently healthy older adults.
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(a) The linear relationship between median (solid black circles) ScysC and age for the n = 1333 apparently healthy Berlin Initiative Study participants (grey circles). (b) Histogram for ScysC measurements of n = 1333 apparently healthy older adults.
(a) n = 1333 名表面健康的柏林倡议研究参与者(灰色圆圈)的中位数(实心黑色圆圈)ScysC 与年龄之间的线性关系。 (b) n = 1333 名表面健康的老年人的 ScysC 测量直方图。

FIGURE 2 图2
RMSE as a function of the weighting factor α for children [based on Q(height)], adults and older adults. The total RMSE for all n = 6132 measurements is also shown. For children the FAScysC equation has smaller RMSE than the FAScrea equation. For adults there is a slightly smaller RMSE for the single biomarker FAScrea equation compared with the single marker FAScysC equation. For older adults there is no real preference for the value of α. For all age groups the RMSE is minimal for α  ≈ 0.5 (= combined FAS equation).
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RMSE as a function of the weighting factor α for children [based on Q(height)], adults and older adults. The total RMSE for all n = 6132 measurements is also shown. For children the FAScysC equation has smaller RMSE than the FAScrea equation. For adults there is a slightly smaller RMSE for the single biomarker FAScrea equation compared with the single marker FAScysC equation. For older adults there is no real preference for the value of α. For all age groups the RMSE is minimal for α  ≈ 0.5 (= combined FAS equation).
RMSE 是儿童 [基于 Q(身高)]、成人和老年人的权重因子 α 的函数。还显示了所有 n = 6132 次测量的总 RMSE。对于儿童,FAS cysC 方程的 RMSE 小于 FAS crea 方程。对于成人,与单标记 FAS cysC 方程相比,单生物标记 FAS crea 方程的 RMSE 稍小。对于老年人来说,对于 α 的值没有真正的偏好。对于所有年龄组,RMSE 最小,α ≈ 0.5(= 组合 FAS 方程)。

Performance results of the different equations
不同方程的性能结果

The performance statistics for the three FAS equations for the different age groups are presented in Tables 6–8. The FAScrea equation [for children in two versions, based on Qcrea(age) and Qcrea(height)] is compared with the relevant Scr-based recommended equations (Schwartz for children, CKD-EPI for adults, BIS1 for older adults). The FAScysC equation is compared with the ScysC-based Schwartz equation (for children), the CAPA equation (for all ages) and ScysC-based CKD-EPI equation (for adults). Finally, the FAScombi equation is compared with the combined CKD-EPI equation (for adults and older adults) and the combined BIS2 equation for older adults. To our knowledge, there is no combined equation for children available yet (based on the certified reference material). Tables 6–8 are presented for all subjects within each age group, but also for subgroups according to the mGFR threshold of 60 mL/min/1.73 m2.
表 6-8 列出了不同年龄组的三个 FAS 方程的表现统计数据。 FAS crea 方程[针对两个版本的儿童,基于 Q crea (年龄)和 Q crea (身高)] 与相关 Scr-基于推荐方程(儿童为 Schwartz,成人为 CKD-EPI,老年人为 BIS1)。将 FAS cysC 方程与基于 ScysC 的 Schwartz 方程(针对儿童)、CAPA 方程(针对所有年龄段)和基于 ScysC 的 CKD-EPI 方程(针对成人)进行比较。最后,将 FAS combi 方程与组合 CKD-EPI 方程(针对成人和老年人)以及针对老年人的组合 BIS2 方程进行比较。据我们所知,目前还没有适用于儿童的组合方程(基于经过认证的参考材料)。表 6-8 列出了每个年龄组内的所有受试者,也列出了根据 60 mL/min/1.73 m 2 mGFR 阈值的亚组。

Table 6

Children n = 368 (age ≤18 years)


表6儿童n=368(年龄≤18岁)
Scr-based eGFR 基于 Scr 的 eGFRScysC-based eGFR 基于ScysC的eGFRCombined Scr-/ScysC-based eGFR
mGFR = 89.2 (n = 368)FAScreaFAScrea(Ht)*SchwartzcreaFAScysC*CAPASchwartzcysCFAScombi*FAScombi(Ht)*
eGFR – mGFR12.3 (7.7; 17.0)3.8 (0.9; 6.6)11.1 (8.1; 14.1)−5.1 (−7.2; −3.1)0.3 (−2.0; 2.6) 0.3(−2.0;2.6)−21.6 (−23.7; −19.6)0.9 (−0.9; 2.7)2.2 (−4.0; −0.4)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.17 (1.12; 1.21)1.06 (1.04; 1.09)1.15 (1.12; 1.18)0.98 (0.96; 1.01)1.03 (1.00; 1.05)0.79 (0.78; 0.81)1.05 (1.03; 1.07)1.01 (0.99; 1.03)
RMSE47.0 (27.2; 67.6)28.3 (11.4; 39.2)31.3 (13.9; 42.9)20.4 (17.9; 22.5)22.3 (20.0; 24.3)29.6 (27.0; 32.1)17.5 (15.1; 19.7)17.6 (15.5; 19.7)
Lin’s CCC 林氏CCC0.43 (0.36; 0.49)0.65 (0.59; 0.70)0.63 (0.57; 0.68)0.73 (0.68; 0.77)0.74 (0.68; 0.78)0.49 (0.44; 0.54)0.81 (0.77; 0.84)0.80 (0.77; 0.84)
P10 (%) P10(%)32.3 (27.5; 37.1)42.7 (37.6; 47.7)40.5 (35.5; 45.5)40.5 (35.5; 45.5)36.4 (31.5; 41.4)16.0 (12.3; 19.8)44.6 (39.5; 49.7)43.2 (38.1; 48.3)
P30 (%) P30(%)78.3 (74.0; 82.5)84.5 (80.8; 88.2)79.9 (75.8; 84.0)86.1 (82.6; 89.7)76.6 (72.3; 81.0)68.8 (64.4; 73.5)90.8 (87.8; 93.7)92.1 (89.4; 94.9)
mGFR <60 mL/min/1.73 m2 (n = 57)
mGFR = 45.2 肾小球滤过率=45.2FAScreaFAScrea(Ht)*SchwartzcreaFAScysCCAPASchwartzcysC*FAScombiFAScombi (Ht)*
eGFR – mGFR12.5 (10.0; 15.1)5.1 (3.0; 7.2)8.8 (6.5; 11.0)6.2 (3.1; 9.3)3.3 (−0.4; 7.1) 3.3(−0.4;7.1)2.4 (−5.0; 0.2)8.3 (6.2; 10.5)5.0 (2.9; 7.1)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.31 (1.24; 1.37)1.14 (1.08; 1.20)1.22 (1.16; 1.29)1.17 (1.09; 1.25)1.10 (1.01; 1.19)0.98 (0.91; 1.04)1.21 (1.15; 1.27)1.14 (1.08; 1.20)
RMSE15.8 (12.7; 18.4)9.4 (7.5; 10.9)12.2 (9.7; 14.2)13.1 (9.8; 15.7)14.3 (10.4; 17.3)10.0 (7.5; 12.0)11.6 (9.4; 13.5)9.3 (7.3; 11.0)
Lin’s CCC 林氏CCC0.44 (0.29; 0.56)0.66 (0.50; 0.77)0.55 (0.40; 0.68)0.48 (0.29; 0.64)0.47 (0.28; 0.63)0.55 (0.35; 0.71)0.56 (0.41; 0.69)0.65 (0.49; 0.77)
P10 (%) P10(%)10.5 (2.3; 18.7)45.6 (32.3; 58.9)36.8 (23.9; 49.8)24.6 (13.0; 36.1)29.8 (17.6; 42.1)36.8 (23.9; 49.8)28.1 (16.0; 40.1)38.6 (25.6; 51.6)
P30 (%) P30(%)63.2 (50.2; 76.1)71.9 (59.9; 84.0)71.9 (59.9; 84.0)68.4 (56.0; 80.9)66.7 (54.0; 79.3)86.0 (76.7; 95.3)71.9 (59.9; 84.0)80.7 (70.1; 91.3)
mGFR ≥60 mL/min/1.73 m2 (n = 311)
mGFR = 97.3 肾小球滤过率=97.3FAScreaFAScrea(Ht)*SchwartzcreaFAScysC*CAPASchwartzcysCFAScombi*FAScombi (Ht)*
eGFR – mGFR12.3 (6.8; 17.8)3.5 (0.1; 6.9)11.5 (8.0; 15.1)−7.2 (−9.5; −5.0)0.2 (−2.9; 2.4) −0.2(−2.9;2.4)−25.2 (−27.4; −23.0)0.5 (−2.5; 1.6)−3.5 (−5.6; −1.4)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.14 (1.09; 1.19)1.05 (1.02; 1.08)1.13 (1.10; 1.17)0.95 (0.93; 0.97)1.02 (0.99; 1.04)0.76 (0.74; 0.78)1.01 (0.99; 1.03)0.98 (0.96; 1.00)
RMSE50.7 (10.5; 77.5)30.5 (10.1; 42.0)33.7 (12.7; 45.9)21.5 (18.8; 23.8)23.5 (21.0; 25.8)32.0 (29.1; 34.5)18.4 (13.5; 20.8)18.8 (16.3; 21.0)
Lin’s CCC 林氏CCC0.29 (0.22; 0.37)0.50 (0.41; 0.57)0.48 (0.40; 0.55)0.59 (0.52; 0.65)0.60 (0.52; 0.66)0.33 (0.27; 0.38)0.71 (0.65; 0.76)0.69 (0.62; 0.74)
P10 (%) P10(%)36.3 (31.0; 41.7)42.1 (36.6; 47.6)41.2 (35.7; 46.7)43.4 (37.9; 48.9)37.6 (32.2; 43.0)12.2 (8.6; 15.9)47.6 (42.0; 53.2)44.1 (38.5; 49.6)
P30 (%) P30(%)81.0 (76.6; 85.4)86.8 (83.0; 90.6)81.4 (77.0; 85.7)89.4 (85.9; 92.8)78.5 (73.9; 83.1)65.6 (60.3; 70.9)94.2 (91.6; 96.8)94.2 (91.6; 96.8)
Scr-based eGFRScysC-based eGFRCombined Scr-/ScysC-based eGFR
mGFR = 89.2 (n = 368)FAScreaFAScrea(Ht)*SchwartzcreaFAScysC*CAPASchwartzcysCFAScombi*FAScombi(Ht)*
eGFR – mGFR12.3 (7.7; 17.0)3.8 (0.9; 6.6)11.1 (8.1; 14.1)−5.1 (−7.2; −3.1)0.3 (−2.0; 2.6)−21.6 (−23.7; −19.6)0.9 (−0.9; 2.7)2.2 (−4.0; −0.4)
eGFR/mGFR1.17 (1.12; 1.21)1.06 (1.04; 1.09)1.15 (1.12; 1.18)0.98 (0.96; 1.01)1.03 (1.00; 1.05)0.79 (0.78; 0.81)1.05 (1.03; 1.07)1.01 (0.99; 1.03)
RMSE47.0 (27.2; 67.6)28.3 (11.4; 39.2)31.3 (13.9; 42.9)20.4 (17.9; 22.5)22.3 (20.0; 24.3)29.6 (27.0; 32.1)17.5 (15.1; 19.7)17.6 (15.5; 19.7)
Lin’s CCC0.43 (0.36; 0.49)0.65 (0.59; 0.70)0.63 (0.57; 0.68)0.73 (0.68; 0.77)0.74 (0.68; 0.78)0.49 (0.44; 0.54)0.81 (0.77; 0.84)0.80 (0.77; 0.84)
P10 (%)32.3 (27.5; 37.1)42.7 (37.6; 47.7)40.5 (35.5; 45.5)40.5 (35.5; 45.5)36.4 (31.5; 41.4)16.0 (12.3; 19.8)44.6 (39.5; 49.7)43.2 (38.1; 48.3)
P30 (%)78.3 (74.0; 82.5)84.5 (80.8; 88.2)79.9 (75.8; 84.0)86.1 (82.6; 89.7)76.6 (72.3; 81.0)68.8 (64.4; 73.5)90.8 (87.8; 93.7)92.1 (89.4; 94.9)
mGFR <60 mL/min/1.73 m2 (n = 57)
mGFR = 45.2FAScreaFAScrea(Ht)*SchwartzcreaFAScysCCAPASchwartzcysC*FAScombiFAScombi (Ht)*
eGFR – mGFR12.5 (10.0; 15.1)5.1 (3.0; 7.2)8.8 (6.5; 11.0)6.2 (3.1; 9.3)3.3 (−0.4; 7.1)2.4 (−5.0; 0.2)8.3 (6.2; 10.5)5.0 (2.9; 7.1)
eGFR/mGFR1.31 (1.24; 1.37)1.14 (1.08; 1.20)1.22 (1.16; 1.29)1.17 (1.09; 1.25)1.10 (1.01; 1.19)0.98 (0.91; 1.04)1.21 (1.15; 1.27)1.14 (1.08; 1.20)
RMSE15.8 (12.7; 18.4)9.4 (7.5; 10.9)12.2 (9.7; 14.2)13.1 (9.8; 15.7)14.3 (10.4; 17.3)10.0 (7.5; 12.0)11.6 (9.4; 13.5)9.3 (7.3; 11.0)
Lin’s CCC0.44 (0.29; 0.56)0.66 (0.50; 0.77)0.55 (0.40; 0.68)0.48 (0.29; 0.64)0.47 (0.28; 0.63)0.55 (0.35; 0.71)0.56 (0.41; 0.69)0.65 (0.49; 0.77)
P10 (%)10.5 (2.3; 18.7)45.6 (32.3; 58.9)36.8 (23.9; 49.8)24.6 (13.0; 36.1)29.8 (17.6; 42.1)36.8 (23.9; 49.8)28.1 (16.0; 40.1)38.6 (25.6; 51.6)
P30 (%)63.2 (50.2; 76.1)71.9 (59.9; 84.0)71.9 (59.9; 84.0)68.4 (56.0; 80.9)66.7 (54.0; 79.3)86.0 (76.7; 95.3)71.9 (59.9; 84.0)80.7 (70.1; 91.3)
mGFR ≥60 mL/min/1.73 m2 (n = 311)
mGFR = 97.3FAScreaFAScrea(Ht)*SchwartzcreaFAScysC*CAPASchwartzcysCFAScombi*FAScombi (Ht)*
eGFR – mGFR12.3 (6.8; 17.8)3.5 (0.1; 6.9)11.5 (8.0; 15.1)−7.2 (−9.5; −5.0)0.2 (−2.9; 2.4)−25.2 (−27.4; −23.0)0.5 (−2.5; 1.6)−3.5 (−5.6; −1.4)
eGFR/mGFR1.14 (1.09; 1.19)1.05 (1.02; 1.08)1.13 (1.10; 1.17)0.95 (0.93; 0.97)1.02 (0.99; 1.04)0.76 (0.74; 0.78)1.01 (0.99; 1.03)0.98 (0.96; 1.00)
RMSE50.7 (10.5; 77.5)30.5 (10.1; 42.0)33.7 (12.7; 45.9)21.5 (18.8; 23.8)23.5 (21.0; 25.8)32.0 (29.1; 34.5)18.4 (13.5; 20.8)18.8 (16.3; 21.0)
Lin’s CCC0.29 (0.22; 0.37)0.50 (0.41; 0.57)0.48 (0.40; 0.55)0.59 (0.52; 0.65)0.60 (0.52; 0.66)0.33 (0.27; 0.38)0.71 (0.65; 0.76)0.69 (0.62; 0.74)
P10 (%)36.3 (31.0; 41.7)42.1 (36.6; 47.6)41.2 (35.7; 46.7)43.4 (37.9; 48.9)37.6 (32.2; 43.0)12.2 (8.6; 15.9)47.6 (42.0; 53.2)44.1 (38.5; 49.6)
P30 (%)81.0 (76.6; 85.4)86.8 (83.0; 90.6)81.4 (77.0; 85.7)89.4 (85.9; 92.8)78.5 (73.9; 83.1)65.6 (60.3; 70.9)94.2 (91.6; 96.8)94.2 (91.6; 96.8)

