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Carobene A, Kilpatrick E, Bartlett WA, Fernández Calle P, Coşkun A, Díaz-Garzón J, Jonker N, Locatelli M, Sandberg S, Aarsand AK. The biological variation of insulin resistance markers: data from the European Biological Variation Study (EuBIVAS). Clin Chem Lab Med 2024; 0:cclm-2024-0672. [PMID: 38987271 DOI: 10.1515/cclm-2024-0672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Accepted: 06/18/2024] [Indexed: 07/12/2024]
Abstract
OBJECTIVES An insulin resistant state is characteristic of patients with type 2 diabetes, polycystic ovary syndrome, and metabolic syndrome. Identification of insulin resistance (IR) is most readily achievable using formulae combining plasma insulin and glucose results. In this study, we have used data from the European Biological Variation Study (EuBIVAS) to examine the biological variability (BV) of IR using the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) and the Quantitative Insulin sensitivity Check Index (QUICKI). METHODS Ninety EuBIVAS non-diabetic subjects (52F, 38M) from five countries had fasting HOMA-IR and QUICKI calculated from plasma glucose and insulin samples collected concurrently on 10 weekly occasions. The within-subject (CVI) and between-subject (CVG) BV estimates with 95 % CIs were obtained by CV-ANOVA after analysis of trends, variance homogeneity and outlier removal. RESULTS The CVI of HOMA-IR was 26.7 % (95 % CI 25.5-28.3), driven largely by variability in plasma insulin and the CVI for QUICKI was 4.1 % (95 % CI 3.9-4.3), reflecting this formula's logarithmic transformation of glucose and insulin values. No differences in values or BV components were observed between subgroups of men or women below and above 50 years. CONCLUSIONS The EuBIVAS, by utilising a rigorous experimental protocol, has produced robust BV estimates for two of the most commonly used markers of insulin resistance in non-diabetic subjects. This has shown that HOMA-IR, in particular, is highly variable in the same individual which limits the value of single measurements.
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Affiliation(s)
- Anna Carobene
- Laboratory Medicine, 48455 IRCCS San Raffaele Scientific Institute , Milano, Italy
| | | | | | | | - Abdurrahman Coşkun
- School of Medicine, Acibadem Mehmet Ali Aydınlar University, Istanbul, Türkiye
| | - Jorge Díaz-Garzón
- Department of Laboratory Medicine, Hospital Universitario La Paz, Madrid, Spain
| | - Niels Jonker
- Certe, Wilhelmina Ziekenhuis Assen, Assen, The Netherlands
| | - Massimo Locatelli
- Laboratory Medicine, 48455 IRCCS San Raffaele Scientific Institute , Milano, Italy
| | - Sverre Sandberg
- Norwegian Porphyria Centre, Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway
- Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital, Bergen, Norway
- Department of Global Health and Primary Care, Faculty of Medicine, University of Bergen, Bergen, Norway
| | - Aasne K Aarsand
- Norwegian Porphyria Centre, Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway
- Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital, Bergen, Norway
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Moreno-Parro I, Diaz-Garzon J, Aarsand AK, Sandberg S, Aikin R, Equey T, Ríos-Blanco JJ, Buño Soto A, Fernandez-Calle P. Biological Variation Data in Triathletes for Metabolism and Growth-Related Biomarkers Included in the Athlete Biological Passport. Clin Chem 2024; 70:987-996. [PMID: 38781424 DOI: 10.1093/clinchem/hvae072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 04/08/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND When using biological variation (BV) data, BV estimates need to be robust and representative. High-endurance athletes represent a population under special physiological conditions, which could influence BV estimates. Our study aimed to estimate BV in athletes for metabolism and growth-related biomarkers involved in the Athlete Biological Passport (ABP), by 2 different statistical models. METHODS Thirty triathletes were sampled monthly for 11 months. The samples were analyzed for human growth hormone (hGH), insulin-like growth factor-1 (IGF-1), insulin-like growth factor binding protein 3 (IGFBP-3), insulin, and N-terminal propeptide of type III procollagen (P-III-NP) by immunoassay. Bayesian and ANOVA methods were applied to estimate within-subject (CVI) and between-subject BV. RESULTS CVI estimates ranged from 7.8% for IGFBP-3 to 27.0% for insulin, when derived by the Bayesian method. The 2 models gave similar results, except for P-III-NP. Data were heterogeneously distributed for P-III-NP for the overall population and in females for IGF-1 and IGFBP-3. BV components were not estimated for hGH due to lack of steady state. The index of individuality was below 0.6 for all measurands, except for insulin. CONCLUSIONS In an athlete population, to apply a common CVI for insulin would be appropriate, but for IGF-1 and IGFBP-3 gender-specific estimates should be applied. P-III-NP data were heterogeneously distributed and using a mean CVI may not be representative for the population. The high degree of individuality for IGF-1, IGFBP-3, and P-III-NP makes them good candidates to be interpreted through reference change values and the ABP.
