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Article

Performance of Nuclear Magnetic Resonance-Based Estimated Glomerular Filtration Rate in a Real-World Setting

by
Amauri Schwäble Santamaria
1,†,
Marcello Grassi
1,†,
Jeffrey W. Meeusen
2,
John C. Lieske
2,3,
Renee Scott
2,
Andrew Robertson
1 and
Eric Schiffer
1,*
1
Department of Research and Development, Numares AG, 93053 Regensburg, Germany
2
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
3
Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Bioengineering 2023, 10(6), 717; https://doi.org/10.3390/bioengineering10060717
Submission received: 20 April 2023 / Revised: 25 May 2023 / Accepted: 6 June 2023 / Published: 13 June 2023
(This article belongs to the Special Issue Chronic Kidney Disease: Diagnosis and Treatment)
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Abstract

:
An accurate estimate of glomerular filtration rate (eGFR) is essential for proper clinical management, especially in patients with kidney dysfunction. This prospective observational study evaluated the real-world performance of the nuclear magnetic resonance (NMR)-based GFRNMR equation, which combines creatinine, cystatin C, valine, and myo-inositol with age and sex. We compared GFRNMR performance to that of the 2021 CKD-EPI creatinine and creatinine-cystatin C equations (CKD-EPI2021Cr and CKD-EPI2021CrCys), using 115 fresh routine samples of patients scheduled for urinary iothalamate clearance measurement (mGFR). Median bias to mGFR of the three eGFR equations was comparably low, ranging from 0.4 to 2.0 mL/min/1.73 m2. GFRNMR outperformed the 2021 CKD-EPI equations in terms of precision (interquartile range to mGFR of 10.5 vs. 17.9 mL/min/1.73 m2 for GFRNMR vs. CKD-EPI2021CrCys; p = 0.01) and accuracy (P15, P20, and P30 of 66.1% vs. 48.7% [p = 0.007], 80.0% vs. 60.0% [p < 0.001] and 95.7% vs. 86.1% [p = 0.006], respectively, for GFRNMR vs. CKD-EPI2021CrCys). Clinical parameters such as etiology, comorbidities, or medications did not significantly alter the performance of the three eGFR equations. Altogether, this study confirmed the utility of GFRNMR for accurate GFR estimation, and its potential value in routine clinical practice for improved medical care.

1. Introduction

Glomerular filtration rate (GFR) is a critical clinical parameter in routine medical care and for the diagnosis and monitoring of kidney diseases [1]. To minimize systematic errors among and between patient groups, equations for estimated GFR (eGFR) incorporate demographic variables such as sex, age, and—until recently—race, together with endogenous filtration markers such as creatinine and cystatin C. As race is a social construct [2], healthcare professionals called for a reassessment of the inclusion of race in eGFR equations. In response, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) recently published the 2021 CKD-EPI eGFR equations that do not incorporate race [3]. Several studies demonstrated the clinical impact of implementing the new race-free equations on global patient management [4], and its benefit for kidney transplantation eligibility listing for Black patients [5,6,7,8].
The National Kidney Foundation (NKF) and the American Society of Nephrology (ASN) Task Force recently recommended the implementation of the race-free 2021 CKD-EPI creatinine equation (CKD-EPI2021Cr) for US adults [9,10]. They also recommended facilitating the routine use of cystatin C, because equations combining creatinine and cystatin C (e.g., CKD-EPI2021CrCys) are more accurate [9,10]. Finally, the Task Force encouraged the development of accurate, unbiased, precise, race-free equations integrating new endogenous filtration markers, to promote health equity [9,10]. We recently described GFRNMR, a nuclear magnetic resonance (NMR)-based equation without a race variable, combining the serum biomarkers creatinine, cystatin C, valine, and myo-inositol [11,12]. GFRNMR demonstrated lower bias, and higher accuracy and precision than the 2009 and 2012 CDK-EPI equations [11], and demonstrated analytical performance suitable for routine clinical use [12]. When compared with the 2021 creatinine CKD-EPI2021Cr equation, GFRNMR showed comparable bias and significantly higher P15 accuracy in patients with or without a kidney transplant, and a stronger agreement with CKD staging by measured GFR (mGFR) in kidney transplant recipients [13]. Therefore, GFRNMR may fulfill the recommendation of the NKF-ASN Task Force and hold promise for an alternative, well-performing and equitable eGFR equation.
Given the importance of accurately estimating GFR in medical care, particularly for proper CKD staging and clinical decision making, there is a clear need for independent validation of GFRNMR in a real-world setting to confirm its potential use for improved patient management. Therefore, the aim of this prospective observational study was to validate GFRNMR in a setting of routine clinical practice. Blood was collected from patients scheduled for clinically indicated urinary iothalamate clearance measurement, and GFR was estimated in fresh sera using GFRNMR and the guideline-recommended CKD-EPI2021Cr and CKD-EPI2021CrCys race-free equations. A wide range of clinical data describing the etiology, comorbidities, and medications of the study sample were collected to investigate their potential impact on eGFR results.

2. Materials and Methods

2.1. Study Design and Participants

A prospective, observational, single center (Mayo Clinic, Rochester, MN, USA) study was conducted in 120 patients ≥ 18 years old scheduled for urinary iothalamate clearance measurement between May and September 2022. During the recruiting period, all patients scheduled for mGFR as part of clinical routine were screened for eligibility. Patients under hemodialysis or under peritoneal dialysis within seven days before urinary iothalamate clearance measurement were excluded from enrolment. Some patients were recruited repeatedly in the study, and the results considered as independent. Patients’ demographic and clinical data (including comorbidities and medications) were documented on the day of examination.
The aim of the study was to validate GFRNMR in the actual daily routine setting of clinical practice. The clinical reference standard was GFR measured by urinary iothalamate clearance, which is part of standard care at Mayo Clinic (Rochester, MN, USA). Fresh blood samples were collected immediately prior to mGFR measurement to estimate GFR (eGFR) using the NMR-based GFRNMR equation [11,12] and the guideline-recommended 2021 CKD-EPI equations (CKD-EPI2021Cr and CKD-EPI2021CrCys) [3,9,10]. Physicians and patients were blinded to the eGFR results.
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the respective Institutional Review Board (Mayo Clinic IRB #21-007723, dated 11 October 2021). All patients gave written informed consent prior to enrolment.

2.2. Sample Collection and Storage

A total of 120 blood samples (9 mL) were collected by venipuncture immediately before injection of iothalamate to measure GFR by urinary iothalamate clearance. Whole blood was allowed to clot for 30–120 min at room temperature and was centrifuged to collect blood serum. Fresh sera (at least 3 mL) were aliquoted (two 1 mL and two 0.5 mL aliquots) and stored at 4 °C until testing. On the day of examination, one 0.5 mL aliquot was used to measure creatinine and cystatin C levels, as described in Section 2.3. Then, one 1 mL aliquot was used within four days of blood collection for NMR measurement, as described in Section 2.3.