Asterisks indicate the best performing equation(s) [13] within the same biomarker category, across all performance statistics. The bold values are the best result(s) for each performance statistic, across all equations. FAS, full-age-spectrum eGFR equation, based on Q(age); FAS(Ht), FAS equation based on Q(height); Schwartz, Schwartz equation for children (Scr-based = 0.413 Ht/Scr; cystatin C-based = 70.1 ScysC 0.93). FAScombi is calculated for α = 0.5.
星号表示同一生物标志物类别中所有性能统计数据中性能最佳的方程[13]。粗体值是所有方程中每个性能统计数据的最佳结果。 FAS,全年龄谱 eGFR 方程,基于 Q(年龄); FAS(Ht),基于Q(高度)的FAS方程; Schwartz,儿童 Schwartz 方程(基于 Scr = 0.413 Ht/Scr;基于胱抑素 C = 70.1 ScysC 0.93 )。 FAS combi 是在 α = 0.5 时计算的。

Table 6

Children n = 368 (age ≤18 years)


表6儿童n=368(年龄≤18岁)
Scr-based eGFR 基于 Scr 的 eGFRScysC-based eGFR 基于ScysC的eGFRCombined Scr-/ScysC-based eGFR
基于 Scr/ScysC 的组合 eGFR

mGFR = 89.2 (n = 368)FAScreaFAScrea(Ht)*SchwartzcreaFAScysC*CAPASchwartzcysCFAScombi*FAScombi(Ht)*
eGFR – mGFR12.3 (7.7; 17.0)3.8 (0.9; 6.6)11.1 (8.1; 14.1)−5.1 (−7.2; −3.1)0.3 (−2.0; 2.6) 0.3(−2.0;2.6)−21.6 (−23.7; −19.6)0.9 (−0.9; 2.7) 0.9(−0.9;2.7)2.2 (−4.0; −0.4)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.17 (1.12; 1.21)1.06 (1.04; 1.09)1.15 (1.12; 1.18)0.98 (0.96; 1.01)1.03 (1.00; 1.05)0.79 (0.78; 0.81)1.05 (1.03; 1.07)1.01 (0.99; 1.03)
RMSE47.0 (27.2; 67.6)28.3 (11.4; 39.2)31.3 (13.9; 42.9)20.4 (17.9; 22.5)22.3 (20.0; 24.3)29.6 (27.0; 32.1)17.5 (15.1; 19.7)17.6 (15.5; 19.7)
Lin’s CCC 林氏CCC0.43 (0.36; 0.49)0.65 (0.59; 0.70)0.63 (0.57; 0.68)0.73 (0.68; 0.77)0.74 (0.68; 0.78)0.49 (0.44; 0.54)0.81 (0.77; 0.84)0.80 (0.77; 0.84)
P10 (%) P10(%)32.3 (27.5; 37.1)42.7 (37.6; 47.7)40.5 (35.5; 45.5)40.5 (35.5; 45.5)36.4 (31.5; 41.4)16.0 (12.3; 19.8)44.6 (39.5; 49.7)43.2 (38.1; 48.3)
P30 (%) P30(%)78.3 (74.0; 82.5)84.5 (80.8; 88.2)79.9 (75.8; 84.0)86.1 (82.6; 89.7)76.6 (72.3; 81.0)68.8 (64.4; 73.5)90.8 (87.8; 93.7)92.1 (89.4; 94.9)
mGFR <60 mL/min/1.73 m2 (n = 57)
mGFR = 45.2 肾小球滤过率=45.2FAScreaFAScrea(Ht)*SchwartzcreaFAScysCCAPASchwartzcysC* 施瓦茨 cysC *FAScombiFAScombi (Ht)*
eGFR – mGFR12.5 (10.0; 15.1)5.1 (3.0; 7.2)8.8 (6.5; 11.0)6.2 (3.1; 9.3)3.3 (−0.4; 7.1) 3.3(−0.4;7.1)2.4 (−5.0; 0.2)8.3 (6.2; 10.5)5.0 (2.9; 7.1)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.31 (1.24; 1.37)1.14 (1.08; 1.20)1.22 (1.16; 1.29)1.17 (1.09; 1.25)1.10 (1.01; 1.19)0.98 (0.91; 1.04)1.21 (1.15; 1.27)1.14 (1.08; 1.20)
RMSE15.8 (12.7; 18.4)9.4 (7.5; 10.9)12.2 (9.7; 14.2)13.1 (9.8; 15.7)14.3 (10.4; 17.3)10.0 (7.5; 12.0)11.6 (9.4; 13.5)9.3 (7.3; 11.0)
Lin’s CCC 林氏CCC0.44 (0.29; 0.56)0.66 (0.50; 0.77)0.55 (0.40; 0.68)0.48 (0.29; 0.64)0.47 (0.28; 0.63)0.55 (0.35; 0.71)0.56 (0.41; 0.69)0.65 (0.49; 0.77)
P10 (%) P10(%)10.5 (2.3; 18.7)45.6 (32.3; 58.9)36.8 (23.9; 49.8)24.6 (13.0; 36.1)29.8 (17.6; 42.1)36.8 (23.9; 49.8)28.1 (16.0; 40.1)38.6 (25.6; 51.6)
P30 (%) P30(%)63.2 (50.2; 76.1)71.9 (59.9; 84.0)71.9 (59.9; 84.0)68.4 (56.0; 80.9)66.7 (54.0; 79.3)86.0 (76.7; 95.3)71.9 (59.9; 84.0)80.7 (70.1; 91.3)
mGFR ≥60 mL/min/1.73 m2 (n = 311)
mGFR = 97.3 肾小球滤过率=97.3FAScreaFAScrea(Ht)*SchwartzcreaFAScysC*CAPASchwartzcysCFAScombi*FAScombi (Ht)*
eGFR – mGFR12.3 (6.8; 17.8)3.5 (0.1; 6.9)11.5 (8.0; 15.1)−7.2 (−9.5; −5.0)0.2 (−2.9; 2.4) −0.2(−2.9;2.4)−25.2 (−27.4; −23.0) −25.2(−27.4;−23.0)0.5 (−2.5; 1.6) −0.5(−2.5;1.6)−3.5 (−5.6; −1.4)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.14 (1.09; 1.19)1.05 (1.02; 1.08)1.13 (1.10; 1.17)0.95 (0.93; 0.97)1.02 (0.99; 1.04)0.76 (0.74; 0.78)1.01 (0.99; 1.03)0.98 (0.96; 1.00)
RMSE50.7 (10.5; 77.5)30.5 (10.1; 42.0)33.7 (12.7; 45.9)21.5 (18.8; 23.8)23.5 (21.0; 25.8)32.0 (29.1; 34.5)18.4 (13.5; 20.8)18.8 (16.3; 21.0)
Lin’s CCC 林氏CCC0.29 (0.22; 0.37)0.50 (0.41; 0.57)0.48 (0.40; 0.55)0.59 (0.52; 0.65)0.60 (0.52; 0.66)0.33 (0.27; 0.38)0.71 (0.65; 0.76)0.69 (0.62; 0.74)
P10 (%) P10(%)36.3 (31.0; 41.7)42.1 (36.6; 47.6)41.2 (35.7; 46.7)43.4 (37.9; 48.9)37.6 (32.2; 43.0)12.2 (8.6; 15.9)47.6 (42.0; 53.2)44.1 (38.5; 49.6)
P30 (%) P30(%)81.0 (76.6; 85.4)86.8 (83.0; 90.6)81.4 (77.0; 85.7)89.4 (85.9; 92.8)78.5 (73.9; 83.1)65.6 (60.3; 70.9)94.2 (91.6; 96.8)94.2 (91.6; 96.8)
Scr-based eGFRScysC-based eGFRCombined Scr-/ScysC-based eGFR
mGFR = 89.2 (n = 368)FAScreaFAScrea(Ht)*SchwartzcreaFAScysC*CAPASchwartzcysCFAScombi*FAScombi(Ht)*
eGFR – mGFR12.3 (7.7; 17.0)3.8 (0.9; 6.6)11.1 (8.1; 14.1)−5.1 (−7.2; −3.1)0.3 (−2.0; 2.6)−21.6 (−23.7; −19.6)0.9 (−0.9; 2.7)2.2 (−4.0; −0.4)
eGFR/mGFR1.17 (1.12; 1.21)1.06 (1.04; 1.09)1.15 (1.12; 1.18)0.98 (0.96; 1.01)1.03 (1.00; 1.05)0.79 (0.78; 0.81)1.05 (1.03; 1.07)1.01 (0.99; 1.03)
RMSE47.0 (27.2; 67.6)28.3 (11.4; 39.2)31.3 (13.9; 42.9)20.4 (17.9; 22.5)22.3 (20.0; 24.3)29.6 (27.0; 32.1)17.5 (15.1; 19.7)17.6 (15.5; 19.7)
Lin’s CCC0.43 (0.36; 0.49)0.65 (0.59; 0.70)0.63 (0.57; 0.68)0.73 (0.68; 0.77)0.74 (0.68; 0.78)0.49 (0.44; 0.54)0.81 (0.77; 0.84)0.80 (0.77; 0.84)
P10 (%)32.3 (27.5; 37.1)42.7 (37.6; 47.7)40.5 (35.5; 45.5)40.5 (35.5; 45.5)36.4 (31.5; 41.4)16.0 (12.3; 19.8)44.6 (39.5; 49.7)43.2 (38.1; 48.3)
P30 (%)78.3 (74.0; 82.5)84.5 (80.8; 88.2)79.9 (75.8; 84.0)86.1 (82.6; 89.7)76.6 (72.3; 81.0)68.8 (64.4; 73.5)90.8 (87.8; 93.7)92.1 (89.4; 94.9)
mGFR <60 mL/min/1.73 m2 (n = 57)
mGFR = 45.2FAScreaFAScrea(Ht)*SchwartzcreaFAScysCCAPASchwartzcysC*FAScombiFAScombi (Ht)*
eGFR – mGFR12.5 (10.0; 15.1)5.1 (3.0; 7.2)8.8 (6.5; 11.0)6.2 (3.1; 9.3)3.3 (−0.4; 7.1)2.4 (−5.0; 0.2)8.3 (6.2; 10.5)5.0 (2.9; 7.1)
eGFR/mGFR1.31 (1.24; 1.37)1.14 (1.08; 1.20)1.22 (1.16; 1.29)1.17 (1.09; 1.25)1.10 (1.01; 1.19)0.98 (0.91; 1.04)1.21 (1.15; 1.27)1.14 (1.08; 1.20)
RMSE15.8 (12.7; 18.4)9.4 (7.5; 10.9)12.2 (9.7; 14.2)13.1 (9.8; 15.7)14.3 (10.4; 17.3)10.0 (7.5; 12.0)11.6 (9.4; 13.5)9.3 (7.3; 11.0)
Lin’s CCC0.44 (0.29; 0.56)0.66 (0.50; 0.77)0.55 (0.40; 0.68)0.48 (0.29; 0.64)0.47 (0.28; 0.63)0.55 (0.35; 0.71)0.56 (0.41; 0.69)0.65 (0.49; 0.77)
P10 (%)10.5 (2.3; 18.7)45.6 (32.3; 58.9)36.8 (23.9; 49.8)24.6 (13.0; 36.1)29.8 (17.6; 42.1)36.8 (23.9; 49.8)28.1 (16.0; 40.1)38.6 (25.6; 51.6)
P30 (%)63.2 (50.2; 76.1)71.9 (59.9; 84.0)71.9 (59.9; 84.0)68.4 (56.0; 80.9)66.7 (54.0; 79.3)86.0 (76.7; 95.3)71.9 (59.9; 84.0)80.7 (70.1; 91.3)
mGFR ≥60 mL/min/1.73 m2 (n = 311)
mGFR = 97.3FAScreaFAScrea(Ht)*SchwartzcreaFAScysC*CAPASchwartzcysCFAScombi*FAScombi (Ht)*
eGFR – mGFR12.3 (6.8; 17.8)3.5 (0.1; 6.9)11.5 (8.0; 15.1)−7.2 (−9.5; −5.0)0.2 (−2.9; 2.4)−25.2 (−27.4; −23.0)0.5 (−2.5; 1.6)−3.5 (−5.6; −1.4)
eGFR/mGFR1.14 (1.09; 1.19)1.05 (1.02; 1.08)1.13 (1.10; 1.17)0.95 (0.93; 0.97)1.02 (0.99; 1.04)0.76 (0.74; 0.78)1.01 (0.99; 1.03)0.98 (0.96; 1.00)
RMSE50.7 (10.5; 77.5)30.5 (10.1; 42.0)33.7 (12.7; 45.9)21.5 (18.8; 23.8)23.5 (21.0; 25.8)32.0 (29.1; 34.5)18.4 (13.5; 20.8)18.8 (16.3; 21.0)
Lin’s CCC0.29 (0.22; 0.37)0.50 (0.41; 0.57)0.48 (0.40; 0.55)0.59 (0.52; 0.65)0.60 (0.52; 0.66)0.33 (0.27; 0.38)0.71 (0.65; 0.76)0.69 (0.62; 0.74)
P10 (%)36.3 (31.0; 41.7)42.1 (36.6; 47.6)41.2 (35.7; 46.7)43.4 (37.9; 48.9)37.6 (32.2; 43.0)12.2 (8.6; 15.9)47.6 (42.0; 53.2)44.1 (38.5; 49.6)
P30 (%)81.0 (76.6; 85.4)86.8 (83.0; 90.6)81.4 (77.0; 85.7)89.4 (85.9; 92.8)78.5 (73.9; 83.1)65.6 (60.3; 70.9)94.2 (91.6; 96.8)94.2 (91.6; 96.8)