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Affiliation(s)
- Isabel Moreno-Parro
- Department of Laboratory Medicine, La Paz University Hospital, Madrid, Spain
- IdiPaz-Hospital La Paz Institute for Health Research, Madrid, Spain
| | - Jorge Diaz-Garzon
- Department of Laboratory Medicine, La Paz University Hospital, Madrid, Spain
- IdiPaz-Hospital La Paz Institute for Health Research, Madrid, Spain
| | - Aasne K Aarsand
- Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway
- Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital, Bergen, Norway
| | - Sverre Sandberg
- Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway
- Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital, Bergen, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Reid Aikin
- World Anti-Doping Agency (WADA), Montreal, Quebec, Canada
| | - Tristan Equey
- World Anti-Doping Agency (WADA), Montreal, Quebec, Canada
| | - Juan José Ríos-Blanco
- IdiPaz-Hospital La Paz Institute for Health Research, Madrid, Spain
- Department of Internal Medicine, La Paz University Hospital, Madrid, Spain
| | - Antonio Buño Soto
- Department of Laboratory Medicine, La Paz University Hospital, Madrid, Spain
- IdiPaz-Hospital La Paz Institute for Health Research, Madrid, Spain
| | - Pilar Fernandez-Calle
- Department of Laboratory Medicine, La Paz University Hospital, Madrid, Spain
- IdiPaz-Hospital La Paz Institute for Health Research, Madrid, Spain
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Zhou C, Xie Q, Wang H, Wu F, He D, Huang Y, He Y, Dai S, Chen J, Kong L, Zhang Y. Biological variation in the estimated glomerular filtration rate of healthy individuals within 24 h calculated using 2021CKD-EPI equations. Ir J Med Sci 2024; 193:1613-1620. [PMID: 38308766 DOI: 10.1007/s11845-024-03621-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 01/25/2024] [Indexed: 02/05/2024]
Abstract
BACKGROUND AND AIMS Use the MDRD (Modification of Diet in Renal Disease) and 2021 CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation void of race coefficients (CKD-EPICrea, CKD-EPICys-C, and CKD-EPICrea+Cys-C) to estimate the BV (Biological variation) of eGFR (estimated glomerular filtration rate) within 24 h in a healthy population to help explain future studies using eGFR in the context of a known BV. METHODS Blood samples were collected from 30 healthy subjects at six time points within 24 h. Serum creatinine (S-Crea) and serum cystatin C (S-Cys-C) were measured, and the BV of eGFR was calculated. Outlier and variance homogeneity analyses were performed, followed by CV-ANOVA on trend-corrected data. RESULTS The eGFR CVI for the four equations (MDRD, CKD-EPICrea, CKD-EPICys-C, and CKD-EPICrea+Cys-C) were 8.39% (7.50-9.51%), 3.90% (3.49-4.42%), 6.58% (5.88-7.46%), and 5.03% (4.50-5.71%), respectively. The corresponding II and RCVpos/neg values were 0.69, 0.48, 0.51, and 0.31, and (29.30%, - 22.66%), (12.69%, - 11.2 6%), (20.97%, - 17.33%), and (15.88%, - 13.70%), respectively; RCVpos /neg of eGFR was highest in the MDRD equation and lowest in the CKD-EPI Crea equation. Additionally, the RCVpos/neg values of the individual was highest in the MDRD equation and lowest in the CKD-EPICrea+Cys-C equation; they are (56.51%, - 36.11%) and (5.01%, - 4.77%), respectively. CONCLUSIONS We present data on the 24 h BV eGFR of the 2021 CKD-EPI equations. The presence of BV has impact on the interpretation of GFR results, affecting CKD disease grading. The RCVpos/neg differences were large among the individuals. When using eGFRs based on the MDRD and CKD-EPI equations, it is necessary to combine RCVpos/neg values before interpreting the results.
