Comparison of various prediction equations for glomerular filtration rate in Korean pediatric cancer patients: a retrospective study

Bo Ram Kim; Hoon Kook; Hee Jo Baek; Eun Mi Yang

doi:10.3339/ckd.25.011

Child Kidney Dis > Volume 29(2); 2025 > Article

Kim, Kook, Baek, and Yang: Comparison of various prediction equations for glomerular filtration rate in Korean pediatric cancer patients: a retrospective study

Original Article

Child Kidney Dis 2025;29(2):73-82.

Published online: April 30, 2025

DOI: https://doi.org/10.3339/ckd.25.011

Comparison of various prediction equations for glomerular filtration rate in Korean pediatric cancer patients: a retrospective study

Bo Ram Kim¹

, Hoon Kook^1,²

, Hee Jo Baek^1,^2,^*

, Eun Mi Yang^2,^3,^*

¹Department of Pediatrics, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea

²Department of Pediatrics, Chonnam National University Medical School, Gwangju, Republic of Korea

³Department of Pediatrics, Chonnam National University Hospital, Gwangju, Republic of Korea

Correspondence to Eun Mi Yang Department of Pediatrics, Department of Pediatrics, Chonnam National University Hospital, Chonnam National University Medical School, 42 Jebong-ro, Dong-gu, Gwangju 61469, Republic of Korea E-mail: emyang@chonam.ac.kr

Hee Jo Baek Chonnam National University Medical School, Chonnam National University Hwasun Hospital, 322 Seoyang-ro, Hwasun 58128, Republic of Korea E-mail: pe00069@jnu.ac.kr

^*These authors contributed equally to this work as corresponding authors.

Received February 17, 2025 Revised February 21, 2025 Accepted February 25, 2025

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited

Abstract

Purpose

Assessment of kidney function is essential in the management and follow-up of children with cancer. However, the best method to assess the kidney function in children with cancer is unknown.

Methods

The study compared glomerular filtration rate (GFR) using various methods. Four methods for determining GFR are as follows: technetium-99m diethylenetriaminepentaacetic acid clearance (mGFR), serum creatinine (sCr)-based equations (eGFR-Schwartz and eGFR-Pottel), cystatin C (CysC)-based equations (eGFR-Zappitelli1 and eGFR-CKiD1), and combined sCr-CysC-based equations (eGFR-Zappitelli2 and eGFR-CKiD2).

Results

A total of 54 pediatric patients diagnosed with cancer were evaluated. The estimated GFR (eGFR) equations showed moderately correlated with mGFR, with eGFR-Zappitelli2 showing the highest correlation (r=0.639, P<0.001). The GFR-Schwartz and eGFR-Pottel equations had mean biases of –5.72 mL/min/1.73 m² and –6.64 mL/min/1.73 m², with 20.4% and 57.4% of values within 10% and 30% of mGFR for GFR-Schwartz, and 20.4% and 48.1% for eGFR-Pottel, respectively. The CysC-based eGFR showed significant bias with mean bias of –30.9 mL/min/1.73 m² for eGFR-Zappitelli1 and –53.8 mL/min/1.73 m² for eGFR-CKiD1. The combined sCr-CysC-based equations of eGFR-Zappitelli2 had a mean bias of –1.32 mL/min/1.73 m², with 16.7% of values within 10% accuracy and 57.4% within 30% accuracy. The eGFR-CKiD2 had a mean bias of –34.7 mL/min/1.73 m² and the best accuracy, with 27.8% and 59.3% of values falling within 10% and 30%, respectively.

Conclusions

Among the methods evaluated, the combined sCr-CysC-based eGFR showed better accuracy with mGFR compared with the other equations in children with cancer.

Key words: Adolescent; Child; Glomerular filtration rate; Neoplasms

Introduction

Assessment of kidney function is essential in patients with cancer, as such function is crucial for determining the quantity of chemotherapeutic drugs excreted by the kidneys. If kidney function is overestimated, inappropriate drug selection or overdose may occur, potentially causing toxic effects. Conversely, underestimating kidney function may result in inadequate treatment or unnecessary treatment exclusion, increasing the risk of treatment failure. Moreover, cancer and its treatments can result in kidney injury, so detecting kidney injury as early as possible is imperative in patients with cancer. Acute kidney injury was reported in 52.6% of children diagnosed with cancer, and 22.6% of cancer survivors had impaired kidney function [1]. Several risk factors can increase the likelihood of kidney injury among patients with cancer, and altered kidney function may make these patients more prone to developing adverse drug reactions and toxicities [2]. Thus, in this specific population, a precise assessment of kidney function is particularly important.