Asterisks indicate the best performing equation(s) [13] within the same biomarker category, across all performance statistics. The bold values are the best result(s) for each performance statistic, across all equations. FAS, full-age-spectrum eGFR equation, based on Q(age); FAS(Ht), FAS equation based on Q(height); Schwartz, Schwartz equation for children (Scr-based = 0.413 Ht/Scr; cystatin C-based = 70.1 ScysC 0.93). FAScombi is calculated for α = 0.5.
星号表示同一生物标志物类别中所有性能统计数据中性能最佳的方程[13]。粗体值是所有方程中每个性能统计数据的最佳结果。 FAS,全年龄谱 eGFR 方程,基于 Q(年龄); FAS(Ht),基于Q(高度)的FAS方程; Schwartz,儿童 Schwartz 方程(基于 Scr = 0.413 Ht/Scr;基于胱抑素 C = 70.1 ScysC 0.93 )。 FAS combi 是在 α = 0.5 时计算的。

Table 7

Adults n = 4295 (age 18–70 years)


表 7 成人 n = 4295(年龄 18-70 岁)
Scr-based eGFR 基于 Scr 的 eGFRScysC-based eGFR 基于ScysC的eGFRCombined Scr-/ScysC-based eGFR
基于 Scr/ScysC 的组合 eGFR
mGFR = 80.1 (n = 4295)FAScrea*CKD-EPIcrea*FAScysC*CKD-EPIcysCCAPAFAScombi*CKD-EPIcombi
eGFR – mGFR1.4 (0.9; 1.9)2.4 (1.9; 2.8)4.2 (3.7; 4.8)8.0 (7.6; 8.5)8.9 (8.3; 9.5)1.9 (1.5; 2.4)6.2 (5.8; 6.6)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.05 (1.04; 1.06)1.06 (1.05; 1.07)1.08 (1.07; 1.09)1.11 (1.10; 1.12)1.12 (1.11; 1.13)1.05 (1.04; 1.06)1.09 (1.08; 1.10)
RMSE16.0 (15.4; 16.6)15.1 (14.6; 15.6)17.7 (17.2; 18.2)18.1 (17.6; 18.5)21.3 (20.8; 21.8)14.1 (13.6; 14.6)15.3 (14.9; 15.8)
Lin’s CCC 林氏CCC0.80 (0.79; 0.81)0.82 (0.81; 0.83)0.78 (0.76; 0.79)0.78 (0.77; 0.80)0.73 (0.72; 0.74)0.84 (0.83; 0.85)0.83 (0.82; 0.84)
P10 (%) P10(%)43.6 (42.1; 45.1)46.0 (44.5; 47.5)37.6 (36.2; 39.1)32.5 (31.1; 34.0)31.5 (30.1; 32.9)47.3 (45.8; 48.8)41.0 (39.6; 42.5)
P30 (%) P30(%)87.6 (86.6; 88.6)88.1 (87.1; 89.0)82.6 (81.4; 83.7)80.4 (79.3; 81.6)75.6 (74.3; 76.9)89.9 (89.0; 90.8)88.2 (87.2; 89.1)
mGFR <60 mL/min/1.73 m2 (n = 925)
mGFR = 42.0 肾小球滤过率=42.0FAScrea*CKD-EPIcrea*FAScysCCKD-EPIcysC*CAPAFAScombiCKD-EPIcombi*
eGFR – mGFR7.0 (6.2; 7.9)5.9 (5.0; 6.9)6.9 (6.1; 7.8)4.9 (3.9; 5.9)5.3 (4.3; 6.2)6.3 (5.5; 7.0)4.2 (3.4; 5.1)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.20 (1.18; 1.23)1.16 (1.13; 1.18)1.19 (1.17; 1.22)1.11 (1.09; 1.14)1.12 (1.10; 1.15)1.18 (1.16; 1.20)1.10 (1.08; 1.12)
RMSE15.4 (14.0; 16.6)16.1 (14.8; 17.2)14.8 (13.7; 15.8)16.2 (14.9; 17.5)16.0 (14.7; 17.3)13.2 (12.1; 14.2)14.4 (13.1; 15.6)
Lin’s CCC 林氏CCC0.54 (0.50; 0.57)0.55 (0.51; 0.58)0.56 (0.52; 0.60)0.57 (0.53; 0.60)0.57 (0.53; 0.60)0.61 (0.58; 0.65)0.62 (0.58; 0.65)
P10 (%) P10(%)31.5 (28.5; 34.5)31.7 (28.7; 34.7)27.9 (25.0; 30.8)29.0 (26.0; 31.9)28.5 (25.6; 31.5)33.8 (30.8; 36.9)34.4 (31.3; 37.4)
P30 (%) P30(%)70.8 (67.9; 73.7)72.3 (69.4; 75.2)68.0 (65.0; 71.0)70.5 (67.5; 73.4)70.9 (68.0; 73.9)75.2 (72.5; 78.0)79.0 (76.4; 81.7)
mGFR ≥60 mL/min/1.73 m2 (n = 3370)
mGFR = 90.6 肾小球滤过率=90.6FAScrea*CKD-EPIcrea*FAScysC*CKD-EPIcysCCAPAFAScombi*CKD-EPIcombi
eGFR – mGFR0.1 (−0.7; 0.4) −0.1(−0.7;0.4)1.4 (0.9; 1.9)3.5 (2.9; 4.1)8.9 (8.4; 9.5)9.9 (9.2; 10.6)0.8 (0.3; 1.2)6.7 (6.3; 7.2)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.01 (1.00; 1.01)1.03 (1.03; 1.04)1.05 (1.04; 1.06)1.11 (1.11; 1.12)1.12 (1.11; 1.13)1.02 (1.01; 1.02)1.09 (1.08; 1.09)
RMSE16.2 (15.5; 16.8)14.8 (14.2; 15.3)18.4 (17.8; 18.9)18.6 (18.1; 19.0)22.5 (21.9; 23.1)14.3 (13.8; 14.9)15.6 (15.1; 16.0)
Lin’s CCC 林氏CCC0.59 (0.57; 0.61)0.57 (0.54; 0.59)0.51 (0.49; 0.54)0.48 (0.46; 0.50)0.42 (0.40; 0.45)0.64 (0.62; 0.66)0.58 (0.56; 0.60)
P10 (%) P10(%)47.0 (45.3; 48.7)49.9 (48.2; 51.6)40.3 (38.6; 41.9)33.5 (31.9; 35.1)32.3 (30.7; 33.9)50.9 (49.3; 52.6)42.9 (41.2; 44.6)
P30 (%) P30(%)92.2 (91.3; 93.1)92.4 (91.5; 93.3)86.6 (85.4; 87.7)83.2 (91.9; 84.4)76.9 (75.4; 78.3)93.9 (93.1; 94.8)90.7 (89.7; 91.7)
Scr-based eGFRScysC-based eGFRCombined Scr-/ScysC-based eGFR
mGFR = 80.1 (n = 4295)FAScrea*CKD-EPIcrea*FAScysC*CKD-EPIcysCCAPAFAScombi*CKD-EPIcombi
eGFR – mGFR1.4 (0.9; 1.9)2.4 (1.9; 2.8)4.2 (3.7; 4.8)8.0 (7.6; 8.5)8.9 (8.3; 9.5)1.9 (1.5; 2.4)6.2 (5.8; 6.6)
eGFR/mGFR1.05 (1.04; 1.06)1.06 (1.05; 1.07)1.08 (1.07; 1.09)1.11 (1.10; 1.12)1.12 (1.11; 1.13)1.05 (1.04; 1.06)1.09 (1.08; 1.10)
RMSE16.0 (15.4; 16.6)15.1 (14.6; 15.6)17.7 (17.2; 18.2)18.1 (17.6; 18.5)21.3 (20.8; 21.8)14.1 (13.6; 14.6)15.3 (14.9; 15.8)
Lin’s CCC0.80 (0.79; 0.81)0.82 (0.81; 0.83)0.78 (0.76; 0.79)0.78 (0.77; 0.80)0.73 (0.72; 0.74)0.84 (0.83; 0.85)0.83 (0.82; 0.84)
P10 (%)43.6 (42.1; 45.1)46.0 (44.5; 47.5)37.6 (36.2; 39.1)32.5 (31.1; 34.0)31.5 (30.1; 32.9)47.3 (45.8; 48.8)41.0 (39.6; 42.5)
P30 (%)87.6 (86.6; 88.6)88.1 (87.1; 89.0)82.6 (81.4; 83.7)80.4 (79.3; 81.6)75.6 (74.3; 76.9)89.9 (89.0; 90.8)88.2 (87.2; 89.1)
mGFR <60 mL/min/1.73 m2 (n = 925)
mGFR = 42.0FAScrea*CKD-EPIcrea*FAScysCCKD-EPIcysC*CAPAFAScombiCKD-EPIcombi*
eGFR – mGFR7.0 (6.2; 7.9)5.9 (5.0; 6.9)6.9 (6.1; 7.8)4.9 (3.9; 5.9)5.3 (4.3; 6.2)6.3 (5.5; 7.0)4.2 (3.4; 5.1)
eGFR/mGFR1.20 (1.18; 1.23)1.16 (1.13; 1.18)1.19 (1.17; 1.22)1.11 (1.09; 1.14)1.12 (1.10; 1.15)1.18 (1.16; 1.20)1.10 (1.08; 1.12)
RMSE15.4 (14.0; 16.6)16.1 (14.8; 17.2)14.8 (13.7; 15.8)16.2 (14.9; 17.5)16.0 (14.7; 17.3)13.2 (12.1; 14.2)14.4 (13.1; 15.6)
Lin’s CCC0.54 (0.50; 0.57)0.55 (0.51; 0.58)0.56 (0.52; 0.60)0.57 (0.53; 0.60)0.57 (0.53; 0.60)0.61 (0.58; 0.65)0.62 (0.58; 0.65)
P10 (%)31.5 (28.5; 34.5)31.7 (28.7; 34.7)27.9 (25.0; 30.8)29.0 (26.0; 31.9)28.5 (25.6; 31.5)33.8 (30.8; 36.9)34.4 (31.3; 37.4)
P30 (%)70.8 (67.9; 73.7)72.3 (69.4; 75.2)68.0 (65.0; 71.0)70.5 (67.5; 73.4)70.9 (68.0; 73.9)75.2 (72.5; 78.0)79.0 (76.4; 81.7)
mGFR ≥60 mL/min/1.73 m2 (n = 3370)
mGFR = 90.6FAScrea*CKD-EPIcrea*FAScysC*CKD-EPIcysCCAPAFAScombi*CKD-EPIcombi
eGFR – mGFR0.1 (−0.7; 0.4)1.4 (0.9; 1.9)3.5 (2.9; 4.1)8.9 (8.4; 9.5)9.9 (9.2; 10.6)0.8 (0.3; 1.2)6.7 (6.3; 7.2)
eGFR/mGFR1.01 (1.00; 1.01)1.03 (1.03; 1.04)1.05 (1.04; 1.06)1.11 (1.11; 1.12)1.12 (1.11; 1.13)1.02 (1.01; 1.02)1.09 (1.08; 1.09)
RMSE16.2 (15.5; 16.8)14.8 (14.2; 15.3)18.4 (17.8; 18.9)18.6 (18.1; 19.0)22.5 (21.9; 23.1)14.3 (13.8; 14.9)15.6 (15.1; 16.0)
Lin’s CCC0.59 (0.57; 0.61)0.57 (0.54; 0.59)0.51 (0.49; 0.54)0.48 (0.46; 0.50)0.42 (0.40; 0.45)0.64 (0.62; 0.66)0.58 (0.56; 0.60)
P10 (%)47.0 (45.3; 48.7)49.9 (48.2; 51.6)40.3 (38.6; 41.9)33.5 (31.9; 35.1)32.3 (30.7; 33.9)50.9 (49.3; 52.6)42.9 (41.2; 44.6)
P30 (%)92.2 (91.3; 93.1)92.4 (91.5; 93.3)86.6 (85.4; 87.7)83.2 (91.9; 84.4)76.9 (75.4; 78.3)93.9 (93.1; 94.8)90.7 (89.7; 91.7)