2.3. Laboratory Methods

GFR was measured at the Mayo Clinic Renal Testing Laboratory (Rochester, MN, USA) by urinary iothalamate clearance (non-radiolabeled) using liquid chromatography-tandem mass spectrometry, as previously reported [13,14]. The mGFR was normalized to body surface area according to the Dubois equation (body surface area = height0.725 × weight0.425 × 0.007184) and expressed as milliliter per minute per 1.73 m2 body surface area (mL/min/1.73 m2).
Biomarker measurements were performed on fresh refrigerated sera at Central Clinical Chemistry Laboratory, Mayo Clinic, Rochester, MN, USA. Serum creatinine was measured by enzymatic assay standardized to international reference materials [15], using Roche Cobas clinical analyzers (c701 or c501, Roche Diagnostics, Indianapolis, IN, USA). Cystatin C was measured by an immunoturbidometric assay (Gentian ASA, Moss, Norway) that was traceable to an international reference material [16], using a Roche Cobas c501 analyzer (Roche Diagnostics; Indianapolis, IN, USA).
Serum creatinine, valine, and myo-inositol were measured by NMR spectroscopy as previously described [11,13,17]. Briefly, 540 μL serum was mixed with 60 μL of Axinon® serum additive solution and 600 μL was transferred into a 5 mm NMR tube with a barcoded cap. Samples were pre-heated at 37 °C for 7.5 min before NMR measurement in a Bruker Avance III 600 MHz, and a 5 mm PATXI probe equipped with automatic Z gradients shimming. The 1H-NMR spectra were recorded using a spectral width of 20 ppm, with a recycling delay of 1.5 s, 16 scans, and a fixed receiver gain of 50.4. A cycling time d2 of 8 ms was used together with a corresponding T2 filter of 112 ms. The mixing time τ between two consecutive spin echoes was 400 μs. The NMR data were automatically phase- and baseline-corrected using the lactate doublet at 1.32 ppm as reference. Metabolite quantification used curve-fitted pseudo-Voigt profiles, as previously described [11,17]. In case of analysis failure, the second 1 mL aliquot was used to repeat the NMR analysis. In case of repeated failure, the sample was excluded from final analysis.
GFR was estimated (eGFR) via three methods: GFRNMR, CKD-EPI2021Cr, and CKD-EPI2021CrCys. GFRNMR test results were automatically generated by the Axinon® NMR software (Numares AG, Regensburg, Germany), integrating age, sex, cystatin C (immunoturbidometric assay) and NMR measurements of creatinine, myo-inositol, and valine. The 2021 CKD-EPI equations were calculated within R using the reported formulas [3], combining either age, sex, and creatinine (enzymatic assay) for CKD-EPI2021Cr, or age, sex, creatinine (enzymatic assay), and cystatin C (immunoturbidometric assay) for CKD-EPI2021CrCys.

2.4. Statistical Analysis

Sample size was estimated based on existing GFRNMR P20 accuracy results using the MedCalc Statistical Software version 12.7.7, according to Machin et al. [18]. Based on an estimated error not exceeding 15% absolute, with a two-sided alpha level of 5% and a power of 90%, a minimum of 106 patients was required for the study. Including a safety margin of 15% for possible dropouts, a total of 120 patients was planned for enrolment.
Performance evaluation (signed median bias, precision, accuracy, and precision intervals) was conducted in all enrolled patients with a valid GFRNMR result. Subgroup analyses were also conducted to evaluate the impact of disease, comorbidities, or medication on the performance of each eGFR equation.
All statistical evaluations were performed within R 4.0.2 [19]. Data structures were handled with data.frame [20], data.table [21], and archivist [22] packages. Bootstrap procedures were implemented via the boot package [23,24]. Visualization was performed with ggplot2 [25]. In bias, precision and accuracy analyses, the respective 95% confidence intervals (CI) were calculated using the bootstrap method. In all analyses, p-values ≤ 0.05 were considered statistically significant.
Bias was calculated as ‘eGFR-mGFR’ and expressed as median signed bias to mGFR. Pairwise significance levels between bias distributions were assessed via the Wilcoxon-signed rank test [26,27] with p-value correction for multiple testing according to Benjamini-Hochberg [28,29].
Precision was assessed by the interquartile range (IQR) of the difference to mGFR. Significance of differences was assessed via the bootstrap method.
Accuracy was evaluated by the percentage of samples with an eGFR within 15% (P15), 20% (P20), or 30% (P30) of mGFR. Pairwise comparisons were tested using the McNemar’s Chi-squared test [30] and Benjamini-Hochberg correction [28,29].
Distribution of mGFR at any given eGFR was assessed by fitting a quantile regression model for quantiles 2.5th, 10th, 25th, 50th, 75th, 90th, and 97.5th (one model for each quantile value, for a total of 7 quantile regression models). Quantile regression differs from the ordinary least squares (OLS) regression in that OLS regression estimates the conditional mean, whereas quantile regression estimates the conditional quantile of interest (e.g., 75th quantile or 50th quantile) [31,32,33,34,35]. For each of the studied equations (GFRNMR, CKD-EPI2021Cr and CKD-EPI2021CrCys), mGFR distribution was calculated at given eGFR thresholds (45, 60 and 90 mL/min/1.73 m2). These thresholds were chosen as they represent GFR decision values for CKD staging [36]. The 95% prediction interval (PI) of mGFR for a given eGFR threshold (e.g., 30 mL/min/1.73 m2) was defined as the predicted mGFR by the 97.5th quantile model at eGFR equals 30 mL/min/1.73 m2 minus the predicted mGFR by the 2.5th quantile model at eGFR equals 30 mL/min/1.73 m2. This 95% PI is thus expected to include approximately 95% of the mGFR values from patients with a given eGFR.

3. Results

3.1. Patient Characteristics

A total of 120 sera were collected as part of routine clinical practice from patients whose mGFR was determined by urinary iothalamate clearance (Figure 1). GFR was estimated using three race-free eGFR equations: NMR-based GFRNMR [11,12], guideline-recommended CKD-EPI2021Cr, and CKD-EPI2021CrCys [3]. Of the 120 tested samples, 115 with a valid GFRNMR result were included in the analysis (Figure 1).
Demographic and clinical characteristics of the study population (n = 115) are shown in Table 1. The study population included mainly White participants (97.4%), 56.5% were men, and mean (standard deviation [SD]) age was 54.9 (10.6) years. Most patients were solid-organ transplant recipients (88.7%, with 60% post kidney transplantation), had chronic kidney disease (CKD; 66.1%), and presented comorbidities, such as hypertension (69.6%) and dyslipidemia (65.2%). The majority (114/115 [99.1%]) of patients were on medications including immunosuppressive agents (87.8%), corticosteroids (47.8%), and beta blockers (35.7%) (Table 1).
Mean (SD) mGFR in the study population was 64.2 (20.8) mL/min/1.73 m2, and mean (SD) eGFR for GFRNMR, CKD-EPI2021Cr, and CKD-EPI2021CrCys were 64.1 (18.7), 63.6 (20.2), and 63.8 (21.5) mL/min/1.73 m2, respectively (Table 1).