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Affiliation(s)
- ChaoQiong Zhou
- Department of Clinical Laboratory, Traditional Chinese Medicine Hospital of Pidu District, No. 342, South Street, Pidu District, Chengdu, Sichuan, 611730, China
| | - QianRong Xie
- Department of Clinical Laboratory, Traditional Chinese Medicine Hospital of Pidu District, No. 342, South Street, Pidu District, Chengdu, Sichuan, 611730, China
- Department of Clinical Laboratory, The Third People's Hospital of Chengdu, Chengdu, Sichuan, 610000, China
| | - HuaLi Wang
- Department of Clinical Laboratory, Traditional Chinese Medicine Hospital of Pidu District, No. 342, South Street, Pidu District, Chengdu, Sichuan, 611730, China
| | - Feng Wu
- Department of Clinical Laboratory, Traditional Chinese Medicine Hospital of Pidu District, No. 342, South Street, Pidu District, Chengdu, Sichuan, 611730, China
| | - DaHai He
- Department of Clinical Laboratory, Traditional Chinese Medicine Hospital of Pidu District, No. 342, South Street, Pidu District, Chengdu, Sichuan, 611730, China
| | - Ying Huang
- Department of Clinical Laboratory, Traditional Chinese Medicine Hospital of Pidu District, No. 342, South Street, Pidu District, Chengdu, Sichuan, 611730, China
| | - Ying He
- Department of Clinical Laboratory, Traditional Chinese Medicine Hospital of Pidu District, No. 342, South Street, Pidu District, Chengdu, Sichuan, 611730, China
| | - ShiRong Dai
- Department of Clinical Laboratory, Traditional Chinese Medicine Hospital of Pidu District, No. 342, South Street, Pidu District, Chengdu, Sichuan, 611730, China
| | - Jie Chen
- Department of Clinical Laboratory, Traditional Chinese Medicine Hospital of Pidu District, No. 342, South Street, Pidu District, Chengdu, Sichuan, 611730, China
| | - LiRui Kong
- Department of Clinical Laboratory, Traditional Chinese Medicine Hospital of Pidu District, No. 342, South Street, Pidu District, Chengdu, Sichuan, 611730, China.
| | - Yan Zhang
- Department of Clinical Laboratory, Traditional Chinese Medicine Hospital of Pidu District, No. 342, South Street, Pidu District, Chengdu, Sichuan, 611730, China.
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Sandberg S, Carobene A, Bartlett B, Coskun A, Fernandez-Calle P, Jonker N, Díaz-Garzón J, Aarsand AK. Biological variation: recent development and future challenges. Clin Chem Lab Med 2022; 61:741-750. [PMID: 36537071 DOI: 10.1515/cclm-2022-1255] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 12/12/2022] [Indexed: 02/18/2023]
Abstract
Abstract
Biological variation (BV) data have many applications in laboratory medicine. However, these depend on the availability of relevant and robust BV data fit for purpose. BV data can be obtained through different study designs, both by experimental studies and studies utilizing previously analysed routine results derived from laboratory databases. The different BV applications include using BV data for setting analytical performance specifications, to calculate reference change values, to define the index of individuality and to establish personalized reference intervals. In this review, major achievements in the area of BV from last decade will be presented and discussed. These range from new models and approaches to derive BV data, the delivery of high-quality BV data by the highly powered European Biological Variation Study (EuBIVAS), the Biological Variation Data Critical Appraisal Checklist (BIVAC) and other standards for deriving and reporting BV data, the EFLM Biological Variation Database and new applications of BV data including personalized reference intervals and measurement uncertainty.