Asterisks indicate the best performing equation(s) [13] within the same biomarker category, across all performance statistics. The bold values are the best result(s) for each performance statistic, across all equations. FAS, full-age-spectrum eGFR equation (Scr-based with Q = 0.70 mg/dL for females and Q = 0.90 mg/dL for males; SCysC-based with Q′ = 0.82 mg/L). FAScombi is calculated for α = 0.5.
星号表示同一生物标志物类别中所有性能统计数据中性能最佳的方程[13]。粗体值是所有方程中每个性能统计数据的最佳结果。 FAS,全年龄谱 eGFR 方程(基于 Scr,女性 Q = 0.70 mg/dL,男性 Q = 0.90 mg/dL;基于 SCysC,Q′ = 0.82 mg/L)。 FAS combi 是在 α = 0.5 时计算的。

Table 7

Adults n = 4295 (age 18–70 years)


表 7 成人 n = 4295(年龄 18-70 岁)
Scr-based eGFR 基于 Scr 的 eGFRScysC-based eGFR 基于ScysC的eGFRCombined Scr-/ScysC-based eGFR
基于 Scr/ScysC 的组合 eGFR
mGFR = 80.1 (n = 4295)FAScrea*CKD-EPIcrea*FAScysC*CKD-EPIcysCCAPAFAScombi*CKD-EPIcombi
eGFR – mGFR1.4 (0.9; 1.9)2.4 (1.9; 2.8)4.2 (3.7; 4.8)8.0 (7.6; 8.5)8.9 (8.3; 9.5)1.9 (1.5; 2.4)6.2 (5.8; 6.6)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.05 (1.04; 1.06)1.06 (1.05; 1.07)1.08 (1.07; 1.09)1.11 (1.10; 1.12)1.12 (1.11; 1.13)1.05 (1.04; 1.06)1.09 (1.08; 1.10)
RMSE16.0 (15.4; 16.6)15.1 (14.6; 15.6)17.7 (17.2; 18.2)18.1 (17.6; 18.5)21.3 (20.8; 21.8)14.1 (13.6; 14.6)15.3 (14.9; 15.8)
Lin’s CCC 林氏CCC0.80 (0.79; 0.81)0.82 (0.81; 0.83)0.78 (0.76; 0.79)0.78 (0.77; 0.80)0.73 (0.72; 0.74)0.84 (0.83; 0.85)0.83 (0.82; 0.84)
P10 (%) P10(%)43.6 (42.1; 45.1)46.0 (44.5; 47.5)37.6 (36.2; 39.1)32.5 (31.1; 34.0)31.5 (30.1; 32.9)47.3 (45.8; 48.8)41.0 (39.6; 42.5)
P30 (%) P30(%)87.6 (86.6; 88.6)88.1 (87.1; 89.0)82.6 (81.4; 83.7)80.4 (79.3; 81.6)75.6 (74.3; 76.9)89.9 (89.0; 90.8)88.2 (87.2; 89.1)
mGFR <60 mL/min/1.73 m2 (n = 925)
mGFR = 42.0 肾小球滤过率=42.0FAScrea*CKD-EPIcrea*FAScysCCKD-EPIcysC*CAPAFAScombiCKD-EPIcombi*
eGFR – mGFR7.0 (6.2; 7.9)5.9 (5.0; 6.9)6.9 (6.1; 7.8)4.9 (3.9; 5.9)5.3 (4.3; 6.2)6.3 (5.5; 7.0)4.2 (3.4; 5.1)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.20 (1.18; 1.23)1.16 (1.13; 1.18)1.19 (1.17; 1.22)1.11 (1.09; 1.14)1.12 (1.10; 1.15)1.18 (1.16; 1.20)1.10 (1.08; 1.12)
RMSE15.4 (14.0; 16.6)16.1 (14.8; 17.2)14.8 (13.7; 15.8)16.2 (14.9; 17.5)16.0 (14.7; 17.3)13.2 (12.1; 14.2)14.4 (13.1; 15.6)
Lin’s CCC 林氏CCC0.54 (0.50; 0.57)0.55 (0.51; 0.58)0.56 (0.52; 0.60)0.57 (0.53; 0.60)0.57 (0.53; 0.60)0.61 (0.58; 0.65)0.62 (0.58; 0.65)
P10 (%) P10(%)31.5 (28.5; 34.5)31.7 (28.7; 34.7)27.9 (25.0; 30.8)29.0 (26.0; 31.9)28.5 (25.6; 31.5)33.8 (30.8; 36.9)34.4 (31.3; 37.4)
P30 (%) P30(%)70.8 (67.9; 73.7)72.3 (69.4; 75.2)68.0 (65.0; 71.0)70.5 (67.5; 73.4)70.9 (68.0; 73.9)75.2 (72.5; 78.0)79.0 (76.4; 81.7)
mGFR ≥60 mL/min/1.73 m2 (n = 3370)
mGFR = 90.6 肾小球滤过率=90.6FAScrea*CKD-EPIcrea*FAScysC*CKD-EPIcysCCAPAFAScombi*CKD-EPIcombi
eGFR – mGFR0.1 (−0.7; 0.4) −0.1(−0.7;0.4)1.4 (0.9; 1.9)3.5 (2.9; 4.1)8.9 (8.4; 9.5)9.9 (9.2; 10.6)0.8 (0.3; 1.2)6.7 (6.3; 7.2)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.01 (1.00; 1.01)1.03 (1.03; 1.04)1.05 (1.04; 1.06)1.11 (1.11; 1.12)1.12 (1.11; 1.13)1.02 (1.01; 1.02)1.09 (1.08; 1.09)
RMSE16.2 (15.5; 16.8)14.8 (14.2; 15.3)18.4 (17.8; 18.9)18.6 (18.1; 19.0)22.5 (21.9; 23.1)14.3 (13.8; 14.9)15.6 (15.1; 16.0)
Lin’s CCC 林氏CCC0.59 (0.57; 0.61)0.57 (0.54; 0.59)0.51 (0.49; 0.54)0.48 (0.46; 0.50)0.42 (0.40; 0.45)0.64 (0.62; 0.66)0.58 (0.56; 0.60)
P10 (%) P10(%)47.0 (45.3; 48.7)49.9 (48.2; 51.6)40.3 (38.6; 41.9)33.5 (31.9; 35.1)32.3 (30.7; 33.9)50.9 (49.3; 52.6)42.9 (41.2; 44.6)
P30 (%) P30(%)92.2 (91.3; 93.1)92.4 (91.5; 93.3)86.6 (85.4; 87.7)83.2 (91.9; 84.4)76.9 (75.4; 78.3)93.9 (93.1; 94.8)90.7 (89.7; 91.7)
Scr-based eGFRScysC-based eGFRCombined Scr-/ScysC-based eGFR
mGFR = 80.1 (n = 4295)FAScrea*CKD-EPIcrea*FAScysC*CKD-EPIcysCCAPAFAScombi*CKD-EPIcombi
eGFR – mGFR1.4 (0.9; 1.9)2.4 (1.9; 2.8)4.2 (3.7; 4.8)8.0 (7.6; 8.5)8.9 (8.3; 9.5)1.9 (1.5; 2.4)6.2 (5.8; 6.6)
eGFR/mGFR1.05 (1.04; 1.06)1.06 (1.05; 1.07)1.08 (1.07; 1.09)1.11 (1.10; 1.12)1.12 (1.11; 1.13)1.05 (1.04; 1.06)1.09 (1.08; 1.10)
RMSE16.0 (15.4; 16.6)15.1 (14.6; 15.6)17.7 (17.2; 18.2)18.1 (17.6; 18.5)21.3 (20.8; 21.8)14.1 (13.6; 14.6)15.3 (14.9; 15.8)
Lin’s CCC0.80 (0.79; 0.81)0.82 (0.81; 0.83)0.78 (0.76; 0.79)0.78 (0.77; 0.80)0.73 (0.72; 0.74)0.84 (0.83; 0.85)0.83 (0.82; 0.84)
P10 (%)43.6 (42.1; 45.1)46.0 (44.5; 47.5)37.6 (36.2; 39.1)32.5 (31.1; 34.0)31.5 (30.1; 32.9)47.3 (45.8; 48.8)41.0 (39.6; 42.5)
P30 (%)87.6 (86.6; 88.6)88.1 (87.1; 89.0)82.6 (81.4; 83.7)80.4 (79.3; 81.6)75.6 (74.3; 76.9)89.9 (89.0; 90.8)88.2 (87.2; 89.1)
mGFR <60 mL/min/1.73 m2 (n = 925)
mGFR = 42.0FAScrea*CKD-EPIcrea*FAScysCCKD-EPIcysC*CAPAFAScombiCKD-EPIcombi*
eGFR – mGFR7.0 (6.2; 7.9)5.9 (5.0; 6.9)6.9 (6.1; 7.8)4.9 (3.9; 5.9)5.3 (4.3; 6.2)6.3 (5.5; 7.0)4.2 (3.4; 5.1)
eGFR/mGFR1.20 (1.18; 1.23)1.16 (1.13; 1.18)1.19 (1.17; 1.22)1.11 (1.09; 1.14)1.12 (1.10; 1.15)1.18 (1.16; 1.20)1.10 (1.08; 1.12)
RMSE15.4 (14.0; 16.6)16.1 (14.8; 17.2)14.8 (13.7; 15.8)16.2 (14.9; 17.5)16.0 (14.7; 17.3)13.2 (12.1; 14.2)14.4 (13.1; 15.6)
Lin’s CCC0.54 (0.50; 0.57)0.55 (0.51; 0.58)0.56 (0.52; 0.60)0.57 (0.53; 0.60)0.57 (0.53; 0.60)0.61 (0.58; 0.65)0.62 (0.58; 0.65)
P10 (%)31.5 (28.5; 34.5)31.7 (28.7; 34.7)27.9 (25.0; 30.8)29.0 (26.0; 31.9)28.5 (25.6; 31.5)33.8 (30.8; 36.9)34.4 (31.3; 37.4)
P30 (%)70.8 (67.9; 73.7)72.3 (69.4; 75.2)68.0 (65.0; 71.0)70.5 (67.5; 73.4)70.9 (68.0; 73.9)75.2 (72.5; 78.0)79.0 (76.4; 81.7)
mGFR ≥60 mL/min/1.73 m2 (n = 3370)
mGFR = 90.6FAScrea*CKD-EPIcrea*FAScysC*CKD-EPIcysCCAPAFAScombi*CKD-EPIcombi
eGFR – mGFR0.1 (−0.7; 0.4)1.4 (0.9; 1.9)3.5 (2.9; 4.1)8.9 (8.4; 9.5)9.9 (9.2; 10.6)0.8 (0.3; 1.2)6.7 (6.3; 7.2)
eGFR/mGFR1.01 (1.00; 1.01)1.03 (1.03; 1.04)1.05 (1.04; 1.06)1.11 (1.11; 1.12)1.12 (1.11; 1.13)1.02 (1.01; 1.02)1.09 (1.08; 1.09)
RMSE16.2 (15.5; 16.8)14.8 (14.2; 15.3)18.4 (17.8; 18.9)18.6 (18.1; 19.0)22.5 (21.9; 23.1)14.3 (13.8; 14.9)15.6 (15.1; 16.0)
Lin’s CCC0.59 (0.57; 0.61)0.57 (0.54; 0.59)0.51 (0.49; 0.54)0.48 (0.46; 0.50)0.42 (0.40; 0.45)0.64 (0.62; 0.66)0.58 (0.56; 0.60)
P10 (%)47.0 (45.3; 48.7)49.9 (48.2; 51.6)40.3 (38.6; 41.9)33.5 (31.9; 35.1)32.3 (30.7; 33.9)50.9 (49.3; 52.6)42.9 (41.2; 44.6)
P30 (%)92.2 (91.3; 93.1)92.4 (91.5; 93.3)86.6 (85.4; 87.7)83.2 (91.9; 84.4)76.9 (75.4; 78.3)93.9 (93.1; 94.8)90.7 (89.7; 91.7)