3.2. Performance of eGFR Equations in Routine Clinical Samples

3.2.1. Bias, Precision, and Accuracy

Median bias to mGFR of the three eGFR equations was overall low and slightly overestimated mGFR (positive median bias ranging from 0.4 to 2.0 mL/min/1.73 m2) (Table 2). Median bias of GFRNMR was not statistically significantly different from that of the 2021 CKD-EPI equations (p > 0.05 in pairwise comparisons; Table 2). However, despite comparable median bias, the bias distribution of GFRNMR differed from that of the CKD-EPI equations. GFRNMR bias distribution was unimodal with one narrow peak centered around its median value of 2.0 mL/min/1.73 m2, while CKD-EPI2021Cr and CKD-EPI2021CrCys bias distribution were bimodal, with one peak of negative biases and another of positive biases (Figure 2). This heterogeneous bias distribution of eGFR determined by CKD-EPI2021Cr and CKD-EPI2021CrCys indicates that these equations often underestimated GFR, as opposed to GFRNMR.
GFRNMR showed a significantly higher precision than CKD-EPI2021Cr (p = 0.01) and CKD-EPI2021CrCys (p = 0.01), with an interquartile range (IQR) of the difference to mGFR of 10.5 mL/min/1.73 m2 (Table 2).
GFRNMR accuracy ranged from 66.1% (P15) to 95.7% (P30) (Table 2), and was higher than that of both 2021 CKD-EPI equations at any error tolerance cutoffs (Table 2 and Figure 3). GFRNMR statistically significantly outperformed CKD-EPI2021Cr and CKD-EPI2021CrCys equations regarding P15 and P30 accuracy (p-values between 0.006 and 0.02; Table 2). For P20 accuracy, GFRNMR significantly outperformed CKD-EPI2021CrCys (p = 0.001), but not CKD-EPI2021Cr (80.0% vs. 73.0% for GFRNMR vs. CKD-EPI2021Cr; p = 0.19) (Table 2).

3.2.2. Prediction Intervals

For each of the studied equations (GFRNMR, CKD-EPI2021Cr and CKD-EPI2021CrCys), we assessed the distribution of mGFR at eGFR thresholds used to define CKD stages, by determining the mGFR 95% prediction intervals (PI). The 95% PI is expected to include approximately 95% (2.5th to 97.5th percentiles) of the mGFR values from patients with a given eGFR. The width of the 95% PI has direct clinical implications on the precision of CKD staging [37]. Due to the small number (n = 3) of patients with GFR < 30 mL/min/1.73 m2 in our study population (Table 1, CKD stages G4 and G5), we considered only the eGFR thresholds of 45, 60, and 90 mL/min/1.73 m2.
At the three investigated eGFR thresholds (45, 60 and 90 mL/min/1.73 m2), the width of the 95% PI, but also of the 50% PI, was consistently smaller for GFRNMR compared to either CKD-EPI2021Cr or CKD-EPI2021CrCys (Figure 4). The predicted median mGFR (50th percentile) for GFRNMR of 45, 60, and 90 mL/min/1.73 m2 was 44.7, 58.2, and 85.4 mL/min/1.73 m2, respectively.
At a GFRNMR of 45 mL/min/1.73 m2, 50% of mGFR ranged from 41.3 to 50.0 mL/min/1.73 m2, 80% from 37.5 to 58.6 mL/min/1.73 m2, and 95% from 34.3 to 61.7 mL/min/1.73 m2.
At a GFRNMR of 60 mL/min/1.73 m2, 50% of mGFR ranged from 54.1 to 65.0 mL/min/1.73 m2, 80% from 49.9 to 75.1 mL/min/1.73 m2, and 95% from 44.3 to 81.9 mL/min/1.73 m2.
At a GFRNMR of 90 mL/min/1.73 m2, 50% of mGFR ranged from 79.7 to 95.0 mL/min/1.73 m2, 80% from 74.6 to 108.1 mL/min/1.73 m2, and 95% from 64.3 to 122.4 mL/min/1.73 m2. Thus, at this eGFR threshold, the 95% PI did not cross the adjacent CKD stage cutoff of 60 mL/min/1.73 m2, in contrast to the 95% PI at the eGFR thresholds of 45 and 60 mL/min/1.73 m2, which crossed the next CKD stage cutoff (either p2.5 or p97.5 percentile) by no more than 1.7 mL/min/1.73 m2 (Figure 4).

3.3. Impact of Disease, Comorbidities and Medication on the Performance of eGFR Equations

To evaluate the impact of clinical parameters on the performance of the three eGFR equations, subgroup analyses were performed according to the documented etiology, comorbidities, and administered medications. This analysis was limited to groups of >25 samples and to conditions related to kidney function or dysfunction (Table 1). Bias distribution for each equation was compared between subgroups of patients with vs. without CKD, kidney transplantation, hypertension, or hyperlipidemia, and those treated vs. untreated with immunosuppressive agents, corticosteroids, or beta blockers (Table 3).
Except for CKD-EPI2021Cr and CKD-EPI2021CrCys between immunosuppressive treatment groups (CKD-EPI2021Cr: median bias [95% CI] of 1.2 [−1.5; 5.5] vs. −11.8 [−21.1; −9.4]; p = 0.002; CKD-EPI2021CrCys: median bias [95% CI] of 1.9 [−1.0; 6.0] vs. −10.7 [−17.8; −5.6]; p = 0.007), and GFRNMR between beta-blocker treatment groups (median bias [95% CI] of 0.0 [−3.0; 4.0] vs. 2.0 [0.0; 4.0]; p = 0.05), bias distribution between subgroups was not statistically significantly different (Table 3).

