The EuBIVAS Project: Within- and Between-Subject Biological Variation Data for Serum Creatinine Using Enzymatic and Alkaline Picrate Methods and Implications for Monitoring

Carobene, Anna; Marino, Irene; Coşkun, Abdurrahman; Serteser, Mustafa; Ünsal, İbrahim; Guerra, Elena; Bartlett, William A.; Sandberg, Sverre; Aarsand, Aasne K.; Sylte, Marit Sverresdotter; Røraas, Thomas; Sølvik, Una Ørvim; Fernández–Calle, Pilar; Díaz-Garzón, Jorge; Tosato, Francesca; Plebani, Mario; Jonker, Niels; Barla, Gerhard; Ceriotti, Ferruccio

doi:10.1373/clinchem.2017.275115

Cited by 64 publications

(46 citation statements)

References 24 publications

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“…In brief, compounds’ classification toward risk of clinical creatinine interaction were based on proposed unbound maximum concentration in plasma (C max,u )/IC 50 cutoffs of 0.1 and 0.02 for OCT/OAT and MATE transporters, respectively 19,20 . Changes in serum creatinine < 5%, associated with intra‐individual variability in serum creatinine, 21 were classed as “negative.”…”

Section: Methodsmentioning

confidence: 99%

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Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine‐Drug Interactions

Scotcher

Arya

Yang

et al. 2020

CPT Pharmacom & Syst Pharma

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Creatinine is widely used as a biomarker of glomerular filtration, and, hence, renal function. However, transporter-mediated secretion also contributes to its renal clearance, albeit to a lesser degree. Inhibition of these transporters causes transient serum creatinine elevation, which can be mistaken as impaired renal function. The current study developed mechanistic models of creatinine kinetics within physiologically based framework accounting for multiple transporters involved in creatinine renal elimination, assuming either unidirectional or bidirectional-OCT2 transport (driven by electrochemical gradient). Robustness of creatinine models was assessed by predicting creatinine-drug interactions with 10 perpetrators; performance evaluation accounted for 5% intra-individual variability in serum creatinine. Models showed comparable predictive performances of the maximum steady-state effect regardless of OCT2 directionality assumptions. However, only the bidirectional-OCT2 model successfully predicted the minimal effect of ranitidine. The dynamic nature of models provides clear advantage to static approaches and most advanced framework for evaluating interplay between multiple processes in creatinine renal disposition.

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Section: Methodsmentioning

confidence: 99%

“…These new prediction limits considered intra‐individual percentage coefficient of variation in baseline serum creatinine concentration (CV I ). The EuBIVAS Project recently reported CV I of 4.4% and 4.7% based on enzymatic and alkaline picrate methods, respectively, 21 and CV I of 4.7% was used in the current study.…”

Section: Methodsmentioning

confidence: 99%

Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine‐Drug Interactions

Scotcher

Arya

Yang

et al. 2020

CPT Pharmacom & Syst Pharma

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“…According to these papers, all the BV data are less than that reported on Westgard website. [7][8][9] The BV data can confirm if patients treated with antihypertensive or insulin are close to normal levels, indicating that the condition is getting better and the development of complications such as cardiovascular disease or diabetes is unlikely. 10 Therefore, the establishment of effective BV can contribute to the management of chronic diseases such as diabetes.…”

Section: Federation Of Clinical Chemistry and Laboratory Medicine (Eflm)mentioning

confidence: 95%

“…Currently, based on the checklist, there are nine enzymes, creatinine, and 21 hematological parameters reported in EFLM. According to these papers, all the BV data are less than that reported on Westgard website …”

Section: Introductionmentioning

confidence: 94%

Evaluation of biological variation of glycated hemoglobin and glycated albumin in healthy Chinese subjects

Liu

Zeng

et al. 2018

Clinical Laboratory Analysis

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Background Glycated hemoglobin (HbA1c) and glycated serum albumin ( GSA ) are used to evaluate the mean blood glucose levels. To ensure safe clinical application of HbA1c and GSA , reliable biological variation ( BV ) data are required. The aim of this research was to define the BV of HbA1c and GSA employing stringent rules. Methods Blood samples were drawn from 19 healthy subjects (10 females, nine males) once per week for 5 weeks. All samples were analyzed using enzymatic method for GSA and HPLC for HbA1c. The data were assessed for outliers, normality and variance homogeneity, and coefficient of variation (by ANOVA ) for BV . Sex‐stratified BV including within‐subject ( CV I ) and between‐subject ( CV G ) was defined for HbA1c and GSA . Results The following estimates for BV values for CV I and CV G , respectively, were GSA : 1.23% and 4.67%, Alb: 0.75% and 3.18%, and HbA1c: 0.12% and 2.91%. The RCV of GSA was 3.61%, and HbA1c was 1.41%. And the II was 0.26 for GSA , and 0.07 for HbA1c, both of them less than 0.6. According to the 95% CI , the CV I of HbA1c was statistically different between females and males. And both the CV G of HbA1c and GSA were statistically different between females and males. Conclusion All CV I and CV G estimates were lower than those reported in the online BV database. And there is a significant difference between males and females. Analytical performance specifications derived from BV of this research can be applied internationally.

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“…To this aim, the EFLM Working Group on Biological Variation (WG-BV) is working to a European project using a biobank of samples from 91 healthy subjects to be used to produce high quality data [49]. The WG-BV has recently published biological variability data for nine enzymes and creatinine in serum [50,51]. A preliminary report is also available for serum ions [52].…”

Section: Stimulating Studies Using Milan Models To Obtain Psmentioning

confidence: 99%

Strategies to define performance specifications in laboratory medicine: 3 years on from the Milan Strategic Conference

Panteghini

Ceriotti

Jones

et al. 2017

Clinical Chemistry and Laboratory Medicine (CCLM)

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Abstract:Measurements in clinical laboratories produce results needed in the diagnosis and monitoring of patients. These results are always characterized by some uncertainty. What quality is needed and what measurement errors can be tolerated without jeopardizing patient safety should therefore be defined and specified for each analyte having clinical use. When these specifications are defined, the total examination process will be "fit for purpose" and the laboratory professionals should then set up rules to control the measuring systems to ensure they perform within specifications. The laboratory community has used different models to set performance specifications (PS). Recently, it was felt that there was a need to revisit different models and, at the same time, to emphasize the presuppositions for using the different models. Therefore, in 2014 the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) organized a Strategic Conference in Milan. It was felt that there was a need for more detailed discussions on, for instance, PS for EQAS, which measurands should use which models to set PS and how to set PS for the extra-analytical phases. There was also a need to critically evaluate the quality of data on biological variation studies and further discussing the use of the total error (TE) concept. Consequently, EFLM established five Task Finish Groups (TFGs) to address each of these topics. The TFGs are finishing their activity on 2017 and the content of this paper includes deliverables from these groups.

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The EuBIVAS Project: Within- and Between-Subject Biological Variation Data for Serum Creatinine Using Enzymatic and Alkaline Picrate Methods and Implications for Monitoring

Abstract: The serum creatinine CV obtained by EuBIVAS specifies a more stringent CV than previously identified. The alkaline picrate method failed to meet this CV, raising questions regarding its future use.

Cited by 64 publications

References 24 publications

Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine‐Drug Interactions

Mechanistic Models as Framework for Understanding Biomarker Disposition: Prediction of Creatinine‐Drug Interactions

Evaluation of biological variation of glycated hemoglobin and glycated albumin in healthy Chinese subjects

Strategies to define performance specifications in laboratory medicine: 3 years on from the Milan Strategic Conference

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