Asterisks indicate the best performing equation(s) [13] within the same biomarker category, across all performance statistics. The bold values are the best result(s) for each performance statistic, across all equations. FAS, full-age-spectrum eGFR equation (Scr-based with Q = 0.70 mg/dL for females and Q = 0.90 mg/dL for males; SCysC-based with Q′ = 0.82 mg/L). FAScombi is calculated for α = 0.5.
星号表示同一生物标志物类别中所有性能统计数据中性能最佳的方程[13]。粗体值是所有方程中每个性能统计数据的最佳结果。 FAS,全年龄谱 eGFR 方程(基于 Scr,女性 Q = 0.70 mg/dL,男性 Q = 0.90 mg/dL;基于 SCysC,Q′ = 0.82 mg/L)。 FAS combi 是在 α = 0.5 时计算的。

Table 8

Older people n = 1469 (age ≥70 years)


表8老年人n=1469(年龄≥70岁)
Scr-based eGFR 基于 Scr 的 eGFRScysC-based eGFR 基于ScysC的eGFRCombined Scr-/ScysC-based eGFR
mGFR = 58.5 (n = 1469)FAScrea*CKD-EPIcreaBIS1* 国际清算银行1*FAScysC*CKD-EPIcysCCAPAFAScombi*CKD-EPIcombiBIS2*
eGFR – mGFR−2.6 (−3.2; −2.0)4.6 (4.0; 5.3)−2.9 (−3.5; −2.4)0.9 (0.3; 1.4)3.8 (3.1; 4.4)5.5 (4.9; 6.2)1.4 (−1.9; −0.9)4.5 (4.0; 5.1)1.2 (−1.7; −0.7)
eGFR/mGFR 肾小球滤过率/肾小球滤过率0.98 (0.97; 1.00)1.10 (1.09; 1.12)0.99 (0.98; 1.00)1.04 (1.03; 1.05)1.07 (1.05; 1.08)1.10 (1.09; 1.12)1.00 (0.99; 1.01)1.08 (1.07; 1.10)1.01 (1.00; 1.02)
RMSE11.4 (10.8; 12.0)12.8 (12.3; 13.3)11.3 (10.8; 11.9)11.3 (10.7; 11.9)12.9 (12.2; 13.5)13.8 (13.1; 14.5)9.8 (9.3; 10.3)11.8 (11.2; 12.3)9.6 (9.1; 10.2)
Lin’s CCC 林氏CCC0.82 (0.81; 0.84)0.81 (0.79; 0.83)0.81 (0.79; 0.82)0.84 (0.82; 0.85)0.83 (0.81; 0.84)0.81 (0.79; 0.82)0.87 (0.86; 0.88)0.85 (0.84; 0.86)0.87 (0.86; 0.88)
P10 (%) P10(%)42.1 (39.5; 44.6)36.3 (33.8; 38.7)42.1 (39.5; 44.6)43.0 (40.5; 45.6)38.1 (35.6; 40.6)36.0 (33.6; 38.5)50.5 (48.0; 53.1)40.0 (37.5; 42.5)52.3 (49.8; 54.9)
P30 (%) P30(%)88.2 (86.6; 89.9)80.0 (77.9; 82.0)89.0 (87.4; 90.6)88.2 (86.6; 89.9)84.4 (82.6; 86.3)82.0 (80.1; 84.0)91.2 (89.8; 92.7)85.6 (83.8; 87.4)92.4 (91.0; 93.7)
mGFR <60 mL/min/1.73 m2 (n = 753)
mGFR = 42.9 肾小球滤过率=42.9FAScrea*CKD-EPIcreaBIS1* 国际清算银行1*FAScysC*CKD-EPIcysC*CAPAFAScombi*CKD-EPIcombiBIS2*
eGFR – mGFR0.7 (0.0; 1.3)5.9 (5.1; 6.7)1.7 (1.1; 2.4)2.9 (2.2; 3.6)2.7 (1.9; 3.5)4.9 (4.1; 5.7)1.4 (0.8; 1.9)3.9 (3.1; 4.6)1.8 (1.2; 2.3)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.04 (1.02; 1.06)1.15 (1.12; 1.17)1.07 (1.05; 1.09)1.09 (1.07; 1.11)1.06 (1.04; 1.08)1.12 (1.10; 1.14)1.05 (1.03; 1.07)1.09 (1.07; 1.11)1.06 (1.05; 1.08)
RMSE9.2 (8.4; 9.9)13.0 (12.1; 13.8)8.7 (8.0; 9.3)9.8 (8.9; 10.6)11.4 (10.3; 12.3)12.1 (11.0; 13.2)8.1 (7.4; 8.7)11.0 (10.0; 11.8)7.9 (7.2; 8.6)
Lin’s CCC 林氏CCC0.75 (0.71; 0.78)0.65 (0.62; 0.69)0.75 (0.72; 0.78)0.73 (0.70; 0.76)0.72 (0.69; 0.75)0.69 (0.66; 0.72)0.80 (0.77; 0.82)0.73 (0.70; 0.76)0.80 (0.78; 0.83)
P10 (%) P10(%)41.2 (37.6; 44.7)31.2 (27.9; 34.5)42.5 (39.0; 46.0)38.5 (35.0; 42.0)35.5 (32.0; 38.9)32.9 (29.6; 36.3)47.9 (44.4; 51.5)35.5 (32.0; 38.9)48.3 (44.8; 51.9)
P30 (%) P30(%)84.6 (82.0; 87.2)69.5 (66.2; 72.8)85.4 (82.9; 87.9)83.4 (80.7; 86.1)81.8 (79.0; 84.6)78.2 (75.3; 81.2)87.0 (84.6; 89.4)79.9 (77.1; 82.8)89.1 (86.9; 91.3)
mGFR ≥60 mL/min/1.73 m2 (n = 716)
mGFR = 74.8 肾小球滤过率=74.8FAScreaCKD-EPIcrea*BIS1FAScysC*CKD-EPIcysC*CAPAFAScombi*CKD-EPIcombiBIS2*
eGFR – mGFR−6.1 (−6.9; −5.2)3.3 (2.4; 4.2)−7.9 (−8.7; −7.1)1.3 (−2.2; −0.4)4.9 (3.9; 5.9)6.2 (5.1; 7.2)−4.3 (−5.1; −3.5)5.2 (4.3; 6.0)−4.3 (−5.1; −3.6)
eGFR/mGFR 肾小球滤过率/肾小球滤过率0.93 (0.92; 0.94)1.06 (1.04; 1.07)0.90 (0.89; 0.91)0.99 (0.98; 1.00)1.07 (1.06; 1.09)1.09 (1.08; 1.10)0.95 (0.94; 0.96)1.08 (1.07; 1.09)0.95 (0.94; 0.96)
RMSE13.3 (12.5; 14.1)12.7 (12.1; 13.3)13.6 (12.7; 14.4)12.7 (11.8; 13.6)14.3 (13.5; 15.0)15.4 (14.4; 16.3)11.3 (10.5; 12.1)12.5 (11.9; 13.2)11.1 (10.3; 11.9)
Lin’s CCC 林氏CCC0.49 (0.44; 0.54)0.46 (0.40; 0.51)0.44 (0.39; 0.49)0.55 (0.49; 0.60)0.50 (0.45; 0.55)0.49 (0.44; 0.54)0.59 (0.54; 0.63)0.55 (0.50; 0.59)0.59 (0.54; 0.63)
P10 (%) P10(%)43.0 (39.4; 46.7)41.6 (38.0; 45.2)41.6 (38.0; 45.2)47.8 (44.1; 51.4)40.9 (37.3; 44.5)39.2 (35.7; 42.8)53.2 (49.5; 56.9)44.7 (41.0; 48.3)56.6 (52.9; 60.2)
P30 (%) P30(%)92.0 (90.0; 94.0)91.1 (89.0; 93.2)92.9 (91.0; 94.8)93.3 (91.5; 95.1)87.2 (84.7; 89.6)86.0 (83.5; 88.6)95.7 (94.2; 97.2)91.5 (89.4; 93.5)95.8 (94.3; 97.3)
Scr-based eGFRScysC-based eGFRCombined Scr-/ScysC-based eGFR
mGFR = 58.5 (n = 1469)FAScrea*CKD-EPIcreaBIS1*FAScysC*CKD-EPIcysCCAPAFAScombi*CKD-EPIcombiBIS2*
eGFR – mGFR−2.6 (−3.2; −2.0)4.6 (4.0; 5.3)−2.9 (−3.5; −2.4)0.9 (0.3; 1.4)3.8 (3.1; 4.4)5.5 (4.9; 6.2)1.4 (−1.9; −0.9)4.5 (4.0; 5.1)1.2 (−1.7; −0.7)
eGFR/mGFR0.98 (0.97; 1.00)1.10 (1.09; 1.12)0.99 (0.98; 1.00)1.04 (1.03; 1.05)1.07 (1.05; 1.08)1.10 (1.09; 1.12)1.00 (0.99; 1.01)1.08 (1.07; 1.10)1.01 (1.00; 1.02)
RMSE11.4 (10.8; 12.0)12.8 (12.3; 13.3)11.3 (10.8; 11.9)11.3 (10.7; 11.9)12.9 (12.2; 13.5)13.8 (13.1; 14.5)9.8 (9.3; 10.3)11.8 (11.2; 12.3)9.6 (9.1; 10.2)
Lin’s CCC0.82 (0.81; 0.84)0.81 (0.79; 0.83)0.81 (0.79; 0.82)0.84 (0.82; 0.85)0.83 (0.81; 0.84)0.81 (0.79; 0.82)0.87 (0.86; 0.88)0.85 (0.84; 0.86)0.87 (0.86; 0.88)
P10 (%)42.1 (39.5; 44.6)36.3 (33.8; 38.7)42.1 (39.5; 44.6)43.0 (40.5; 45.6)38.1 (35.6; 40.6)36.0 (33.6; 38.5)50.5 (48.0; 53.1)40.0 (37.5; 42.5)52.3 (49.8; 54.9)
P30 (%)88.2 (86.6; 89.9)80.0 (77.9; 82.0)89.0 (87.4; 90.6)88.2 (86.6; 89.9)84.4 (82.6; 86.3)82.0 (80.1; 84.0)91.2 (89.8; 92.7)85.6 (83.8; 87.4)92.4 (91.0; 93.7)
mGFR <60 mL/min/1.73 m2 (n = 753)
mGFR = 42.9FAScrea*CKD-EPIcreaBIS1*FAScysC*CKD-EPIcysC*CAPAFAScombi*CKD-EPIcombiBIS2*
eGFR – mGFR0.7 (0.0; 1.3)5.9 (5.1; 6.7)1.7 (1.1; 2.4)2.9 (2.2; 3.6)2.7 (1.9; 3.5)4.9 (4.1; 5.7)1.4 (0.8; 1.9)3.9 (3.1; 4.6)1.8 (1.2; 2.3)
eGFR/mGFR1.04 (1.02; 1.06)1.15 (1.12; 1.17)1.07 (1.05; 1.09)1.09 (1.07; 1.11)1.06 (1.04; 1.08)1.12 (1.10; 1.14)1.05 (1.03; 1.07)1.09 (1.07; 1.11)1.06 (1.05; 1.08)
RMSE9.2 (8.4; 9.9)13.0 (12.1; 13.8)8.7 (8.0; 9.3)9.8 (8.9; 10.6)11.4 (10.3; 12.3)12.1 (11.0; 13.2)8.1 (7.4; 8.7)11.0 (10.0; 11.8)7.9 (7.2; 8.6)
Lin’s CCC0.75 (0.71; 0.78)0.65 (0.62; 0.69)0.75 (0.72; 0.78)0.73 (0.70; 0.76)0.72 (0.69; 0.75)0.69 (0.66; 0.72)0.80 (0.77; 0.82)0.73 (0.70; 0.76)0.80 (0.78; 0.83)
P10 (%)41.2 (37.6; 44.7)31.2 (27.9; 34.5)42.5 (39.0; 46.0)38.5 (35.0; 42.0)35.5 (32.0; 38.9)32.9 (29.6; 36.3)47.9 (44.4; 51.5)35.5 (32.0; 38.9)48.3 (44.8; 51.9)
P30 (%)84.6 (82.0; 87.2)69.5 (66.2; 72.8)85.4 (82.9; 87.9)83.4 (80.7; 86.1)81.8 (79.0; 84.6)78.2 (75.3; 81.2)87.0 (84.6; 89.4)79.9 (77.1; 82.8)89.1 (86.9; 91.3)
mGFR ≥60 mL/min/1.73 m2 (n = 716)
mGFR = 74.8FAScreaCKD-EPIcrea*BIS1FAScysC*CKD-EPIcysC*CAPAFAScombi*CKD-EPIcombiBIS2*
eGFR – mGFR−6.1 (−6.9; −5.2)3.3 (2.4; 4.2)−7.9 (−8.7; −7.1)1.3 (−2.2; −0.4)4.9 (3.9; 5.9)6.2 (5.1; 7.2)−4.3 (−5.1; −3.5)5.2 (4.3; 6.0)−4.3 (−5.1; −3.6)
eGFR/mGFR0.93 (0.92; 0.94)1.06 (1.04; 1.07)0.90 (0.89; 0.91)0.99 (0.98; 1.00)1.07 (1.06; 1.09)1.09 (1.08; 1.10)0.95 (0.94; 0.96)1.08 (1.07; 1.09)0.95 (0.94; 0.96)
RMSE13.3 (12.5; 14.1)12.7 (12.1; 13.3)13.6 (12.7; 14.4)12.7 (11.8; 13.6)14.3 (13.5; 15.0)15.4 (14.4; 16.3)11.3 (10.5; 12.1)12.5 (11.9; 13.2)11.1 (10.3; 11.9)
Lin’s CCC0.49 (0.44; 0.54)0.46 (0.40; 0.51)0.44 (0.39; 0.49)0.55 (0.49; 0.60)0.50 (0.45; 0.55)0.49 (0.44; 0.54)0.59 (0.54; 0.63)0.55 (0.50; 0.59)0.59 (0.54; 0.63)
P10 (%)43.0 (39.4; 46.7)41.6 (38.0; 45.2)41.6 (38.0; 45.2)47.8 (44.1; 51.4)40.9 (37.3; 44.5)39.2 (35.7; 42.8)53.2 (49.5; 56.9)44.7 (41.0; 48.3)56.6 (52.9; 60.2)
P30 (%)92.0 (90.0; 94.0)91.1 (89.0; 93.2)92.9 (91.0; 94.8)93.3 (91.5; 95.1)87.2 (84.7; 89.6)86.0 (83.5; 88.6)95.7 (94.2; 97.2)91.5 (89.4; 93.5)95.8 (94.3; 97.3)