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Affiliation(s)
- Sverre Sandberg
- Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital , Bergen , Norway
- Department of Medical Biochemistry and Pharmacology , Norwegian Porphyria Centre, Haukeland University Hospital , Bergen , Norway
- Department of Global Public Health and Primary Care , University of Bergen , Bergen , Norway
| | - Anna Carobene
- Laboratory Medicine, IRCCS San Raffaele Scientific Institute , Milan , Italy
| | - Bill Bartlett
- School of Science and Engineering, University of Dundee , Dundee , Scotland
| | - Abdurrahman Coskun
- Acibadem Mehmet Ali Aydınlar University, School of Medicine , Istanbul , Türkiye
| | - Pilar Fernandez-Calle
- Hospital Universitario La Paz, Quality Analytical Commission of Spanish Society of Clinical Chemistry (SEQC) , Madrid , Spain
| | - Niels Jonker
- Certe, Wilhelmina Ziekenhuis Assen , Assen , The Netherlands
| | - Jorge Díaz-Garzón
- Hospital Universitario La Paz, Quality Analytical Commission of Spanish Society of Clinical Chemistry (SEQC) , Madrid , Spain
| | - Aasne K. Aarsand
- Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital , Bergen , Norway
- Department of Medical Biochemistry and Pharmacology , Norwegian Porphyria Centre, Haukeland University Hospital , Bergen , Norway
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5
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Sandberg S, Carobene A, Aarsand AK. Biological variation - eight years after the 1st Strategic Conference of EFLM. Clin Chem Lab Med 2022; 60:465-468. [PMID: 35138052 DOI: 10.1515/cclm-2022-0086] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Sverre Sandberg
- Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital, Bergen, Norway.,Department of Medical Biochemistry and Pharmacology, Norwegian Porphyria Centre, Haukeland University Hospital, Bergen, Norway.,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Anna Carobene
- Laboratory Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Aasne K Aarsand
- Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital, Bergen, Norway.,Department of Medical Biochemistry and Pharmacology, Norwegian Porphyria Centre, Haukeland University Hospital, Bergen, Norway
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Tan RZ, Markus C, Vasikaran S, Loh TP. Comparison of four indirect (data mining) approaches to derive within-subject biological variation. Clin Chem Lab Med 2022; 60:636-644. [PMID: 35107229 DOI: 10.1515/cclm-2021-0442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 01/21/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVES Within-subject biological variation (CV i ) is a fundamental aspect of laboratory medicine, from interpretation of serial results, partitioning of reference intervals and setting analytical performance specifications. Four indirect (data mining) approaches in determination of CV i were directly compared. METHODS Paired serial laboratory results for 5,000 patients was simulated using four parameters, d the percentage difference in the means between the pathological and non-pathological populations, CV i the within-subject coefficient of variation for non-pathological values, f the fraction of pathological values, and e the relative increase in CV i of the pathological distribution. These parameters resulted in a total of 128 permutations. Performance of the Expected Mean Squares method (EMS), the median method, a result ratio method with Tukey's outlier exclusion method and a modified result ratio method with Tukey's outlier exclusion were compared. RESULTS Within the 128 permutations examined in this study, the EMS method performed the best with 101/128 permutations falling within ±0.20 fractional error of the 'true' simulated CV i , followed by the result ratio method with Tukey's exclusion method for 78/128 permutations. The median method grossly under-estimated the CV i . The modified result ratio with Tukey's rule performed best overall with 114/128 permutations within allowable error. CONCLUSIONS This simulation study demonstrates that with careful selection of the statistical approach the influence of outliers from pathological populations can be minimised, and it is possible to recover CV i values close to the 'true' underlying non-pathological population. This finding provides further evidence for use of routine laboratory databases in derivation of biological variation components.
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Affiliation(s)
- Rui Zhen Tan
- Engineering Cluster, Singapore Institute of Technology, Singapore, Singapore
| | - Corey Markus
- Flinders University International Centre for Point-of-Care Testing, Flinders Health and Medical Research Institute, Flinders University, Rundle Mall, South Australia, Australia
| | - Samuel Vasikaran
- Department of Clinical Biochemistry, PathWest-Royal Perth Hospital, Perth, Western Australia, Australia
| | - Tze Ping Loh
- Department of Laboratory Medicine, National University Hospital, Singapore, Singapore
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Haeckel R, Carobene A, Wosniok W. Problems with estimating reference change values (critical differences). Clin Chim Acta 2021; 523:437-440. [PMID: 34653386 DOI: 10.1016/j.cca.2021.10.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 10/06/2021] [Indexed: 11/19/2022]
Abstract
The concept of reference change values (RCVs) for diagnosis and monitoring of diseases has become well established. Several models habe been developed, e. g. one assuming a normal distribution and another one for a log-normal distribution. RCV values calculated for some measurands with both models are compared with each other and led to similar results. A few examples led to RCV values which are not plausible for diagnostic purposes. Although statistical concepts of RCV values are well established, their clinical relevance remains questionable at least for some measurands. Studies with clinicians are required whether RCVs are of practical usefulness.