4. Discussion

The performance of three eGFR equations (GFRNMR, CKD-EPI2021Cr, and CKD-EPI2021CrCys) was compared using fresh samples of patients with clinically ordered urinary iothalamate clearance mGFR. The three equations showed an overall low median bias, however the bias distributions differed in subtle but potentially important ways. Bias distribution for GFRNMR was centered around its median of 2.0 mL/min/1.73 m2, while that of the CKD-EPI equations was broad and bimodal, indicating heterogeneity in performance. This heterogenicity may be reflective of the different confounders for creatinine and cystatin C, which can be in opposite directions. On the other hand, GFRNMR outperformed the 2021 CKD-EPI equations in terms of precision (interquartile range to mGFR of 10.5 mL/min/1.73 m2) and accuracy (P15, P20, and P30 of 66.1%, 80.0%, and 95.7%, respectively).
Similar to the 2021 CKD-EPI equations, GFRNMR is a race-free equation, and like CKD-EPI2021CrCys, GFRNMR integrates both creatinine and cystatin C as biomarkers in addition to age and sex. The improved precision and accuracy of GFRNMR compared to CKD-EPI2021CrCys supports the benefit of adding myo-inositol and valine to the eGFR equation. These results further support published reports and recommendations on the need for adequate biomarker constellations to improve GFR estimation [9,10,11,17,38,39,40].
The GFRNMR performance evaluation reported here confirms in an independent cohort the findings of our first validation study [11]. Both studies, employing an iothalamate-based mGFR reference standard and including patients with comparable demographics (Mayo Clinic, Rochester, NY, USA), confirmed the results obtained for GFRNMR as to precision (10.5 vs. 13.0 mL/min/1.73 m2 in the present vs. previous study) and accuracy (66.1% vs. 61.2% [P15], 80.0% vs. 71.5% [P20], 95.7% vs. 87.2% [P30], in the present vs. previous study). This finding further supports the suitability of GFRNMR for use in clinical routine settings, particularly for e.g., the accurate classification of CKD stages, assessment of renal impairment and renal drug dose adjustment.
For the first time, we were able to systematically investigate the impact of a variety of etiologies, comorbidities, and medications on GFRNMR results. Although clinical data were collected on a wide range of conditions, the statistical analysis had to be restricted to groups of > 25 samples and to univariate statistics to ensure result integrity. While in most comparisons, no statistically significant differences in bias distributions were found between groups, we observed a significant difference in bias for GFRNMR between beta-blocker treatment groups (median bias [95% CI] of 0.0 [−3.0; 4.0] vs. 2.0 [0.0; 4.0]; p = 0.05). In the kidney, adrenergic receptors mediate vasoconstriction, renin secretion, and vasodilation, respectively [41,42,43]. Blockade of beta-receptors may therefore affect renal blood flow and possibly GFR through intrarenal effects [41,42,43]. Interestingly, all investigated eGFR equations tended to underestimate mGFR in the beta-blocker treatment group compared to the control group, an observation consistent with a decreased renal perfusion. However, the univariate nature of our evaluation due to the small sample size prevented us from correcting the observed effect sizes for important covariates, such as age, sex, race, other medications, or CKD etiology. Significantly larger patient populations for such an analysis is warranted.
Limitations of our study include its small population size (n = 115), its single center nature, the high proportion (88.7%) of solid-organ transplant recipients, the small proportion (<3%) of non-White participants, and the small number (n = 3) of patients with low (<30 mL/min/1.73 m2) mGFR. The latter is critical because low mGFR thresholds represent essential clinical decision points, such as to eligibility to kidney transplantation listing (at GFR < 20 mL/min/1.73 m2 in the US). The weak representation of non-White participants and of patients with low mGFR, and the strong representation of certain medical conditions (such as solid-organ transplantation) constitute the drawback of a real-world single-center study. Future real-world studies should be conducted at multiple centers to cover more ethnicities, more patients with severe CKD, and more diverse medical conditions.
Strengths of our study include its real-world setting, the subgroup analysis according to comorbidities and medication, and the use of a single standardized method for mGFR determination (urinary iothalamate clearance). This is important because the use of mixed mGFR determination methods might introduce a bias in eGFR performance interpretation [3,11,14,44,45]. In fact, recent studies evaluating GFRNMR exclusively relied on urinary iothalamate clearance (this study and [13]).
Overall, our results add real-world eGFR validation data to the growing body of evidence confirming the suitability of GFRNMR to meet the NKF-ASN Task Force recommendations, and hold promise for an alternative, well-performing, and equitable eGFR equation.

5. Conclusions

This real-world study demonstrated a superior performance of GFRNMR compared to the CKD-EPI2021Cr and CKD-EPI2021CrCys equations on routine samples. This study thus validates the use of GFRNMR for accurate estimation of GFR in routine clinical practice, potentially improving patient management.

Author Contributions

Conceptualization, A.S.S. and E.S.; methodology, A.S.S., M.G. and R.S.; validation, J.W.M. and R.S.; formal analysis, M.G.; investigation, A.S.S. and M.G.; resources, J.W.M., J.C.L. and E.S.; data curation, A.S.S. and M.G.; writing—review and editing, A.S.S., M.G., J.W.M., J.C.L., R.S., A.R. and E.S.; visualization, M.G.; supervision, E.S.; project administration, E.S. All authors have read and agreed to the published version of the manuscript.

Funding

This study received no external funding and was sponsored by Numares AG. The APC was funded by Numares AG.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Mayo Clinic (IRB #21-007723 dated 11 October 2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available within the article.

Acknowledgments

The authors are grateful to Frank Stämmler and John Venz (Numares AG, Regensburg, Germany) for their assistance in case number estimation and statistical analysis. The authors thank Anne Rascle of AR Medical Writing (Regensburg, Germany) for providing medical writing support, which was funded by numares (Regensburg, Germany), in accordance with Good Publication Practice (GPP3) guidelines (http://www.ismpp.org/gpp3 (accessed on 20 April 2023)).

Conflicts of Interest

A.S.S., M.G., A.R., and E.S. are employees of Numares AG. Numares AG is a diagnostics company and focuses on the discovery, development, and commercialization of diagnostic tests by metabolite constellations. This study was funded by numares AG. The funder had a role in the design of the study, in the analysis and interpretation of data, in the decision to publish the results, and in the writing of the manuscript.

