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Analytical Validation of GFR_NMR: A Blood-Based Multiple Biomarker Assay for Accurate Estimation of Glomerular Filtration Rate.

ABSTRACT: Accurate and precise monitoring of kidney function is critical for a timely and reliable diagnosis of chronic kidney disease (CKD). The determination of kidney function usually involves the estimation of the glomerular filtration rate (eGFR). We recently reported the clinical performance of a new eGFR equation (GFR_NMR) based on the nuclear magnetic resonance (NMR) measurement of serum myo-inositol, valine, and creatinine, in addition to the immunoturbidometric quantification of serum cystatin C, age and sex. We now describe the analytical performance evaluation of GFR_NMR according to the Clinical and Laboratory Standards Institute guidelines. Within-laboratory coefficients of variation (CV%) of the GFR_NMR equation did not exceed 4.3%, with a maximum CV% for repeatability of 3.7%. Between-site reproducibility (three sites) demonstrated a maximum CV% of 5.9%. GFR_NMR stability was demonstrated for sera stored for up to 8 days at 2-10°C and for NMR samples stored for up to 10 days in the NMR device at 6 ± 2°C. Substance interference was limited to 4/40 (10.0%) of the investigated substances, resulting in an underestimated GFR_NMR (for glucose and metformin) or a loss of results (for naproxen and ribavirin) for concentrations twice as high as usual clinical doses. The analytical performances of GFR_NMR, combined with its previously reported clinical performance, support the potential integration of this NMR method into clinical practice.

SUBMITTER: Fuhrmann M

PROVIDER: S-EPMC9139323 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Analytical Validation of GFR<sub>NMR</sub>: A Blood-Based Multiple Biomarker Assay for Accurate Estimation of Glomerular Filtration Rate.

Fuhrmann Markus M Schwaeble Santamaria Amauri A Scott Renee R Meeusen Jeffrey W JW Fernandes Marianna M Venz John J Rothe Victoria V Stämmler Frank F Ehrich Jochen J Schiffer Eric E

Diagnostics (Basel, Switzerland) 20220430 5

Accurate and precise monitoring of kidney function is critical for a timely and reliable diagnosis of chronic kidney disease (CKD). The determination of kidney function usually involves the estimation of the glomerular filtration rate (eGFR). We recently reported the clinical performance of a new eGFR equation (GFR<sub>NMR</sub>) based on the nuclear magnetic resonance (NMR) measurement of serum myo-inositol, valine, and creatinine, in addition to the immunoturbidometric quantification of serum ...[more]

PMID: 35626276

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Project description:BackgroundAccurate evaluation of glomerular filtration rates (GFRs) is of critical importance in clinical practice. A previous study showed that models based on artificial neural networks (ANNs) could achieve a better performance than traditional equations. However, large-sample cross-sectional surveys have not resolved questions about ANN performance.MethodsA total of 1,180 patients that had chronic kidney disease (CKD) were enrolled in the development data set, the internal validation data set and the external validation data set. Additional 222 patients that were admitted to two independent institutions were externally validated. Several ANNs were constructed and finally a Back Propagation network optimized by a genetic algorithm (GABP network) was chosen as a superior model, which included six input variables; i.e., serum creatinine, serum urea nitrogen, age, height, weight and gender, and estimated GFR as the one output variable. Performance was then compared with the Cockcroft-Gault equation, the MDRD equations and the CKD-EPI equation.ResultsIn the external validation data set, Bland-Altman analysis demonstrated that the precision of the six-variable GABP network was the highest among all of the estimation models; i.e., 46.7 ml/min/1.73 m(2) vs. a range from 71.3 to 101.7 ml/min/1.73 m(2), allowing improvement in accuracy (15% accuracy, 49.0%; 30% accuracy, 75.1%; 50% accuracy, 90.5% [P<0.001 for all]) and CKD stage classification (misclassification rate of CKD stage, 32.4% vs. a range from 47.3% to 53.3% [P<0.001 for all]). Furthermore, in the additional external validation data set, precision and accuracy were improved by the six-variable GABP network.ConclusionsA new ANN model (the six-variable GABP network) for CKD patients was developed that could provide a simple, more accurate and reliable means for the estimation of GFR and stage of CKD than traditional equations. Further validations are needed to assess the ability of the ANN model in diverse populations.

Project description:Assessing glomerular filtration rate (GFR) is critical for diagnosis, staging, and management of kidney disease. However, accuracy of estimated GFR (eGFR) is limited by large errors (>30% error present in >10-50% of patients), adversely impacting patient care. Errors often result from variation across populations of non-GFR determinants affecting the filtration markers used to estimate GFR. We hypothesized that combining multiple filtration markers with non-overlapping non-GFR determinants into a panel GFR could improve eGFR accuracy, extending current recognition that adding cystatin C to serum creatinine improves accuracy. Non-GFR determinants of markers can affect the accuracy of eGFR in two ways: first, increased variability in the non-GFR determinants of some filtration markers among application populations compared to the development population may result in outlying values for those markers. Second, systematic differences in the non-GFR determinants of some markers between application and development populations can lead to biased estimates in the application populations. Here, we propose and evaluate methods for estimating GFR based on multiple markers in applications with potentially higher rates of outlying predictors than in development data. We apply transfer learning to address systematic differences between application and development populations. We evaluated a panel of 8 markers (5 metabolites and 3 low molecular weight proteins) in 3,554 participants from 9 studies. Results show that contamination in two strongly predictive markers can increase imprecision by more than two-fold, but outlier identification with robust estimation can restore precision nearly fully to uncontaminated data. Furthermore, transfer learning can yield similar results with even modest training set sample size. Combining both approaches addresses both sources of error in GFR estimates. Once the laboratory challenge of developing a validated targeted assay for additional metabolites is overcome, these methods can inform the use of a panel eGFR across diverse clinical settings, ensuring accuracy despite differing non-GFR determinants.