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Affiliation(s)
- Rainer Haeckel
- Bremer Zentrum für Laboratoriumsmedizin, Klinikum Bremen Mitte, 28305 Bremen, Germany.
| | - Anna Carobene
- Laboratory Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Werner Wosniok
- Institut für Statistik, Universität Bremen, 28359 Bremen, Germany
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Carobene A, Aarsand AK, Bartlett WA, Coskun A, Diaz-Garzon J, Fernandez-Calle P, Guerra E, Jonker N, Locatelli M, Plebani M, Sandberg S, Ceriotti F. The European Biological Variation Study (EuBIVAS): a summary report. Clin Chem Lab Med 2021; 60:505-517. [PMID: 34049424 DOI: 10.1515/cclm-2021-0370] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 05/14/2021] [Indexed: 12/20/2022]
Abstract
Biological variation (BV) data have many important applications in laboratory medicine. Concerns about quality of published BV data led the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) 1st Strategic Conference to indicate need for new studies to generate BV estimates of required quality. In response, the EFLM Working Group on BV delivered the multicenter European Biological Variation Study (EuBIVAS). This review summarises the EuBIVAS and its outcomes. Serum/plasma samples were taken from 91 ostensibly healthy individuals for 10 consecutive weeks at 6 European centres. Analysis was performed by Siemens ADVIA 2400 (clinical chemistry), Cobas Roche 8000, c702 and e801 (proteins and tumor markers/hormones respectively), ACL Top 750 (coagulation parameters), and IDS iSYS or DiaSorin Liaison (bone biomarkers). A strict preanalytical and analytical protocol was applied. To determine BV estimates with 95% CI, CV-ANOVA after analysis of outliers, homogeneity and trend analysis or a Bayesian model was applied. EuBIVAS has so far delivered BV estimates for 80 different measurands. Estimates for 10 measurands (Non-HDL Cholesterol, S100-β protein, neuron-specific enolase, soluble transferrin receptor, intact fibroblast growth-factor-23, uncarboxylated-unphosphorylated matrix-Gla protein, human epididymis protein-4, free, conjugated and %free prostate-specific antigen), prior to EuBIVAS, have not been available. BV data for creatinine and troponin I were obtained using two analytical methods in each case. The EuBIVAS has delivered high-quality BV data for a wide range of measurands. The BV estimates are for many measurands lower than those previously reported, having an impact on the derived analytical performance specifications and reference change values.
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Affiliation(s)
- Anna Carobene
- Laboratory Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Aasne K Aarsand
- Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway.,Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital, Bergen, Norway
| | | | - Abdurrahman Coskun
- Acibadem Mehmet Ali Aydınlar University, School of Medicine, Istanbul, Turkey
| | - Jorge Diaz-Garzon
- Hospital Universitario La Paz, and Quality Analytical Commission of Spanish Society of Laboratory Medicine (SEQCML), Madrid, Spain
| | - Pilar Fernandez-Calle
- Hospital Universitario La Paz, and Quality Analytical Commission of Spanish Society of Laboratory Medicine (SEQCML), Madrid, Spain
| | - Elena Guerra
- Laboratory Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Niels Jonker
- Certe-Wilhelmina Ziekenhuis Assen, Europaweg-Zuid 1, Assen, The Netherlands
| | - Massimo Locatelli
- Laboratory Medicine, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Mario Plebani
- Department of Laboratory Medicine, University Hospital of Padova, Padova, Italy
| | - Sverre Sandberg
- Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway.,Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital, Bergen, Norway.,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Ferruccio Ceriotti
- Central Laboratory, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
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