Glomerular filtration rate (GFR) is currently the best surrogate of kidney function [3]. GFR cannot be measured directly, but many approaches have been developed to indirectly measure or estimate GFR. The gold standard for measured GFR (mGFR) is inulin clearance, but it is impractical in the clinical setting. However, there is good agreement with inulin and nuclear medicine methods, such as ^99mTc-iohexol (technetium-99m iohexol), ^99mTc-DTPA (technetium-99m diethylenetriaminepentaacetic acid), ⁵¹Cr-EDTA (chromium-51 ethylenediaminetetraacetic acid), and iothalamate-based clearance [4]. These methods have been validated and widely used as the accepted standard for determining mGFR [5]. However, nuclear-medicine GFR is expensive, time-consuming, and requires infusions intravenously or subcutaneously. In addition, radiation exposure in pediatric populations is associated with latent risks, including malignancies [6]. Due to these limitations, estimated GFR (eGFR) evaluation using kidney biomarkers has been used and remains relevant in daily practice [3]. Several equations for eGFR have been developed [7]; the most widely used is serum creatinine (sCr)-based equation, with more recent emphasis placed on cystatin C (CysC)-based equations. However, when discrepancies exist between eGFR values calculated via different equations, it is challenging to determine which method most accurately reflects kidney function. In this study, we aimed to investigate the relationship between mGFR and eGFR and to identify the most accurate eGFR method compared to mGFR in children with cancer.

Methods

Study participants and design

Patients aged <18 years who were diagnosed with cancer from January 2020 to December 2023 were retrospectively reviewed. Patients who underwent ^99mTc-DTPA renal scan were enrolled (n=145). The ^99mTc-DTPA renal scan is routinely performed to assess kidney function before chemotherapy or transplantation. Patients aged <1 year and >18 years at the time of examination, or patients for whom blood tests for eGFR calculation were not performed within 1 day before or after the ^99mTc-DTPA renal scan, were excluded from the analysis. A final analysis was conducted for patients who underwent a ^99mTc-DTPA renal scan and had sCr and CysC measurements within 1 day before or after the ^99mTc-DTPA renal scan (n=54) (Fig. 1). GFR was determined by four methods: (1) ^99mTc-DTPA clearance (mGFR); (2) sCr-based equations; (3) CysC-based equation; and (4) combined sCr-CysC-based equations. GFR estimations using several methods were compared with mGFR. Growth was analyzed as age-, sex-, and height-standardized percentiles, with underweight defined as weight-for-age <5th percentile, overweight as weight-for-age ≥95th percentile for children under 2 years and body mass index-for-age ≥85th to <95th percentile for those aged 2 years and older, and obesity as body mass index-for-age ≥95th percentile [8]. The study was approved by the Ethics Committee of Chonnam National University Hwasun (No. CNUHH-2023-172).

mGFR assessment

According to hospital protocol, the patient received an intravenous dose of ^99mTc-DTPA of 37 MBq/10 mCi on either side of their upper extremity veins. The acquisition of dynamic images began simultaneously at a rate of 1 frame per second for the first 60 seconds and 1 frame per 30 seconds for the following 30 minutes. A two-head gamma camera (Discovery NM 630, GE Healthcare) was used for imaging in all patients. The region of interest was manually drawn on the kidney frame, and a semi-lunar background was placed around the lower, outer kidney margin. Using commercially available software, the GFR was calculated automatically after each patient's weight and height were entered onto an online computer [9]. GFR (mL/min) was normalized to a body surface area of 1.73 m²; thus, mGFR is reported in units of mL/min/1.73 m².