References

  1. Guidelines–KDIGO. Available online: https://kdigo.org/guidelines/ (accessed on 21 February 2023).
  2. Ifekwunigwe, J.O.; Wagner, J.K.; Yu, J.-H.; Harrell, T.M.; Bamshad, M.J.; Royal, C.D. A Qualitative Analysis of How Anthropologists Interpret the Race Construct. Am. Anthropol. 2017, 119, 422–434. [Google Scholar] [CrossRef]
  3. Inker, L.A.; Eneanya, N.D.; Coresh, J.; Tighiouart, H.; Wang, D.; Sang, Y.; Crews, D.C.; Doria, A.; Estrella, M.M.; Froissart, M.; et al. New Creatinine- and Cystatin C-Based Equations to Estimate GFR without Race. N. Engl. J. Med. 2021, 385, 1737–1749. [Google Scholar] [CrossRef]
  4. Diao, J.A.; Wu, G.J.; Wang, J.K.; Kohane, I.S.; Taylor, H.A.; Tighiouart, H.; Levey, A.S.; Inker, L.A.; Powe, N.R.; Manrai, A.K. National Projections for Clinical Implications of Race-Free Creatinine-Based GFR Estimating Equations. J. Am. Soc. Nephrol. 2023, 34, 309–321. [Google Scholar] [CrossRef]
  5. Abate, M.; Jandovitz, N.; Hirsch, J.S.; Breslin, N.; Lau, L.; Fahmy, A.E.; Jhaveri, K.D.; Richardson, S.; Alsalmay, Y.; Baez, A.; et al. The Effect of Race Coefficients on Preemptive Listing for Kidney Transplantation. Clin. Kidney J. 2022, 15, 942–950. [Google Scholar] [CrossRef]
  6. Hoenig, M.P.; Mann, A.; Pavlakis, M. Removal of the Black Race Coefficient from the Estimated Glomerular Filtration Equation Improves Transplant Eligibility for Black Patients at a Single Center. Clin. Transplant. 2022, 36, e14467. [Google Scholar] [CrossRef] [PubMed]
  7. Zelnick, L.R.; Leca, N.; Young, B.; Bansal, N. Association of the Estimated Glomerular Filtration Rate With vs Without a Coefficient for Race with Time to Eligibility for Kidney Transplant. JAMA Netw. Open 2021, 4, e2034004. [Google Scholar] [CrossRef] [PubMed]
  8. Meeusen, J.W.; Kasozi, R.N.; Larson, T.S.; Lieske, J.C. Clinical Impact of the Refit CKD-EPI 2021 Creatinine-Based EGFR Equation. Clin. Chem. 2022, 68, 534–539. [Google Scholar] [CrossRef] [PubMed]
  9. Miller, W.G.; Kaufman, H.W.; Levey, A.S.; Straseski, J.A.; Wilhelms, K.W.; Yu, H.-Y.E.; Klutts, J.S.; Hilborne, L.H.; Horowitz, G.L.; Lieske, J.; et al. National Kidney Foundation Laboratory Engagement Working Group Recommendations for Implementing the CKD-EPI 2021 Race-Free Equations for Estimated Glomerular Filtration Rate: Practical Guidance for Clinical Laboratories. Clin. Chem. 2022, 68, 511–520. [Google Scholar] [CrossRef]
  10. Delgado, C.; Baweja, M.; Crews, D.C.; Eneanya, N.D.; Gadegbeku, C.A.; Inker, L.A.; Mendu, M.L.; Miller, W.G.; Moxey-Mims, M.M.; Roberts, G.V.; et al. A Unifying Approach for GFR Estimation: Recommendations of the NKF-ASN Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease. Am. J. Kidney Dis. 2022, 79, 268–288.e1. [Google Scholar] [CrossRef]
  11. Stämmler, F.; Grassi, M.; Meeusen, J.W.; Lieske, J.C.; Dasari, S.; Dubourg, L.; Lemoine, S.; Ehrich, J.; Schiffer, E. Estimating Glomerular Filtration Rate from Serum Myo-Inositol, Valine, Creatinine and Cystatin C. Diagnostics 2021, 11, 2291. [Google Scholar] [CrossRef]
  12. Fuhrmann, M.; Schwaeble Santamaria, A.; Scott, R.; Meeusen, J.W.; Fernandes, M.; Venz, J.; Rothe, V.; Stämmler, F.; Ehrich, J.; Schiffer, E. Analytical Validation of GFRNMR: A Blood-Based Multiple Biomarker Assay for Accurate Estimation of Glomerular Filtration Rate. Diagnostics 2022, 12, 1120. [Google Scholar] [CrossRef]
  13. Meeusen, J.W.; Stämmler, F.; Dasari, S.; Schiffer, E.; Lieske, J.C. Serum Myo-Inositol and Valine Improve Metabolomic-Based Estimated Glomerular Filtration Rate among Kidney Transplant Recipients. Front. Med. 2022, 9, 988989. [Google Scholar] [CrossRef]
  14. Seegmiller, J.C.; Burns, B.E.; Fauq, A.H.; Mukhtar, N.; Lieske, J.C.; Larson, T.S. Iothalamate Quantification by Tandem Mass Spectrometry to Measure Glomerular Filtration Rate. Clin. Chem. 2010, 56, 568–574. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Piéroni, L.; Delanaye, P.; Boutten, A.; Bargnoux, A.-S.; Rozet, E.; Delatour, V.; Carlier, M.-C.; Hanser, A.-M.; Cavalier, E.; Froissart, M.; et al. A Multicentric Evaluation of IDMS-Traceable Creatinine Enzymatic Assays. Clin. Chim. Acta 2011, 412, 2070–2075. [Google Scholar] [CrossRef] [PubMed]
  16. Grubb, A.; Blirup-Jensen, S.; Lindström, V.; Schmidt, C.; Althaus, H.; Zegers, I.; IFCC Working Group on Standardisation of Cystatin C (WG-SCC). First Certified Reference Material for Cystatin C in Human Serum ERM-DA471/IFCC. Clin. Chem. Lab. Med. 2010, 48, 1619–1621. [Google Scholar] [CrossRef]
  17. Ehrich, J.; Dubourg, L.; Hansson, S.; Pape, L.; Steinle, T.; Fruth, J.; Höckner, S.; Schiffer, E. Serum Myo-Inositol, Dimethyl Sulfone, and Valine in Combination with Creatinine Allow Accurate Assessment of Renal Insufficiency-A Proof of Concept. Diagnostics 2021, 11, 234. [Google Scholar] [CrossRef]
  18. Machin, D.; Campbell, M.J.; Tan, S.-B.; Tan, S.-H. Sample Size Tables for Clinical Studies, 3rd ed.; Wiley: Hoboken, NJ, USA, 2009; ISBN 978-1-4443-0072-7. [Google Scholar]
  19. R Core Team. R: The R Project for Statistical Computing. R Package Version 4.0.2. 2020. Available online: https://www.r-project.org/ (accessed on 14 July 2022).
  20. Chambers, J.M. Data for Models. In Statistical Models in S; Chambers, J.M., Hastie, T.J., Eds.; Wadsworth and Brooks/Cole: Pacific Grove, CA, USA, 1992; ISBN 0-534-16765-9. [Google Scholar]
  21. Dowle, M.; Srinivasan, A.; Gorecki, J.; Chirico, M.; Stetsenko, P.; Short, T.; Lianoglou, S.; Antonyan, E.; Bonsch, M.; Parsonage, H.; et al. Data.Table: Extension of “Data.Frame”. R Package Version 1.13.2. 2020. Available online: https://CRAN.R-project.org/package=data.table (accessed on 14 July 2022).