Project description:BackgroundCarboplatin remains integral for treatment of gynaecological malignancies and dosing is based on glomerular filtration rate (GFR). Measurement via radiotracer decay [nuclear medicine GFR (nmGFR)] is ideal. However, this may be unavailable. Therefore GFR is often estimated using formulae that have not been validated in patients with cancer and/or specifically for gynaecological malignancies, leading to debate over optimal estimation. Suboptimal GFR estimation may affect efficacy or toxicity.MethodsWe surveyed several UK National Health Service Trusts to assess carboplatin dosing practise. We then explored single-centre accuracy, bias and precision of various formulae for GFR estimation, relative to nmGFR, before validating our findings in an external cohort.ResultsAcross 18 Trusts, there was considerable heterogeneity in GFR estimation, including the formulae used [Cockcroft-Gault (CG) versus Wright], weight adjustment and area under the curve (AUC; 5 versus 6). We analysed 274 and 192 patients in two centres. Overall, CamGFR v2 (a novel formula for GFR estimation developed at Cambridge University Hospitals NHS Foundation Trust) excelled, showing the highest accuracy and precision. This translated into accuracy of hypothetical carboplatin dosing; nmGFR-derived carboplatin dose fell within 20% of the Cam GFR v2-derived dose in 86.5% and 87% of patients across the cohorts. Among the CG formula and its derivatives, using adjusted body weight in those with body mass index ≥25 kg/m2 [CG-adjusted body weight (CG-AdBW)] was optimal. The Wright and unadjusted CG estimators performed most poorly.ConclusionsWhen compared with nmGFR assessment, accuracy, bias and precision varied widely between GFR estimators, with the newly developed Cam GFR v2 and CG-AdBW performing best. In general, weight (or body surface area)-adjusted formulae excelled, while the unadjusted CG and Wright formulae or the use of AUC6 (versus nmGFR AUC5) produced risk of significant overdose. Thus, individual centres should validate their GFR estimation methods. In the absence of validation, CG-AdBW or CamGFR v2 is likely to perform well while unadjusted CG/Wright formulae or AUC6 dosing should be avoided.

Project description:IntroductionEstimation of kidney function in critically ill patients with acute kidney injury (AKI), is important for appropriate dosing of drugs and adjustment of therapeutic strategies, but challenging due to fluctuations in kidney function, creatinine metabolism and fluid balance. Data on the agreement between estimating and gold standard methods to assess glomerular filtration rate (GFR) in early AKI are lacking. We evaluated the agreement of urinary creatinine clearance (CrCl) and three commonly used estimating equations, the Cockcroft Gault (CG), the Modification of Diet in Renal Disease (MDRD) and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations, in comparison to GFR measured by the infusion clearance of chromium-ethylenediaminetetraacetic acid (51Cr-EDTA), in critically ill patients with early AKI after complicated cardiac surgery.MethodsThirty patients with early AKI were studied in the intensive care unit, 2 to 12 days after complicated cardiac surgery. The infusion clearance for 51Cr-EDTA obtained as a measure of GFR (GFR51Cr-EDTA) was calculated from the formula: GFR (mL/min/1.73m2)=(51Cr-EDTA infusion rate×1.73)/(arterial 51Cr-EDTA×body surface area) and compared with the urinary CrCl and the estimated GFR (eGFR) from the three estimating equations. Urine was collected in two 30-minute periods to measure urine flow and urine creatinine. Urinary CrCl was calculated from the formula: CrCl (mL/min/1.73m2)=(urine volume×urine creatinine×1.73)/(serum creatinine×30 min×body surface area).ResultsThe within-group error was lower for GFR51Cr-EDTA than the urinary CrCl method, 7.2% versus 55.0%. The between-method bias was 2.6, 11.6, 11.1 and 7.39 ml/min for eGFRCrCl, eGFRMDRD, eGFRCKD-EPI and eGFRCG, respectively, when compared to GFR51Cr-EDTA. The error was 103%, 68.7%, 67.7% and 68.0% for eGFRCrCl, eGFRMDRD, eGFRCKD-EPI and eGFRCG, respectively, when compared to GFR51Cr-EDTA.ConclusionsThe study demonstrated poor precision of the commonly utilized urinary CrCl method for assessment of GFR in critically ill patients with early AKI, suggesting that this should not be used as a reference method when validating new methods for assessing kidney function in this patient population. The commonly used estimating equations perform poorly when estimating GFR, with high biases and unacceptably high errors.

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Analytical Validation of GFR_NMR: A Blood-Based Multiple Biomarker Assay for Accurate Estimation of Glomerular Filtration Rate.

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Analytical Validation of GFR<sub>NMR</sub>: A Blood-Based Multiple Biomarker Assay for Accurate Estimation of Glomerular Filtration Rate.

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Analytical Validation of GFRNMR: A Blood-Based Multiple Biomarker Assay for Accurate Estimation of Glomerular Filtration Rate.

Publications

Analytical Validation of GFR<sub>NMR</sub>: A Blood-Based Multiple Biomarker Assay for Accurate Estimation of Glomerular Filtration Rate.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

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Analytical Validation of GFR_NMR: A Blood-Based Multiple Biomarker Assay for Accurate Estimation of Glomerular Filtration Rate.