Endogenous markers and eGFR calculation

sCr was measured by the Kinetic Jaffe method traceable to isotope dilution mass spectrometry, and CysC was measured by Gentian CysC immunoassay standardized with ERM-DA471/International Federation of Clinical Chemistry and Laboratory Medicine calibrations [10]. Measurements performed after the International Federation of Clinical Chemistry and Laboratory Medicine calibration of the Siemens assay equipment were divided by 1.17 to fit the original calibration of the CysC-based equation [11]. Blood urea nitrogen was measured using an enzymatic method. The following sCr-based equations were used to calculate eGFR:

Height-dependent eGFR-Schwartz (mL/min/1.73m²) [12]=41.3×height (m)/sCr (mg/dL)

Height-independent eGFR-Pottel (mL/min/1.73m²) [13,14]=107.3/[sCr (mg/dL)/Q],

where Q is the median sCr concentration for children based on age and sex.

The CysC-based equation used the Zappitelli formula [15] and a formula derived from the Chronic Kidney Dis¬ease in Children (CKiD) study cohort [16]:

eGFR-Zappitelli1 (mL/min/1.73m²) = 75.94/(CysC^1.17)

eGFR-CKiD1 (mL/min/1.73m²) = 40.6×(1.8/CysC)^0.93

To evaluate the combined sCr and CysC based eGFR equation, we used the Zappitelli formula [15] and the CKiD equation [16]:

eGFR-Zappitelli2 (mL/min/1.73 m²) = (43.82×e^{0.003*height (m)})/[CysC^0.635×sCr^0.547 (mg/dL)]

eGFR-CKiD2 (mL/min/1.73 m²)=39.8×[height (m)/sCr (mg/dL)]^0.456×[1.8/CysC (mg/L)]^0.418×[30/blood urea nitrogen (mg/dL)]^0.079×[^1.076]^male × [height (m)/1.4]^0.179

Statistical analysis

For continuous variables, descriptive statistics were presented as median and interquartile range (IQR), and for qualitative variables as percentages. A Pearson or Spearman correlation analysis was conducted to analyze the relationship between mGFR and eGFR. Bland-Altman plots determined the agreement between mGFR and eGFR values. The difference between mGFR and eGFR on the y-axis were plotted against the mean of the two measurements on the x-axis. The bias was defined as the difference between mGFR and eGFR, and the standard deviation bias are 95% limits of agreement (LOA) were determined. Agreement was quantified using the mean bias, the percentage of eGFR values within 10% (P10) and 30% (P30) of mGFR values. P30 values over 75% indicate good medical decisions according to the National Kidney Foundation [17]. The root mean square error (RMSE) was calculated to assess the imprecision of the different eGFR equations. RMSE was calculated using the following formula, where number represent the sample size [18]: RMSE =∑i=1n(eGFRi-mGFRt)2n

The statistical calculations were performed with IBM SPSS Statistics for window, version 27.0. Significant P-values were considered to be less than 0.05.

Results

Between January 1, 2020, and December 31, 2023, 54 children with cancer were enrolled in the study. Median age at the time of study was 13.0 years (IQR, 11.3–16.0 years). Most study participants (85.2%) had hematologic disease, and 14.8% had a solid tumor. A total of 32 males (59.3%) and 22 females (40.6%) were included (Table 1).

Comparison of mGFR, sCr, and CysC in children with cancer

The median sCr levels were 0.47 mg/dL (IQR, 0.31–0.58 mg/dL) and the median CysC levels were 0.83 mg/L (IQR, 0.65–0.90 mg/L). The median GFR measured by ^99mTc-DTPA renal scan was 96.8 mL/min, and after adjustment for body surface area, the median mGFR was 124.9 mL/min/1.73 m² with an IQR ranging from 93.5 to 202.7 mL/min/1.73 m². mGFR and sCr demonstrated a strong negative association (r=–0.763, P<0.001), while mGFR and CysC showed a moderate negative association (r=–0.415, P=0.002) (Fig. 2).

Relationship between mGFR and eGFR in children with cancer

Using sCr-based eGFR, median values for eGFR-Schwartz and eGFR-Pottel were 144.9 mL/min/1.73 m² and 141.3 mL/min/1.73 m², respectively. Using CysC-based eGFR, median values for eGFR-Zappitelli1 and eGFR-CKiD1 were 123.9 mL/min/1.73 m² and 103.5 mL/min/1.73 m², respectively. Median values for CysC-based eGFR were lower than those for sCr-based eGFR and mGFR. All equations of eGFR showed a moderate significant correlation with mGFR as demonstrated in Fig. 3. sCr-based eGFR (eGFR-Schwartz: r=0.580; eGFR-Pottel: r=0.446) demonstrating a slightly better correlation than CysC-based eGFR (eGFR-Zappitelli1: r=0.415; eGFR-CKiD1: r=0.419). There is the best association between mGFR and the combined sCr and CysC-based eGFR equations (eGFR-Zappitelli2: r=0.639; eGFR-CKiD2: r=0.491). In spite of the highest correlation between eGFR-Zappitelli2 and mGFR, the Spearman correlation coefficient of 0.639 indicates a moderate correlation.