  22. Biecek, P.; Kosiński, M. Archivist: An R Package for Managing, Recording and Restoring Data Analysis Results. J. Stat. Softw. 2017, 82, 1–28. [Google Scholar] [CrossRef] [Green Version]
  23. Davison, A.C.; Hinkley, D.V. Bootstrap Methods and Their Application; Cambridge Series in Statistical and Probabilistic Mathematics; Cambridge University Press: Cambridge, MA, USA, 1997; ISBN 0-521-57391-2. Available online: http://statwww.epfl.ch/davison/BMA/ (accessed on 26 July 2021).
  24. Canty, A.; Ripley, B.D. Boot: Bootstrap R (S-Plus) Functions. R Package Version 1.3-25. 2020. Available online: http://statwww.epfl.ch/davison/BMA/library.html (accessed on 14 July 2022).
  25. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis, 2nd ed.; Use R!; Springer International Publishing: Cham, Switzerland, 2016; ISBN 978-3-319-24275-0. [Google Scholar]
  26. Wilcoxon, F. Individual Comparisons by Ranking Methods. Biom. Bull. 1945, 1, 80–83. [Google Scholar] [CrossRef]
  27. Siegel, S. Nonparametric Statistics for the Behavioral Sciences; McGraw-Hill: New York, NY, USA, 1956; ISBN 978-0-07-085689-9. [Google Scholar]
  28. Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B. 1995, 57, 289–300. [Google Scholar] [CrossRef]
  29. Yekutieli, D.; Benjamini, Y. Resampling-Based False Discovery Rate Controlling Multiple Test Procedures for Correlated Test Statistics. J. Stat. Plan. Inference 1999, 82, 171–196. [Google Scholar] [CrossRef]
  30. McNemar, Q. Note on the Sampling Error of the Difference between Correlated Proportions or Percentages. Psychometrika 1947, 12, 153–157. [Google Scholar] [CrossRef]
  31. Pencina, M.J.; D’Agostino, R.B.; D’Agostino, R.B.; Vasan, R.S. Evaluating the Added Predictive Ability of a New Marker: From Area under the ROC Curve to Reclassification and Beyond. Stat. Med. 2008, 27, 157–172; discussion 207–212. [Google Scholar] [CrossRef]
  32. Dreiseitl, S.; Ohno-Machado, L.; Binder, M. Comparing Three-Class Diagnostic Tests by Three-Way ROC Analysis. Med. Decis. Mak. 2000, 20, 323–331. [Google Scholar] [CrossRef]
  33. Nakas, C.T.; Yiannoutsos, C.T. Ordered Multiple-Class ROC Analysis with Continuous Measurements. Stat. Med. 2004, 23, 3437–3449. [Google Scholar] [CrossRef] [PubMed]
  34. Shafi, T.; Zhu, X.; Lirette, S.T.; Rule, A.D.; Mosley, T.; Butler, K.R.; Hall, M.E.; Vaitla, P.; Wynn, J.J.; Tio, M.C.; et al. Quantifying Individual-Level Inaccuracy in Glomerular Filtration Rate Estimation: A Cross-Sectional Study. Ann. Intern. Med. 2022, 175, 1073–1082. [Google Scholar] [CrossRef] [PubMed]
  35. Austin, P.C.; Tu, J.V.; Daly, P.A.; Alter, D.A. The Use of Quantile Regression in Health Care Research: A Case Study Examining Gender Differences in the Timeliness of Thrombolytic Therapy. Stat. Med. 2005, 24, 791–816. [Google Scholar] [CrossRef]
  36. Levin, A.S.; Bilous, R.W.; Coresh, J. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease-Chapter 1: Definition and Classification of CKD. Kidney Int. Suppl. 2013, 3, 19–62. [Google Scholar] [CrossRef] [Green Version]
  37. Sehgal, A.R. Race and the False Precision of Glomerular Filtration Rate Estimates. Ann. Intern. Med. 2020, 173, 1008–1009. [Google Scholar] [CrossRef]
  38. Levey, A.S.; Tighiouart, H.; Inker, L.A. Improving Glomerular Filtration Rate Estimation-Across the Age and Diversity Spectrum. Ann. Intern. Med. 2021, 174, 265–267. [Google Scholar] [CrossRef] [PubMed]
  39. Steubl, D.; Inker, L.A. How Best to Estimate Glomerular Filtration Rate? Novel Filtration Markers and Their Application. Curr. Opin. Nephrol. Hypertens 2018, 27, 398–405. [Google Scholar] [CrossRef]
  40. Schnaper, H.W.; Furth, S.L.; Yao, L.P. Defining New Surrogate Markers for CKD Progression. Pediatr. Nephrol. 2015, 30, 193–198. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Wilkinson, R. Beta-Blockers and Renal Function. Drugs 1982, 23, 195–206. [Google Scholar] [CrossRef] [PubMed]
  42. Hart, P.D.; Bakris, G.L. Should Beta-Blockers Be Used to Control Hypertension in People with Chronic Kidney Disease? Semin. Nephrol. 2007, 27, 555–564. [Google Scholar] [CrossRef] [PubMed]
  43. Johns, E.J.; Kopp, U.C.; DiBona, G.F. Neural Control of Renal Function. Compr. Physiol. 2011, 1, 731–767. [Google Scholar] [CrossRef]
  44. Kwong, Y.-T.D.; Stevens, L.A.; Selvin, E.; Zhang, Y.L.; Greene, T.; Van Lente, F.; Levey, A.S.; Coresh, J. Imprecision of Urinary Iothalamate Clearance as a Gold-Standard Measure of GFR Decreases the Diagnostic Accuracy of Kidney Function Estimating Equations. Am. J. Kidney Dis. 2010, 56, 39–49. [Google Scholar] [CrossRef] [Green Version]
  45. Soveri, I.; Berg, U.B.; Björk, J.; Elinder, C.-G.; Grubb, A.; Mejare, I.; Sterner, G.; Bäck, S.-E. SBU GFR Review Group Measuring GFR: A Systematic Review. Am. J. Kidney Dis. 2014, 64, 411–424. [Google Scholar] [CrossRef]
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Figure 1. Study flow diagram. Abbreviations: CKD-EPI, Chronic Kidney Disease—Epidemiology Collaboration; CKD-EPI2021Cr, 2021 creatinine eGFR equation without race [3]; CKD-EPI2021CrCys, 2021 creatinine-cystatin C eGFR equation without race [3]; eGFR, estimated GFR; GFR, glomerular filtration rate; GFRNMR, NMR-based eGFR equation [11,12]; mGFR, measured GFR.
Figure 1. Study flow diagram. Abbreviations: CKD-EPI, Chronic Kidney Disease—Epidemiology Collaboration; CKD-EPI2021Cr, 2021 creatinine eGFR equation without race [3]; CKD-EPI2021CrCys, 2021 creatinine-cystatin C eGFR equation without race [3]; eGFR, estimated GFR; GFR, glomerular filtration rate; GFRNMR, NMR-based eGFR equation [11,12]; mGFR, measured GFR.