Asterisks indicate the best performing equation(s) [13] within the same biomarker category, across all performance statistics. The bold values are the best result(s) for each performance statistic, across all equations. FAS, full-age-spectrum eGFR equation (Scr-based with Q = 0.70 mg/dL for females and Q = 0.90 mg/dL for males; SCysC-based with Q′ = 0.95 mg/L). FAScombi is calculated for α = 0.5.
星号表示同一生物标志物类别中所有性能统计数据中性能最佳的方程[13]。粗体值是所有方程中每个性能统计数据的最佳结果。 FAS,全年龄谱 eGFR 方程(基于 Scr,女性 Q = 0.70 mg/dL,男性 Q = 0.90 mg/dL;基于 SCysC,Q′ = 0.95 mg/L)。 FAS combi 是在 α = 0.5 时计算的。

Table 8

Older people n = 1469 (age ≥70 years)


表8老年人n=1469(年龄≥70岁)
Scr-based eGFR 基于 Scr 的 eGFRScysC-based eGFR 基于ScysC的eGFRCombined Scr-/ScysC-based eGFR
基于 Scr/ScysC 的组合 eGFR
mGFR = 58.5 (n = 1469)FAScrea*CKD-EPIcreaBIS1* 国际清算银行1*FAScysC*CKD-EPIcysCCAPAFAScombi*CKD-EPIcombiBIS2*
eGFR – mGFR−2.6 (−3.2; −2.0)4.6 (4.0; 5.3)−2.9 (−3.5; −2.4)0.9 (0.3; 1.4)3.8 (3.1; 4.4)5.5 (4.9; 6.2)1.4 (−1.9; −0.9)4.5 (4.0; 5.1)1.2 (−1.7; −0.7)
eGFR/mGFR 肾小球滤过率/肾小球滤过率0.98 (0.97; 1.00)1.10 (1.09; 1.12)0.99 (0.98; 1.00)1.04 (1.03; 1.05)1.07 (1.05; 1.08)1.10 (1.09; 1.12)1.00 (0.99; 1.01)1.08 (1.07; 1.10)1.01 (1.00; 1.02)
RMSE11.4 (10.8; 12.0)12.8 (12.3; 13.3)11.3 (10.8; 11.9)11.3 (10.7; 11.9)12.9 (12.2; 13.5)13.8 (13.1; 14.5)9.8 (9.3; 10.3)11.8 (11.2; 12.3)9.6 (9.1; 10.2)
Lin’s CCC 林氏CCC0.82 (0.81; 0.84)0.81 (0.79; 0.83)0.81 (0.79; 0.82)0.84 (0.82; 0.85)0.83 (0.81; 0.84)0.81 (0.79; 0.82)0.87 (0.86; 0.88)0.85 (0.84; 0.86)0.87 (0.86; 0.88)
P10 (%) P10(%)42.1 (39.5; 44.6)36.3 (33.8; 38.7)42.1 (39.5; 44.6)43.0 (40.5; 45.6)38.1 (35.6; 40.6)36.0 (33.6; 38.5)50.5 (48.0; 53.1)40.0 (37.5; 42.5)52.3 (49.8; 54.9)
P30 (%) P30(%)88.2 (86.6; 89.9)80.0 (77.9; 82.0)89.0 (87.4; 90.6)88.2 (86.6; 89.9)84.4 (82.6; 86.3)82.0 (80.1; 84.0)91.2 (89.8; 92.7)85.6 (83.8; 87.4)92.4 (91.0; 93.7)
mGFR <60 mL/min/1.73 m2 (n = 753)
mGFR = 42.9 肾小球滤过率=42.9FAScrea*CKD-EPIcreaBIS1* 国际清算银行1*FAScysC*CKD-EPIcysC*CAPAFAScombi*CKD-EPIcombiBIS2*
eGFR – mGFR0.7 (0.0; 1.3)5.9 (5.1; 6.7)1.7 (1.1; 2.4)2.9 (2.2; 3.6)2.7 (1.9; 3.5)4.9 (4.1; 5.7)1.4 (0.8; 1.9)3.9 (3.1; 4.6)1.8 (1.2; 2.3)
eGFR/mGFR 肾小球滤过率/肾小球滤过率1.04 (1.02; 1.06)1.15 (1.12; 1.17)1.07 (1.05; 1.09)1.09 (1.07; 1.11)1.06 (1.04; 1.08)1.12 (1.10; 1.14)1.05 (1.03; 1.07)1.09 (1.07; 1.11)1.06 (1.05; 1.08)
RMSE9.2 (8.4; 9.9)13.0 (12.1; 13.8)8.7 (8.0; 9.3)9.8 (8.9; 10.6)11.4 (10.3; 12.3)12.1 (11.0; 13.2)8.1 (7.4; 8.7)11.0 (10.0; 11.8)7.9 (7.2; 8.6)
Lin’s CCC 林氏CCC0.75 (0.71; 0.78)0.65 (0.62; 0.69)0.75 (0.72; 0.78)0.73 (0.70; 0.76)0.72 (0.69; 0.75)0.69 (0.66; 0.72)0.80 (0.77; 0.82)0.73 (0.70; 0.76)0.80 (0.78; 0.83)
P10 (%) P10(%)41.2 (37.6; 44.7)31.2 (27.9; 34.5)42.5 (39.0; 46.0)38.5 (35.0; 42.0)35.5 (32.0; 38.9)32.9 (29.6; 36.3)47.9 (44.4; 51.5)35.5 (32.0; 38.9)48.3 (44.8; 51.9)
P30 (%) P30(%)84.6 (82.0; 87.2)69.5 (66.2; 72.8)85.4 (82.9; 87.9)83.4 (80.7; 86.1)81.8 (79.0; 84.6)78.2 (75.3; 81.2)87.0 (84.6; 89.4)79.9 (77.1; 82.8)89.1 (86.9; 91.3)
mGFR ≥60 mL/min/1.73 m2 (n = 716)
mGFR = 74.8 肾小球滤过率=74.8FAScreaCKD-EPIcrea*BIS1FAScysC*CKD-EPIcysC*CAPAFAScombi*CKD-EPIcombiBIS2*
eGFR – mGFR−6.1 (−6.9; −5.2)3.3 (2.4; 4.2)−7.9 (−8.7; −7.1)1.3 (−2.2; −0.4)4.9 (3.9; 5.9)6.2 (5.1; 7.2)−4.3 (−5.1; −3.5)5.2 (4.3; 6.0)−4.3 (−5.1; −3.6)
eGFR/mGFR 肾小球滤过率/肾小球滤过率0.93 (0.92; 0.94)1.06 (1.04; 1.07)0.90 (0.89; 0.91)0.99 (0.98; 1.00)1.07 (1.06; 1.09)1.09 (1.08; 1.10)0.95 (0.94; 0.96)1.08 (1.07; 1.09)0.95 (0.94; 0.96)
RMSE13.3 (12.5; 14.1)12.7 (12.1; 13.3)13.6 (12.7; 14.4)12.7 (11.8; 13.6)14.3 (13.5; 15.0)15.4 (14.4; 16.3)11.3 (10.5; 12.1)12.5 (11.9; 13.2)11.1 (10.3; 11.9)
Lin’s CCC 林氏CCC0.49 (0.44; 0.54)0.46 (0.40; 0.51)0.44 (0.39; 0.49)0.55 (0.49; 0.60)0.50 (0.45; 0.55)0.49 (0.44; 0.54)0.59 (0.54; 0.63)0.55 (0.50; 0.59)0.59 (0.54; 0.63)
P10 (%) P10(%)43.0 (39.4; 46.7)41.6 (38.0; 45.2)41.6 (38.0; 45.2)47.8 (44.1; 51.4)40.9 (37.3; 44.5)39.2 (35.7; 42.8)53.2 (49.5; 56.9)44.7 (41.0; 48.3)56.6 (52.9; 60.2)
P30 (%) P30(%)92.0 (90.0; 94.0)91.1 (89.0; 93.2)92.9 (91.0; 94.8)93.3 (91.5; 95.1)87.2 (84.7; 89.6)86.0 (83.5; 88.6)95.7 (94.2; 97.2)91.5 (89.4; 93.5)95.8 (94.3; 97.3)
Scr-based eGFRScysC-based eGFRCombined Scr-/ScysC-based eGFR
mGFR = 58.5 (n = 1469)FAScrea*CKD-EPIcreaBIS1*FAScysC*CKD-EPIcysCCAPAFAScombi*CKD-EPIcombiBIS2*
eGFR – mGFR−2.6 (−3.2; −2.0)4.6 (4.0; 5.3)−2.9 (−3.5; −2.4)0.9 (0.3; 1.4)3.8 (3.1; 4.4)5.5 (4.9; 6.2)1.4 (−1.9; −0.9)4.5 (4.0; 5.1)1.2 (−1.7; −0.7)
eGFR/mGFR0.98 (0.97; 1.00)1.10 (1.09; 1.12)0.99 (0.98; 1.00)1.04 (1.03; 1.05)1.07 (1.05; 1.08)1.10 (1.09; 1.12)1.00 (0.99; 1.01)1.08 (1.07; 1.10)1.01 (1.00; 1.02)
RMSE11.4 (10.8; 12.0)12.8 (12.3; 13.3)11.3 (10.8; 11.9)11.3 (10.7; 11.9)12.9 (12.2; 13.5)13.8 (13.1; 14.5)9.8 (9.3; 10.3)11.8 (11.2; 12.3)9.6 (9.1; 10.2)
Lin’s CCC0.82 (0.81; 0.84)0.81 (0.79; 0.83)0.81 (0.79; 0.82)0.84 (0.82; 0.85)0.83 (0.81; 0.84)0.81 (0.79; 0.82)0.87 (0.86; 0.88)0.85 (0.84; 0.86)0.87 (0.86; 0.88)
P10 (%)42.1 (39.5; 44.6)36.3 (33.8; 38.7)42.1 (39.5; 44.6)43.0 (40.5; 45.6)38.1 (35.6; 40.6)36.0 (33.6; 38.5)50.5 (48.0; 53.1)40.0 (37.5; 42.5)52.3 (49.8; 54.9)
P30 (%)88.2 (86.6; 89.9)80.0 (77.9; 82.0)89.0 (87.4; 90.6)88.2 (86.6; 89.9)84.4 (82.6; 86.3)82.0 (80.1; 84.0)91.2 (89.8; 92.7)85.6 (83.8; 87.4)92.4 (91.0; 93.7)
mGFR <60 mL/min/1.73 m2 (n = 753)
mGFR = 42.9FAScrea*CKD-EPIcreaBIS1*FAScysC*CKD-EPIcysC*CAPAFAScombi*CKD-EPIcombiBIS2*
eGFR – mGFR0.7 (0.0; 1.3)5.9 (5.1; 6.7)1.7 (1.1; 2.4)2.9 (2.2; 3.6)2.7 (1.9; 3.5)4.9 (4.1; 5.7)1.4 (0.8; 1.9)3.9 (3.1; 4.6)1.8 (1.2; 2.3)
eGFR/mGFR1.04 (1.02; 1.06)1.15 (1.12; 1.17)1.07 (1.05; 1.09)1.09 (1.07; 1.11)1.06 (1.04; 1.08)1.12 (1.10; 1.14)1.05 (1.03; 1.07)1.09 (1.07; 1.11)1.06 (1.05; 1.08)
RMSE9.2 (8.4; 9.9)13.0 (12.1; 13.8)8.7 (8.0; 9.3)9.8 (8.9; 10.6)11.4 (10.3; 12.3)12.1 (11.0; 13.2)8.1 (7.4; 8.7)11.0 (10.0; 11.8)7.9 (7.2; 8.6)
Lin’s CCC0.75 (0.71; 0.78)0.65 (0.62; 0.69)0.75 (0.72; 0.78)0.73 (0.70; 0.76)0.72 (0.69; 0.75)0.69 (0.66; 0.72)0.80 (0.77; 0.82)0.73 (0.70; 0.76)0.80 (0.78; 0.83)
P10 (%)41.2 (37.6; 44.7)31.2 (27.9; 34.5)42.5 (39.0; 46.0)38.5 (35.0; 42.0)35.5 (32.0; 38.9)32.9 (29.6; 36.3)47.9 (44.4; 51.5)35.5 (32.0; 38.9)48.3 (44.8; 51.9)
P30 (%)84.6 (82.0; 87.2)69.5 (66.2; 72.8)85.4 (82.9; 87.9)83.4 (80.7; 86.1)81.8 (79.0; 84.6)78.2 (75.3; 81.2)87.0 (84.6; 89.4)79.9 (77.1; 82.8)89.1 (86.9; 91.3)
mGFR ≥60 mL/min/1.73 m2 (n = 716)
mGFR = 74.8FAScreaCKD-EPIcrea*BIS1FAScysC*CKD-EPIcysC*CAPAFAScombi*CKD-EPIcombiBIS2*
eGFR – mGFR−6.1 (−6.9; −5.2)3.3 (2.4; 4.2)−7.9 (−8.7; −7.1)1.3 (−2.2; −0.4)4.9 (3.9; 5.9)6.2 (5.1; 7.2)−4.3 (−5.1; −3.5)5.2 (4.3; 6.0)−4.3 (−5.1; −3.6)
eGFR/mGFR0.93 (0.92; 0.94)1.06 (1.04; 1.07)0.90 (0.89; 0.91)0.99 (0.98; 1.00)1.07 (1.06; 1.09)1.09 (1.08; 1.10)0.95 (0.94; 0.96)1.08 (1.07; 1.09)0.95 (0.94; 0.96)
RMSE13.3 (12.5; 14.1)12.7 (12.1; 13.3)13.6 (12.7; 14.4)12.7 (11.8; 13.6)14.3 (13.5; 15.0)15.4 (14.4; 16.3)11.3 (10.5; 12.1)12.5 (11.9; 13.2)11.1 (10.3; 11.9)
Lin’s CCC0.49 (0.44; 0.54)0.46 (0.40; 0.51)0.44 (0.39; 0.49)0.55 (0.49; 0.60)0.50 (0.45; 0.55)0.49 (0.44; 0.54)0.59 (0.54; 0.63)0.55 (0.50; 0.59)0.59 (0.54; 0.63)
P10 (%)43.0 (39.4; 46.7)41.6 (38.0; 45.2)41.6 (38.0; 45.2)47.8 (44.1; 51.4)40.9 (37.3; 44.5)39.2 (35.7; 42.8)53.2 (49.5; 56.9)44.7 (41.0; 48.3)56.6 (52.9; 60.2)
P30 (%)92.0 (90.0; 94.0)91.1 (89.0; 93.2)92.9 (91.0; 94.8)93.3 (91.5; 95.1)87.2 (84.7; 89.6)86.0 (83.5; 88.6)95.7 (94.2; 97.2)91.5 (89.4; 93.5)95.8 (94.3; 97.3)