Agreement between mGFR and eGFR in children with cancer

As shown on the Bland-Altman plots (Fig. 4), sCr-based eGFR had less bias and a smaller range in the 95% LOA compared to CysC-based eGFR. The mean bias for eGFR-Schwartz was –5.72 (95% LOA, –154.5 to 143.1 mL/min/1.73 m²), and for eGFR-Pottel, it was –6.64 (95% LOA, –176.4 to 161.3 mL/min/1.73 m²). The P10, P30 values and RMSE were 20.4%, 57.4% and 75.4 for the eGFR-Schwartz, and 20.4%, 48.1% and 85.2 for the eGFR-Pottel, respectively. Both height dependent and height-independent Cr-based eGFRs demonstrated unacceptable poor accuracy (Table 2). The CysC-based eGFR showed significant difference from mGFR, with biases of –30.9 mL/min/1.73 m² (eGFR-Zappitelli1) and –53.8 mL/min/1.73 m² (eGFR-CKiD1). The combined sCr-CysC-based equations of eGFR-Zappitelli2 had a mean bias of –1.32 mL/min/1.73 m², with P10, P30 values, and RMSE of 16.7%, 57.4% and 68.9, respectively. The eGFR-CKiD2 had a mean bias of –34.7 mL/min/1.73 m² and showed the best accuracy, with P10 and P30 values of 27.8% and 59.3%, respectively. However, even the best P30 value for eGFR-CKiD2 showed unacceptably poor accuracy (Table 2, Fig. 4).

Discussion

Our study aims to verify the correlation and agreement between eGFR and mGFR in children with cancer. Discrepancies between mGFR and eGFR were common, and accuracy of Cr-based and/or CysC-based eGFR compared to mGFR was insufficient. Thus, when accurately assessing kidney function, direct measurement of GFR is needed in patients with cancer. GFR is indeed the most reliable indicator of kidney function. As a replacement for classic inulin clearance, ^99mTc-DTPA renal scan has become widely accepted in Europe and the United States [19-21]. It is relatively cost-effective, provides separate functional assessments for each kidney, and is less invasive than inulin clearance [7]. However, the accuracy of ^99mTc-DTPA can vary depending on its commercial source [22]. Indirect GFR assessment using kidney biomarkers is more suitable for routine clinical practice. Among these biomarkers, sCr is the most widely used, but it has limitations due to its dependency on muscle mass and nutrition, interference with histamine H2 antagonists and other medications, and a large volume of distribution [23,24]. In patients with cancer, muscle wasting and certain medications (e.g., H2 antagonist, trimethoprim) may affect eGFR, leading to inaccurate [3]. CysC is an alternative marker for GFR assessment. Plasma CysC levels stabilize from the age of 1 year and are independent of muscle mass [25]. Additionally, CysC is distributed only in the extracellular volume, which explains its shorter half-life compared to sCr [23]. Therefore, the potential of CysC as a marker for GFR has been widely studied and is considered promising. However, in this study, sCr showed a better correlation with mGFR compared to CysC in children with cancer. Non-GFR factors associated with CysC, such as acute inflammation [26], and corticosteroid use [27] may influence its accuracy. A recent large study in patients with solid tumors found that sCr overestimated GFR, CysC underestimated GFR, and an equation combining both sCr and CysC was the most accurate and precise method for estimating GFR [28].