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Figure 2. Bias distribution for GFRNMR, CKD−EPI2021Cr, and CKD−EPI2021CrCys. The dashed lines indicate the median bias. GFRNMR bias distribution appeared unimodal, with one main peak centered around its median value of 2.0 mL/min/1.73 m2. By contrast, CKD-EPI2021Cr and CKD−EPI2021CrCys bias distribution appeared bimodal, with a pool of patients with negative bias and another with positive bias. Median bias of all three equations was positive and close to zero (Table 2). Abbreviations: CKD−EPI2021Cr, 2021 creatinine eGFR equation without race [3]; CKD−EPI2021CrCys, 2021 creatinine−cystatin C eGFR equation without race [3]; eGFR, estimated GFR; GFR, glomerular filtration rate; GFRNMR, NMR−based eGFR equation [11,12].
Figure 2. Bias distribution for GFRNMR, CKD−EPI2021Cr, and CKD−EPI2021CrCys. The dashed lines indicate the median bias. GFRNMR bias distribution appeared unimodal, with one main peak centered around its median value of 2.0 mL/min/1.73 m2. By contrast, CKD-EPI2021Cr and CKD−EPI2021CrCys bias distribution appeared bimodal, with a pool of patients with negative bias and another with positive bias. Median bias of all three equations was positive and close to zero (Table 2). Abbreviations: CKD−EPI2021Cr, 2021 creatinine eGFR equation without race [3]; CKD−EPI2021CrCys, 2021 creatinine−cystatin C eGFR equation without race [3]; eGFR, estimated GFR; GFR, glomerular filtration rate; GFRNMR, NMR−based eGFR equation [11,12].
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Figure 3. Regression Error Characteristic Curve for all error tolerance Px values for GFRNMR, CKD-EPI2021Cr, and CKD-EPI2021CrCys. Px denotes the percentage of eGFR values within x% of mGFR. The red dashed lines indicate error tolerance cutoffs of (from left to right) P15, P20, and P30, respectively. The y-axis shows the corresponding percentage of samples within the given error tolerance Px value on the x-axis. Abbreviations: CKD-EPI2021Cr, 2021 creatinine eGFR equation without race [3]; CKD-EPI2021CrCys, 2021 creatinine-cystatin C eGFR equation without race [3]; eGFR, estimated GFR; GFR, glomerular filtration rate; GFRNMR, NMR-based eGFR equation [11,12].
Figure 3. Regression Error Characteristic Curve for all error tolerance Px values for GFRNMR, CKD-EPI2021Cr, and CKD-EPI2021CrCys. Px denotes the percentage of eGFR values within x% of mGFR. The red dashed lines indicate error tolerance cutoffs of (from left to right) P15, P20, and P30, respectively. The y-axis shows the corresponding percentage of samples within the given error tolerance Px value on the x-axis. Abbreviations: CKD-EPI2021Cr, 2021 creatinine eGFR equation without race [3]; CKD-EPI2021CrCys, 2021 creatinine-cystatin C eGFR equation without race [3]; eGFR, estimated GFR; GFR, glomerular filtration rate; GFRNMR, NMR-based eGFR equation [11,12].
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Figure 4. Distribution of mGFR at selected eGFR thresholds (45, 60 and 90 mL/min/1.73 m2). The whiskers represent the 95% prediction intervals (PI) between mGFR and eGFR (2.5th to 97.5th percentiles; p2.5, p97.5). The symbols (triangles, circles, and squares) indicate the percentiles of mGFR at a given eGFR (p10 to p90, p25 to p75, and p50 or median, respectively). At a given eGFR, 50% of mGFR values range from the 25th to 75th percentiles, 80% from the 10th to 90th percentiles, and 95% from the 2.5th to 97.5th percentiles (defined as 95% PI). Abbreviations: CKD-EPI2021Cr, 2021 creatinine eGFR equation without race [3]; CKD-EPI2021CrCys, 2021 creatinine-cystatin C eGFR equation without race [3]; eGFR, estimated GFR; GFR, glomerular filtration rate; GFRNMR, NMR-based eGFR equation [11,12]; mGFR, measured GFR; px, percentile.
Figure 4. Distribution of mGFR at selected eGFR thresholds (45, 60 and 90 mL/min/1.73 m2). The whiskers represent the 95% prediction intervals (PI) between mGFR and eGFR (2.5th to 97.5th percentiles; p2.5, p97.5). The symbols (triangles, circles, and squares) indicate the percentiles of mGFR at a given eGFR (p10 to p90, p25 to p75, and p50 or median, respectively). At a given eGFR, 50% of mGFR values range from the 25th to 75th percentiles, 80% from the 10th to 90th percentiles, and 95% from the 2.5th to 97.5th percentiles (defined as 95% PI). Abbreviations: CKD-EPI2021Cr, 2021 creatinine eGFR equation without race [3]; CKD-EPI2021CrCys, 2021 creatinine-cystatin C eGFR equation without race [3]; eGFR, estimated GFR; GFR, glomerular filtration rate; GFRNMR, NMR-based eGFR equation [11,12]; mGFR, measured GFR; px, percentile.
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Table
Table 1. Patient characteristics.
Table 1. Patient characteristics.
Study Population, N (%)115 (100.0%)
Sex, N (%)
Female50 (43.5%)
Male65 (56.5%)
Age group in years, N (%)
<5037 (32.2%)
50–6455 (47.8%)
≥6523 (20.0%)
Age in years, Mean ± SD (range)54.9 ± 10.6 (32.0–78.0)
Height in cm, Mean ± SD (range) 1171.7 ± 9.9 (148.0–195.0)
Weight in kg, Mean ± SD (range) 187.9 ± 20.8 (51.0–161.0)
Ethnicity, N (%)
White112 (97.4%)
Asian1 (0.9%)
Black or African American1 (0.9%)
Not disclosed1 (0.9%)
CKD Stage, N (%) 2
G113 (11.3%)
G248 (41.7%)
G3a36 (31.3%)
G3b15 (13.0%)
G43 (2.6%)
G50 (0.0%)
Underlying disease or condition, N (%)
Solid-organ transplantation (n missing = 13) 1102 (88.7%)
Kidney transplantation 369 (60.0%)
Chronic kidney disease (CKD) (n missing = 3) 176 (66.1%)
Liver disease33 (28.7%)
Nephrectomy (n missing = 1) 113 (11.3%)
Concomitant disease, N (%)
Hypertension80 (69.6%)
Dyslipidemia 75 (65.2%)
Hyperlipidemia67 (60.0%)
Diabetes mellitus (n missing = 1) 126 (22.6%)