Asterisks indicate the best performing equation(s) [13] within the same biomarker category, across all performance statistics. The bold values are the best result(s) for each performance statistic, across all equations. FAS, full-age-spectrum eGFR equation (Scr-based with Q = 0.70 mg/dL for females and Q = 0.90 mg/dL for males; SCysC-based with Q′ = 0.95 mg/L). FAScombi is calculated for α = 0.5.
星号表示同一生物标志物类别中所有性能统计数据中性能最佳的方程[13]。粗体值是所有方程中每个性能统计数据的最佳结果。 FAS,全年龄谱 eGFR 方程(基于 Scr,女性 Q = 0.70 mg/dL,男性 Q = 0.90 mg/dL;基于 SCysC,Q′ = 0.95 mg/L)。 FAS combi 是在 α = 0.5 时计算的。

For children, the FAScysC equation performs significantly better than the FAScrea equation based on Qcrea(age) and slightly better than or, in some cases, equivalent to the FAScrea equation based on Qcrea(height) (Table 6). We found that n = 7 children [for Qcrea(height)] and n = 20 children [for Qcrea(age)] with Scr/Qcrea <0.67 had FAScrea predictions that largely overestimate mGFR and were responsible for the large bias, RMSE and worse performance statistics. These children had spina bifida, Duchenne muscular dystrophy and severe growth retardation, explaining the very low Scr values and the poor match between Qcrea and age. The FAScysC equation has equivalent Lin’s CCC with CAPA but better RMSE. Also, P10 and P30 performance statistics were superior to CAPA. The SchwartzcysC equation shows the best performance in the mGFR <60 mL/min/1.73 m2 subgroup. However, although all children suffered from some underlying renal pathology, this subgroup was rather small (n = 57, 15%). The FAScombi equations [based on Qcrea(age) and Qcrea(height)] outperform all other paediatric equations and increase the precision for P10 to ≈45% and P30 to ≈90%, which is significantly higher (P < 0.0001) compared with single biomarker equations, including the single biomarker FAS equations.
对于儿童,FAS cysC 方程的表现明显优于基于 Q crea (年龄)的 FAS crea 方程,并且在某些情况下略好于或,相当于基于 Q crea (高度)的 FAS crea 方程(表 6)。我们发现 n = 7 个儿童 [对于 Q crea (身高)] 和 n = 20 个儿童 [对于 Q crea (年龄)] 具有 Scr/Q crea  <0.67 的 FAS crea 预测在很大程度上高估了 mGFR,并导致较大偏差、RMSE 和较差的性能统计数据。这些儿童患有脊柱裂、杜氏肌营养不良症和严重的生长迟缓,这解释了 Scr 值非常低以及 Q crea 与年龄之间不匹配的原因。 FAS cysC 方程具有与 CAPA 等效的 Lin 的 CCC,但具有更好的 RMSE。此外,P10 和 P30 性能统计数据优于 CAPA。 Schwartz cysC 方程显示 mGFR <60 mL/min/1.73 m 2 亚组的最佳性能。然而,尽管所有儿童都患有一些潜在的肾脏病变,但该亚组规模相当小(n = 57,15%)。 FAS combi 方程[基于 Q crea (年龄)和 Q crea (身高)] 优于所有其他儿科方程,并将 P10 的精度提高到 ≈ 45%,P30 约为 90%,与单一生物标志物方程(包括单一生物标志物 FAS 方程)相比显着更高 (P < 0.0001)。

For adults, the FAScysC equation performs worse than FAScrea, but better (overall and in the mGFR ≥60 mL/min/1.73 m2 subgroup) or equivalent (in the mGFR <60 mL/min/1.73 m2 subgroup) than the CKD-EPIcysC equation. The FAScysC equation is significantly better than the CAPA equation. The combined equations show higher precision, but the difference with the FAScrea equation is small. However, the FAScombi equation is overall the best prediction equation and performs better than the CKD-EPI combined equation, except for mGFR <60 mL/min/1.73 m2, where the performance is statistically equivalent.
对于成人,FAS cysC 方程的表现比 FAS crea 差,但更好(总体而言以及在 mGFR ≥60 mL/min/1.73 m 2 亚组中)或相当于 CKD-EPI cysC 方程(在 mGFR <60 mL/min/1.73 m 2 亚组中)。 FAS cysC 方程明显优于 CAPA 方程。组合方程精度较高,但与FAS crea 方程差异较小。然而,FAS combi 方程总体来说是最好的预测方程,并且比 CKD-EPI 组合方程表现更好,除了 mGFR <60 mL/min/1.73 m 2 ,其中性能在统计上是相当的。

In older adults, the FAScysC equation (with QcysC =0.95) performs better than the CKD-EPIcysC equation and shows equivalent performance with the FAScrea equation. If we use the linear function QcysC = 0.863 + 0.01704 × (Age – 70) to normalize ScysC in the FAScysC and FAScombi equations, then the performance results (data not shown) are not significantly different than when QcysC =0.95 is used to normalize ScysC in the FAS equation. The combined FAS equation is performing equivalent to the BIS2 equation, reaching P10 > 50% and P30 > 90%, and better than the combined CKD-EPI equation.
在老年人中,FAS cysC 方程(Q cysC  =0.95)比 CKD-EPI cysC 方程表现更好,并且与 FAS cysC 方程表现出相同的性能。 b3>方程。如果我们使用线性函数 Q cysC = 0.863 + 0.01704 × (Age – 70) 来标准化 FAS cysC 和 FAS combi 方程中的 ScysC,则性能结果(数据未显示)与使用 Q cysC  =0.95 标准化 FAS 方程中的 ScysC 时没有显着差异。组合的 FAS 方程与 BIS2 方程等效,达到 P10 > 50% 和 P30 > 90%,并且优于组合的 CKD-EPI 方程。

In Tables 6–8, we highlighted (the equations marked with asterisk) the best performing equation per biomarker category based on the scoring system previously used by Hoste et al. [13], which is based on bias, P10 and P30. We also highlighted (in bold) the best performance statistic.
在表 6-8 中,我们根据 Hoste 等人之前使用的评分系统突出显示了每个生物标志物类别中表现最佳的方程(标有星号的方程)。 [13],基于偏差、P10和P30。我们还突出显示了(以粗体显示)最佳性能统计数据。

We also calculated the performance statistics (RMSE in Figure 2 and P30 in Figure 3) of the FAScombi equation as a function of the weighting parameter α. These figures show the performance statistics as a continuous function of α, evolving from the FAScysC equation (with α = 0) to the FAScrea equation (with α = 1), and in-between for the FAScombi for all values of α.
我们还计算了 FAS combi 方程的性能统计数据(图 2 中的 RMSE 和图 3 中的 P30)作为权重参数 α 的函数。这些图将性能统计数据显示为 α 的连续函数,从 FAS cysC 方程(α = 0)演变为 FAS crea 方程(α = 1),并且所有 α 值的 FAS combi 的中间值。
FIGURE 3 图3
P30 as a function of the weighting factor α for the different age groups.
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P30 as a function of the weighting factor α for the different age groups.
P30 作为不同年龄组的权重因子 α 的函数。

DISCUSSION 讨论

Through the introduction of the international certified reference material ERM-DA471/IFCC for cystatin C [5] it has become possible to develop ScysC-based as well as combined Scr-/ScysC-based eGFR equations on the basis of normalized biomarkers. Despite the fact that manufacturers still need to improve the accuracy of cystatin C assays [19], we have shown here that the basic concept upon which the FAScrea equation was built [2] is not only applicable for normalized Scr, but can also be applied to normalized ScysC. By replacing normalized Scr with ScysC, or introducing the (weighted) average of both biomarkers, we can change from a Scr-based FAS equation to a ScysC-based FAS equation or a combined Scr-/ScysC-based FAS equation. These FAS equations show performance values that are equivalent or in some conditions superior to the currently recommended eGFR equations for children, adolescents, adults and older adults. Normalization of the biomarkers is a key in this construction. In the case of Scr, normalization is required to account for the difference in creatinine generation during childhood, the age/gender differences during adolescence and the difference between adult men and women. Normalization of ScysC is required to account for the age effect beyond the age of 70 years. For the healthy population, the normalized biomarkers show equivalent distributions with mean of ‘1’ and 2.5th and 97.5th Pct of 0.67 and 1.33, respectively. These similar characteristics of normalized biomarker concentration distributions lead to an interchangeable usage of both renal markers in the FAS equation(s).
通过引入半胱氨酸蛋白酶抑制剂 C 的国际认证标准物质 ERM-DA471/IFCC [5],可以在标准化生物标志物的基础上开发基于 ScysC 以及基于 Scr-/ScysC 的组合 eGFR 方程。尽管制造商仍然需要提高胱抑素 C 测定的准确性 [19],但我们在此表明​​,建立 FAS crea 方程的基本概念 [2] 不仅适用于标准化Scr,但也可以应用于标准化的 ScysC。通过用 ScysC 替换归一化的 Scr,或引入两种生物标志物的(加权)平均值,我们可以从基于 Scr 的 FAS 方程更改为基于 ScysC 的 FAS 方程或基于 Scr/ScysC 的组合 FAS 方程。这些 FAS 方程显示的性能值相当于或在某些条件下优于目前推荐的儿童、青少年、成人和老年人的 eGFR 方程。生物标志物的标准化是该构建的关键。就 Scr 而言,需要标准化以解释儿童时期肌酐生成的差异、青春期年龄/性别差异以及成年男性和女性之间的差异。需要对 ScysC 进行标准化,以考虑 70 岁以上的年龄效应。对于健康人群,标准化生物标志物显示出等效分布,平均值为“1”,第 2.5% 和第 97.5% 分别为 0.67 和 1.33。归一化生物标志物浓度分布的这些相似特征导致 FAS 方程中两种肾脏标志物的可互换使用。

The performance of the new FAScysC equation was better than the CAPA equation and better (in adults with mGFR ≥60 mL/min/1.73 m2 and in older adults) or equivalent (in adults with mGFR <60 mL/min/1.73 m2) to the CKD-EPIcysC equation. In children, the RMSE statistic is worst (highest) for the FAScrea equation due to a fraction of children with Scr/Qcrea <0.67. Therefore, we would recommend not to use FAScrea (or the combined FAS equations) when Scr/Qcrea <0.67 [2]. For adults, based on the performance statistics (RMSE and P30), there is still a slight preference for the single biomarker FAScrea equation over the single biomarker FAScysC equation. For older adults, both single biomarker FAS equations perform in a similar manner. However, for all age groups, the FAScombi equation with α ≈ 0.5 (corresponding to the average of both biomarkers) showed the smallest RMSE and the highest P30 and P10. Also the FAScombi equation outperformed all other combined equations, with the exception of the BIS2 equation, which showed an equivalent performance for older adults (but note that BIS data used to derive the BIS equations are part of the current validation dataset) and the CKD-EPI equation for adults with mGFR <60 mL/min/1.73 m2, where FAS showed equivalent performance results.
新的 FAS cysC 方程的性能优于 CAPA 方程,并且更好(mGFR ≥60 mL/min/1.73 m 2 的成人和老年人)或同等水平( mGFR <60 mL/min/1.73 m 2 的成人)符合 CKD-EPI cysC 方程。在儿童中,FAS crea 方程的 RMSE 统计数据最差(最高),因为一小部分儿童的 Scr/Q crea  <0.67。因此,当 Scr/Q crea  <0.67 [2] 时,我们建议不要使用 FAS crea (或组合的 FAS 方程)。对于成年人,根据表现统计数据(RMSE 和 P30),单一生物标志物 FAS crea 方程相对于单一生物标志物 FAS cysC 方程仍略有偏好。对于老年人来说,两个单一生物标志物 FAS 方程的表现相似。然而,对于所有年龄组,α ≈ 0.5(对应于两种生物标志物的平均值)的 FAS combi 方程显示出最小的 RMSE 和最高的 P30 和 P10。此外,FAS combi 方程的表现优于所有其他组合方程,但 BIS2 方程除外,该方程对于老年人显示出相同的性能(但请注意,用于推导 BIS 方程的 BIS 数据是当前验证数据集)和 mGFR <60 mL/min/1.73 m 2 成人的 CKD-EPI 方程,其中 FAS 显示了相同的性能结果。