Discordances between sCr-based eGFR and CysC-based eGFR were common. When there are discrepancies between eGFR values calculated using different equations, it becomes challenging to determine which method most accurately reflects kidney function. In patients with chronic illnesses, larger discrepancies may be observed due to a reduced muscle mass, administration of high-dose glucocorticoids, severe thyroid disease, and reabsorption of filtered Cr or interference from heterophilic antibodies in the antibody-based CysC assay [10,29,30]. In patients with cancer, especially, muscle loss, poor nutrition, anemia, hypoalbuminemia, and medications such as trimethoprim or H2 blockers can affect GFR estimation [3,31]. According to previous studies, incorporating CysC into the eGFR equation improved precision in patients with malnutrition and sarcopenia [28,32]. Combining sCr and CysC for the most accurate estimation of GFR is recommended for both children [16] and adults [10]. As per the Kidney Disease: Improving Global Outcomes guidelines, eGFR based on sCr and CysC is more accurate in cancer studies, but it may be inaccurate in frail individuals or cancers with high cell turnover [33]. This study compared different eGFR formulas (sCr, CysC, and sCr-CysC-based formulas) with the ^99mTc-DTPA renal scan in children with cancer and found that eGFR-CKiD2, which is based on both sCr and CysC, demonstrated the highest accuracy. However, the eGFR-CKiD2 values lacked sufficient accuracy, as the proportion of eGFR within 30% of mGFR was below the 75% threshold defined by the National Kidney Foundation [17]. Since GFR estimating equations are only accurate in a steady state, their accuracy may be reduced in cancer patients, whose kidney function may be more vulnerable. The patients in this study were evaluated for kidney function using a ^99mTc-DTPA renal scan before receiving anticancer treatment or undergoing transplantation, and they did not take medications that could affect CysC levels, such as high-dose steroids. Of the patients, 11% were underweight, while the majority had a normal weight or higher. However, the eGFR demonstrated low accuracy in children with cancer. Accurate assessment of kidney function in oncology patients is crucial in profiling survival risk, determining treatment, and modulating drug toxicity. Therefore, isotope GFR measurement should be considered in patients with cancer.

This study had several limitations. First, the number of patients was small, limiting the strength of potential conclusions. Second, we did not use a gold standard method for measuring GFR, such as inulin clearance. Although highly accurate, this method is not commercially available. Third, the GFR measurements in this study were made using camera-based methods rather than plasma samples, which may not be accurate enough to be used as a standard. Fourthly, most of the patients had normal kidney function, with only about 20% having mild kidney insufficiency at stage 2 CKD. In clinical practice, accurate GFR measurement is crucial in patients with reduced kidney function; however, no patients with moderate to severe reductions in kidney function were included in this study. Finally, we did not account for medication use and patients’ comorbidities in the analysis, nor did we assess hydration status. However, most patients were in a stable state, undergoing routine kidney function workup before treatment or transplantation. In spite of this, this study is valuable as few studies have compared mGFR with eGFR in children with cancer. Based on overall performance, combined use of CysC and sCr based eGFR is better to estimating GFR in children with cancer. However, due to the imprecision of eGFR, there is no truly accurate and precise substitute for the direct measurement in children with cancer.

Notes

Ethical statements

The study was approved by the Ethics Committee of Chonnam National University Hwasun (No. CNUHH-2023-172). Written informed consent was waived by the Ethics Committee because this was a retrospective study.

Conflicts of interest

Eun Mi Yang is an editorial board member of the journal but was not involved in the peer reviewer selection, evaluation, or decision process of this article. No potential conflicts of interest relevant to this article were reported.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2023-00217317).

Author contributions

Conceptualization: EMY

Data curation: BRK

Formal analysis: EMY

Funding acquisition: EMY

Investigation: HK, HJB

Methodology: BRK

Project administration: HJB

Visualization: EMY

Writing–original draft: EMY

Writing–review & editing: BRK, HK, HJB

All authors read and approved the final manuscript.

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Fig. 1.

Study population. ^99mTc-DTPA, technetium-99m diethylenetriaminepentaacetic acid.

Fig. 2.

Relationship of measured glomerular filtration rate to serum creatinine and cystatin C. (A) Scatter plot of ^99mTc-DTPA renal scan versus serum creatinine. (B) Scatter plot of ^99mTc-DTPA renal scan versus cystatin C. ^99mTc-DTPA, technetium-99m diethylenetriaminepentaacetic acid.

Fig. 3.