Cardiovascular disease (n missing = 2) 122 (19.1%)
Medication, N (%)
Immunosuppressive agents (n missing = 1) 1101 (87.8%)
Corticosteroids (n missing = 2) 155 (47.8%)
Beta-blocker (n missing = 2) 141 (35.7%)
ACE inhibitor (n missing = 2) 121 (18.3%)
Antidiabetics (n missing = 2) 122 (19.1%)
Measured GFR (mGFR), Mean (SD) 464.2 (20.8)
Estimated GFR (eGFR), Mean (SD) 4
CKD-EPI2021Cr63.6 (20.2)
CKD-EPI2021CrCys63.8 (21.5)
GFRNMR64.1 (18.7)
1 Percentages refer to documented characteristics in the study population, not taking into account patients with missing characteristics (the number of patients with missing characteristics is indicated in brackets); 2 CKD staging based on mGFR and according to the Kidney Disease: Improving Global Outcomes (KDIGO) guideline [36]; 3 Includes patients with single kidney transplantation, combined kidney-liver or kidney-pancreas transplantation; 4 Expressed as mL/min/1.73 m2 of body-surface area. Abbreviations: ACE, angiotensin-converting enzyme; CKD, chronic kidney disease; CKD-EPI, Chronic Kidney Disease—Epidemiology Collaboration; CKD-EPI2021Cr, 2021 creatinine eGFR equation without race [3]; CKD-EPI2021CrCys, 2021 creatinine-cystatin C eGFR equation without race [3]; eGFR, estimated GFR; GFR, glomerular filtration rate; GFRNMR, NMR-based eGFR equation [11,12]; mGFR, measured GFR; N, number of samples; SD, standard deviation.
Table
Table 2. Performance of eGFR equations.
Table 2. Performance of eGFR equations.
VariablePerformance Value
Median signed bias to mGFR (95% CI) 1
CKD-EPI2021Cr1.0 (0.3; 6.8)
CKD-EPI2021CrCys0.4 (−2.1; 3.3)
GFRNMR2.0 (1.0; 4.0)
Precision—IQR of the difference to mGFR (95% CI) 1
CKD-EPI2021Cr16.8 (12.9; 19.9) *
CKD-EPI2021CrCys17.9 (14.0; 21.9) *
GFRNMR10.5 (5.5; 13.0)
Accuracy—P15 (95% CI) [%] 2
CKD-EPI2021Cr52.2 (43.5; 60.9) *
CKD-EPI2021CrCys48.7 (39.2; 57.4) **
GFRNMR66.1 (57.4; 74.8)
Accuracy—P20 (95% CI) [%] 2
CKD-EPI2021Cr73.0 (64.3; 80.9)
CKD-EPI2021CrCys60.0 (51.3; 68.7) ***
GFRNMR80.0 (73.0; 87.0)
Accuracy—P30 (95% CI) [%] 2
CKD-EPI2021Cr87.0 (80.9; 93.0) *
CKD-EPI2021CrCys86.1 (80.0; 92.2) **
GFRNMR95.7 (92.2; 100.0)
1 Expressed as mL/min/1.73 m2; 2 P15, P20, and P30 denote the percentage of eGFR values lying within the tolerance range of 15%, 20%, and 30% of measured GFR (mGFR), respectively. Bold numbers highlight the best performance results in each analysis. Symbols *, ** and *** indicate the level of significance for p-values < 0.05, < 0.01, and < 0.001, respectively, in the pairwise tests against GFRNMR for each KPI. Abbreviations: CI, confidence interval; CKD-EPI, Chronic Kidney Disease—Epidemiology Collaboration; CKD-EPI2021Cr, 2021 creatinine eGFR equation without race [3]; CKD-EPI2021CrCys, 2021 creatinine-cystatin C eGFR equation without race [3]; eGFR, estimated GFR; GFR, glomerular filtration rate; GFRNMR, NMR-based eGFR equation [11,12]; IQR, interquartile range. mGFR, measured GFR.
Table
Table 3. Comparison of median signed bias (95% CI) for GFRNMR, CKD-EPI2021Cr, and CKD-EPI2021CrCys according to underlying disease, comorbidities, and medication.
Table 3. Comparison of median signed bias (95% CI) for GFRNMR, CKD-EPI2021Cr, and CKD-EPI2021CrCys according to underlying disease, comorbidities, and medication.
Variable eGFR Equation
GFRNMRCKD—EPI2021CrCKD—EPI2021CrCys
CKDYes (n = 76)0.5 (−2.0; 2.0)1.3 (−1.2; 5.8)2.1 (−0.8; 6.4)
No (n = 39)2.0 (−1.0; 7.0)−6.9 (−15.6; −2.6)−2.4 (−7.9; 5.2)
p-value 10.960.120.29
Kidney transplantationYes (n = 69)1.0 (−1.0; 3.0)1.5 (−1.1; 6.1)2.3 (−1.3; 6.8)
No (n = 46)2.0 (−1.0; 5.0)−6.4 (−14.7; −2.4)−2.2 (−6.6; 5.5)
p-value 10.940.120.18
HypertensionYes (n = 80)0.5 (−2.0; 2.0)1.1 (−0.9; 5.4)0.6 (−2.7; 4.4)
No (n = 35)2.0 (−2.0; 4.0)−4.7 (−13.5; −2.3)−0.9 (−8.4; 2.8)
p-value 10.330.780.79
HyperlipidemiaYes (n = 67)0.0 (−3.0; 1.0)−1.0 (−4.7; 4.0)−0.8 (−5.2; 2.0)
No (n = 48)2.0 (−1.0; 4.5)1.7 (−0.4; 9.5)0.9 (−5.1; 5.7)
p-value 10.410.640.50
Immunosuppressive agentsYes (n = 101)2.0 (1.0; 4.0)1.2 (−1.5; 5.5)1.9 (−1.0; 6.0)
No (n = 14)0.5 (−5.0; 13.0)−11.8 (−21.1; −9.4)−10.7 (−17.8; −5.6)
p-value 10.210.0020.007
CorticosteroidsYes (n = 55)1.0 (−1.0; 3.0)1.6 (−3.1; 6.4)2.3 (−0.8; 6.8)
No (n = 60)2.0 (0.0; 5.0)−3.7 (−9.2; −0.3)−2.0 (−6.9; 3.8)
p-value 10.880.110.13
Beta blockersYes (n = 41)0.0 (−3.0; 4.0)−0.2 (−3.6; 6.6)−2.7 (−10.9; 2.1)
No (n = 74)2.0 (0.0; 4.0)1.1 (−1.6; 7.2)1.2 (−1.7; 4.5)
p-value 10.050.430.22
1 The Wilcoxon rank sum test was used to compare bias distributions in patients with (‘Yes’) vs. without (‘No’) the indicated disease, comorbidity, or medication. Bold p-values indicate statistical significance.
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Schwäble Santamaria, A.; Grassi, M.; Meeusen, J.W.; Lieske, J.C.; Scott, R.; Robertson, A.; Schiffer, E. Performance of Nuclear Magnetic Resonance-Based Estimated Glomerular Filtration Rate in a Real-World Setting. Bioengineering 2023, 10, 717. https://doi.org/10.3390/bioengineering10060717

AMA Style

Schwäble Santamaria A, Grassi M, Meeusen JW, Lieske JC, Scott R, Robertson A, Schiffer E. Performance of Nuclear Magnetic Resonance-Based Estimated Glomerular Filtration Rate in a Real-World Setting. Bioengineering. 2023; 10(6):717. https://doi.org/10.3390/bioengineering10060717

Chicago/Turabian Style

Schwäble Santamaria, Amauri, Marcello Grassi, Jeffrey W. Meeusen, John C. Lieske, Renee Scott, Andrew Robertson, and Eric Schiffer. 2023. "Performance of Nuclear Magnetic Resonance-Based Estimated Glomerular Filtration Rate in a Real-World Setting" Bioengineering 10, no. 6: 717. https://doi.org/10.3390/bioengineering10060717

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