When the overall performance statistics for specific age groups was calculated, we found that α ≈ 0.5 corresponding to the average of normalized creatinine and cystatin C biomarker concentrations gave the best performance statistics for all age groups and demonstrated the smallest RMSE and highest P10 and P30 values. Although we calculated the average of both biomarkers and entered this into the FAS equation, it approximated the average of both single biomarker FAS equations (Scr and ScysC), a finding that has been observed by Björk et al. [20, 21] in a Swedish cohort, when combining the Scr-based Lund–Malmö and the ScysC-based CAPA equation. The choice to use a single or the mean of two biomarkers should be based on the clinical context, when conditions are disclosed that invalidate either Scr or ScysC as renal biomarker. The use of Scr may be discouraged in case of severe muscle wasting (anorexic patients, patients with muscle disorder, like Duchenne muscle dystrophy), immobile patients, or elderly cachectic patients with reduced muscle mass. Also, abnormal meat consumption, abnormal muscle development in athletes or weight lifters, or medication usage that affects creatinine generation may have an impact on the validity of creatinine as a renal biomarker. The use of ScysC-based equations may be discouraged when patients are treated with (high dose) glucocorticoids or other medication impacting on the biomarker’s serum concentration [22], in obese patients, tobacco users or patients with thyroid dysfunction or inflammation [11, 23–25]. The combination of both biomarkers has the advantage that it may cancel out the non-GFR-related factors influencing creatinine and cystatin C in different directions compared with mGFR [24–26]. The great advantage of our approach is that the same equation can be used, only the appropriate normalized biomarker has to be chosen (either Scr/Qcrea, or ScysC/QcysC or the average of both). However, the cost of cystatin C is relatively high and additional studies are needed to prove that measuring cystatin C is cost-effective. In the context of GFR estimation, the additional value of cystatin C could be defined by the clinical condition, knowing that non-GFR determinants influence both creatinine and cystatin C.
当计算特定年龄组的整体表现统计数据时,我们发现对应于标准化肌酐和胱抑素 C 生物标志物浓度平均值的 α ≈ 0.5 为所有年龄组提供了最佳的表现统计数据,并显示出最小的 RMSE 和最高的 P10 和 P30 值。尽管我们计算了两个生物标志物的平均值并将其输入 FAS 方程,但它近似于两个单一生物标志物 FAS 方程(Scr 和 ScysC)的平均值,这是 Björk 等人观察到的发现。 [20, 21] 在瑞典队列中,结合基于 Scr 的 Lund-Malmö 和基于 ScysC 的 CAPA 方程。当发现 Scr 或 ScysC 作为肾脏生物标志物无效的情况时,应根据临床情况选择使用单一生物标志物或两种生物标志物的平均值。如果出现严重肌肉萎缩(厌食症患者、肌肉疾病患者,如杜氏肌营养不良症)、行动不便的患者或肌肉量减少的老年恶病质患者,可能不鼓励使用 Scr。此外,肉类消费异常、运动员或举重运动员肌肉发育异常或影响肌酐生成的药物使用可能会影响肌酐作为肾脏生物标志物的有效性。当患者接受(高剂量)糖皮质激素或其他影响生物标志物血清浓度的药物治疗时,可能不鼓励使用基于 ScysC 的方程 [22]、肥胖患者、吸烟者或甲状腺功能障碍或炎症患者 [11, 23] –25]。与mGFR相比,两种生物标志物的组合的优点是可以抵消不同方向上影响肌酐和胱抑素C的非GFR相关因素[24-26]。 我们的方法的巨大优点是可以使用相同的方程,只需选择适当的归一化生物标志物(Scr/Q crea 或 ScysC/Q cysC 或两者的平均值)。然而,胱抑素C的成本相对较高,需要更多的研究来证明测量胱抑素C具有成本效益。在 GFR 估计的背景下,半胱氨酸蛋白酶抑制剂 C 的附加值可以通过临床状况来定义,因为非 GFR 决定因素会影响肌酐和半胱氨酸蛋白酶抑制剂 C。

We also investigated the impact of the weighting factor α on the performance of the FAS equations by varying α (between 0 = FAScysC and 1 = FAScrea) and calculating the difference ‘FAS – mGFR’, on an individual basis. Due to the way the FAS equations are designed, FAScrea FAScysC FAScombi, in the case of the normalized biomarkers Scr/Qcrea ScysC/QcysC. When Scr/Qcrea strongly deviates from ScysC/QcysC, then FAScrea will strongly deviate from FAScysc and FAScombi will lie in-between both single biomarker FAS predictions. We found that, on an individual basis, in approximately one-third of the subjects, the FAScrea equation was closest to mGFR, in one-third of the subjects the FAScysC equation had the lowest individual bias and in one-third the FAScombi equation was the best choice for a specific value of α. In the latter, when mGFR lies between FAScrea and FAScysC predictions, there is always a value of α for which FAScombi =mGFR. We realize that this analysis is mainly speculative as we do not know the optimal value of α in actual clinical situations, but the intention of this analysis was to evaluate in which conditions a preference for single biomarker FAS predictions or for the combined biomarker FAS prediction might exist. Unfortunately, we could not identify specific conditions where one over the other equations was to be preferred (unless the situation where Scr/Qcrea <0.67).
我们还通过改变 α(在 0 = FAS cysC 和 1 = FAS crea 之间)并计算差值 'FAS,研究了权重因子 α 对 FAS 方程性能的影响。 – mGFR',以个人为基础。由于 FAS 方程的设计方式,对于标准化生物标志物 Scr/Q crea  ≈FAS cysC  ≈FAS combi ≈ScysC/Q cysC 。当 Scr/Q crea 强烈偏离 ScysC/Q cysC 时,FAS crea 将强烈偏离 FAS cysc 和 FAS combi 将位于两个单一生物标志物 FAS 预测之间。我们发现,就个体而言,在大约三分之一的受试者中,FAS crea 方程最接近 mGFR,在三分之一的受试者中,FAS cysC 方程具有最低的个体偏差,并且在三分之一的情况下,FAS combi 方程是特定 α 值的最佳选择。在后者中,当 mGFR 位于 FAS crea 和 FAS cysC 预测之间时,始终存在一个 α 值,其中 FAS combi  = mGFR。我们意识到,该分析主要是推测性的,因为我们不知道实际临床情况下 α 的最佳值,但该分析的目的是评估在哪些条件下可能会偏好单一生物标志物 FAS 预测或组合生物标志物 FAS 预测存在。不幸的是,我们无法确定特定条件下哪个方程优于其他方程(除非 Scr/Q crea  <0.67 的情况)。

The strength of this study is the large number of subjects (n = 6132) covering the complete age span from 2 to 100 years of age. This study partially used data from our previous study, where n = 6870 subjects were used to validate the FAScrea equation. Although both studies partially used the same subjects, the ScysC data was not part of the previous evaluation. All ScysC concentrations were analysed with cystatin C assays based on the international certified standard or were back-calculated using calibration curves developed for that purpose. The reference tests used in this study comprise all currently used direct measurement methods: 51Cr-EDTA (plasma/renal clearance), inulin (renal clearance), iohexol (plasma clearance in its different configurations) and iothalamate (renal clearance), illustrating the diversity of mGFR results and demonstrating the robustness of the FAS construction. Moreover, the cohorts used in this study were from different countries in Europe (Norway, Germany, France, Belgium) and the USA (Rochester, MN and the CRIC cohort), making the sample representative for the general Caucasian population and kidney disease population.
这项研究的优势在于受试者数量众多(n = 6132),涵盖了从 2 岁到 100 岁的整个年龄跨度。本研究部分使用了我们之前研究的数据,其中 n = 6870 名受试者用于验证 FAS crea 方程。尽管两项研究部分使用了相同的受试者,但 ScysC 数据并不是之前评估的一部分。所有 ScysC 浓度均根据国际认证标准通过半胱氨酸蛋白酶抑制剂 C 测定进行分析,或使用为此目的开发的校准曲线进行反算。本研究中使用的参考测试包括所有当前使用的直接测量方法: 51 Cr-EDTA(血浆/肾脏清除率)、菊粉(肾脏清除率)、碘海醇(不同配置的血浆清除率)和碘酞酸盐(肾清除率),说明了 mGFR 结果的多样性并证明了 FAS 构建的稳健性。此外,本研究中使用的队列来自欧洲(挪威、德国、法国、比利时)和美国(明尼苏达州罗切斯特和 CRIC 队列)的不同国家,使样本代表了一般白种人和肾脏疾病人群。

Our study has some limitations. First, we did not incorporate different ancestries, and, therefore, this validation study is limited to Caucasians only. Although it is known that creatinine generation (and thus Scr) is affected by ancestry, it is also known that ScysC is not influenced by differences in ancestry. We always have claimed that using appropriate ancestry-specific normalization factors for Scr may solve this problem and consequently the FAS concept remains applicable. Secondly, our goal was to validate the new FAS equations against mGFR and compare them with the existing and recommended equations, not to predict the risk of mortality. Whether the FAS equations are better predictors of mortality is another topic and requires further studies using a different statistical methodology [27].
我们的研究有一些限制。首先,我们没有纳入不同的血统,因此,这项验证研究仅限于白种人。尽管已知肌酐生成(以及 Scr)受血统影响,但也知道 ScysC 不受血统差异的影响。我们一直声称,对 Scr 使用适当的祖先特异性标准化因子可以解决这个问题,因此 FAS 概念仍然适用。其次,我们的目标是验证新的 FAS 方程与 mGFR 的关系,并将其与现有和推荐的方程进行比较,而不是预测死亡风险。 FAS 方程是否能更好地预测死亡率是另一个话题,需要使用不同的统计方法进行进一步研究 [27]。

CONCLUSIONS 结论

The fundamental concept for the Scr-based FAS equation development, namely that mean GFR for healthy subjects evolves along an age-specific curve, and that deviation from that curve is related to the inverse of normalized Scr/Qcrea, also holds true for normalized ScysC/QcysC. The current work shows that the FAS equations display better or equivalent prediction performance than the currently recommended eGFR equations, across the full age spectrum, both in normal and reduced kidney function. The FAS equation is not only applicable to all ages, but also to all currently recommended renal biomarkers. The FAS concept may also be applicable to other renal biomarkers, if appropriately normalized, but this remains to be proven once standardized assays are in place.
基于 Scr 的 FAS 方程开发的基本概念,即健康受试者的平均 GFR 沿着特定年龄的曲线演变,并且该曲线的偏差与标准化 Scr/Q crea 的倒数相关,也适用于标准化的 ScysC/Q cysC 。目前的工作表明,在整个年龄范围内,无论肾功能正常还是肾功能下降,FAS 方程都比当前推荐的 eGFR 方程显示出更好或相当的预测性能。 FAS方程不仅适用于所有年龄段,而且适用于目前推荐的所有肾脏生物标志物。如果适当标准化,FAS 概念也可能适用于其他肾脏生物标志物,但一旦标准化测定到位,这一点仍有待证明。

SUPPLEMENTARY DATA 补充数据

Supplementary data are available online at http://ndt.oxfordjournals.org.
补充数据可在线获取:http://ndt.oxfordjournals.org。

ACKNOWLEDGEMENTS 致谢

The Chronic Renal Insufficiency Cohort (CRIC) Study was conducted by the CRIC Investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). The data from the CRIC Study reported here were supplied by the NIDDK Central Repositories. This manuscript was not prepared in collaboration with Investigators of the CRIC Study and does not necessarily reflect the opinions or views of the CRIC Study, the NIDDK Central Repositories, or the NIDDK. We would also like to thank all patients and researchers, service users, carers and lay people who contributed to the original datasets and who are not mentioned here as co-authors. No specific funding was obtained for this study. S.T.T. (1 R01 DK073537) was supported by research grants from the National Institutes of Health, US Public Health Service.
慢性肾功能不全队列 (CRIC) 研究由 CRIC 研究人员进行,并得到国家糖尿病、消化和肾脏疾病研究所 (NIDDK) 的支持。此处报告的 CRIC 研究数据由 NIDDK 中央存储库提供。本手稿并非与 CRIC 研究的调查人员合作编写,并不一定反映 CRIC 研究、NIDDK 中央存储库或 NIDDK 的意见或观点。我们还要感谢所有为原始数据集做出贡献的患者和研究人员、服务使用者、护理人员和非专业人士,以及此处未提及的共同作者。这项研究没有获得具体资助。 S.T.T. (1 R01 DK073537) 得到了美国国立卫生研究院、美国公共卫生服务部的研究资助。

CONFLICT OF INTEREST STATEMENT
利益冲突声明

None declared. 没有宣布。

(See related article by Agarwal. Glomerular filtration rate estimating equations: practical, yes, but can they replace measured glomerular filtration rate? Nephrol Dial Transplant 2017; 32: 405--407)
(参见 Agarwal 的相关文章。肾小球滤过率估计方程:实用,是的,但它们可以代替测量的肾小球滤过率吗? Nephrol Dial Transplant 2017; 32: 405--407)

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