Correlation between measured GFR and eGFR. Scattered plot of eGFR versus ^99mTc-DTPA renal scan. The correlation between the ^99mTc-DTPA renal scan and (A) eGFR-Schwartz, (B) eGFR-Pottel, (C) eGFR-Zappitelli1, (D) eGFR-CKiD1, (E) eGFR-Zappitelli2, and (F) eGFR-CKiD2 was analyzed. The correlation coefficients (r) and P-values for each were reported. ^99mTc-DTPA, technetium-99m diethylenetriaminepentaacetic acid; GFR, glomerular filtration rate; eGFR, estimated glomerular filtration rate; CKiD, Chronic Kidney Disease in Children.

Fig. 4.

Bland-Altman plots of mGFR and eGFR using (A) eGFR-Schwartz, (B) eGFR-Pottel, (C) eGFR-Zappitelli1, (D) eGFR-CKiD1, (E) eGFR-Zappitelli2, and (F) eGFR-CKiD2. Mean bias and the 95% limits of agreements are represented by one horizontal solid line and two dash line. ^99mTc-DTPA, technetium-99m diethylenetriaminepentaacetic acid; GFR, glomerular filtration rate; eGFR, estimated GFR; mGFR, measured GFR; CKiD, Chronic Kidney Disease in Children.

Table 1.

Baseline characteristics of patients

Variable	Value
Age (yr)	13.0 (11.3–16.0)
Male sex	32 (59.3)
Underlying disease
Hematologic	46 (85.2)
Solid tumors	8 (14.8)
Height (cm)	154.6 (139.5–160.6)
Weight (kg)	40.9 (31.6–55.9)
Body mass index (kg/m²)	16.6 (16.0–21.0)
Underweight	6 (11.1)
Obesity	10 (18.5)
Renal variables
Blood urea nitrogen (mg/dL)	7.48 (6.41–8.72)
Creatinine (mg/dL)	0.47 (0.31–0.58)
Cystatin C (mg/L)	0.83 (0.65–0.90)
GFR ^99mTc-DTPA renal scan (mL/min)	96.8 (87.0–118.3)
mGFR ^99mTc-DTPA renal scan (mL/min/1.73m²)^a)	124.9 (93.5–202.7)
eGFR-Schwartz (mL/min/1.73m²)	144.9 (113.2–179.0)
eGFR-Pottel (mL/min/1.73m²)	141.3 (126.7–171.3)
eGFR-Zappitelli1 (mL/min/1.73m²)	123.9 (105.3–154.6)
eGFR-CKiD1 (mL/min/1.73m²)	103.5 (90.9–123.4)
eGFR-Zappitelli2 (mL/min/1.73m²)	160.2 (118.6–183.2)
eGFR-CKiD2 (mL/min/1.73m²)	128.0 (105.5–140.9)
CKD stage 2 or higher^b)	11 (20.4)

Values are presented as median (interquartile range) or number (%).

^99mTc-DTPA, technetium-99m diethylenetriaminepentaacetic acid; GFR, glomerular filtration rate; mGFR, measured GFR; eGFR, estimated GFR; CKiD, Chronic Kidney Disease in Children; CKD, chronic kidney disease.

^a)mGFR: normalized to body surface area of GFR ^99mTc-DTPA renal scan.

^b)Values based on mGFR.

Table 2.

Bias, precision, and accuracy of eGFR compared with mGFR in children with cancer

Variable	Mean bias	P10 (%)^a)	P30 (%)^b)	RMSE^c)
eGFR-Schwartz (mL/min/1.73 m²)	–5.72	20.4	57.4	75.4
eGFR-Pottel ( mL/min/1.73 m²)	–6.64	20.4	48.1	85.2
eGFR-Zappitelli1 (mL/min/1.73 m²)	–30.9	20.4	51.9	92.2
eGFR-CKiD1 (mL/min/1.73 m²)	–53.8	25.9	53.7	104.8
eGFR-Zappitelli2 (mL/min/1.73 m²)	–1.32	16.7	57.4	68.9
eGFR-CKiD2 (mL/min/1.73 m²)	–34.7	27.8	59.3	93.7

GFR, glomerular filtration rate; eGFR, estimated GFR; mGFR, measured GFR; P10, 10% accuracy; P30, 30% accuracy; RMSE, root mean square error; CKiD, Chronic Kidney Disease in Children.

^a)Proportion of eGFR within ±10% of mGFR.

^b)Proportion of eGFR within ±30% of mGFR.

^c)RMSE was calculated using the following formula: RMSE=∑i=1n(eGFRi-mGFRt)2n