Startseite A novel corrective model based on red blood cells indices and haemolysis index enables accurate unhaemolysed potassium determination in haemolysed samples – Hemokalc project
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

A novel corrective model based on red blood cells indices and haemolysis index enables accurate unhaemolysed potassium determination in haemolysed samples – Hemokalc project

  • Charles R. Lefèvre ORCID logo EMAIL logo , Bérénice Vigier , Mathilde Favalelli , Jordan Garnier , Alexandre Scanff , Maxime Pawlowski , Nicolas Collet und Claude Bendavid
Veröffentlicht/Copyright: 8. August 2025
Veröffentlichen auch Sie bei De Gruyter Brill
Manuskript einreichen Informationen für Autor*innen
Clinical Chemistry and Laboratory Medicine (CCLM)
Aus der Zeitschrift Clinical Chemistry and Laboratory Medicine (CCLM) Band 63 Heft 11

Abstract

Objectives

Haemolysis is a major preanalytical issue that affects potassium measurements, often leading to sample rejection and delayed clinical management. This study proposes a novel corrective model for accurate unhaemolysed potassium prediction.

Methods

Blood samples from 14 healthy volunteers were used to prepare a range of haemolysates via freeze-thaw method. First, the relationship between potassium variation and haemolysis variation (ΔK/ΔHI) was studied both individually and globally to assess inter-individual variability. Then, to achieve a more personalised unhaemolysed potassium prediction, a novel corrective model was developed based on: potassium levels in paired unhaemolysed and gradually haemolysed samples, measured haemolysis index, mean corpuscular haemoglobin concentration, mean corpuscular volume and intraerythrocytic potassium level. The bias between true and model-predicted unhaemolysed potassium values was calculated and compared to the reference change value (RCV%).

Results

Global data showed a strong correlation between ΔK and ΔHI (Pearson r=0.97, p<0.0001), following a linear relationship: ΔK=0.33*ΔHI (p<0.0001). However, individual data revealed substantial inter-individual variation (min ΔK=0.23*ΔHI and max ΔK=0.39*ΔHI). The correction model achieved 100 % accuracy for the 116 prepared samples, with predicted unhaemolysed potassium values falling within a ± 10 % bias range (mean ± standard deviation of bias = −0.5 ± 2.8 %).

Conclusions

We propose a novel, reliable, and cost-effective corrective model to predict unhaemolysed potassium from haemolysed samples. Compared with previously published models, the integration of red blood cells indices allows for a more personalised, patient-centred approach with high efficiency.

Keywords: haemolysis; spurious hyperkalaemia; pseudohyperkalaemia; corrective model
Corresponding author: Dr. Charles R. Lefèvre, Laboratoire de Biochimie-Toxicologie, Hôpital Pontchaillou, CHU de Rennes, 2 rue Henri Le Guilloux, 35000, Rennes, France; Clinical Biochemistry and Toxicology Laboratory, Rennes University Hospital Centre, Rennes, France; and INSERM, INRAE, Univ Rennes, Institut NUMECAN, UMR_S1317, 35000 Rennes, France, E-mail:
Bérénice Vigier and Mathilde Favalelli contributed equally to this work.

  1. Research ethics: Not applicable.

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: ChatGPT was used to improve language of the manuscript.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

References

1. Gumz, ML, Rabinowitz, L, Wingo, CS. An integrated view of potassium homeostasis. N England J Med 2015;373:60–72. https://doi.org/10.1056/NEJMra1313341.Suche in Google Scholar PubMed PubMed Central

2. Zacchia, M, Abategiovanni, ML, Stratigis, S, Capasso, G. Potassium: from physiology to clinical implications. Kidney Dis (Basel) 2016;2:72–9. https://doi.org/10.1159/000446268.Suche in Google Scholar PubMed PubMed Central

3. Gowans, EM, Fraser, CG. Longer-term biological variation of commonly analyzed serum constituents. Clin Chem 1987;33:717. https://doi.org/10.1093/clinchem/33.5.717.Suche in Google Scholar

4. Aarsand, AK, Díaz-Garzón, J, Fernandez-Calle, P, Guerra, E, Locatelli, M, Bartlett, WA, et al.. The EuBIVAS: within- and between-subject biological variation data for electrolytes, lipids, urea, uric acid, total protein, total bilirubin, direct bilirubin, and glucose. Clin Chem 2018;64:1380–93. https://doi.org/10.1373/clinchem.2018.288415.Suche in Google Scholar PubMed

5. Engelhardt, LJ, Balzer, F, Müller, MC, Grunow, JJ, Spies, CD, Christopher, KB, et al.. Association between potassium concentrations, variability and supplementation, and in-hospital mortality in ICU patients: a retrospective analysis. Ann Intensive Care 2019;9:100. https://doi.org/10.1186/s13613-019-0573-0.Suche in Google Scholar PubMed PubMed Central

6. Ma, I, Guo, M, Lau, CK, Ramdas, Z, Jackson, R, Naugler, C. Test volume data for 51 most commonly ordered laboratory tests in Calgary, Alberta, Canada. Data Brief 2019;23:103748. https://doi.org/10.1016/j.dib.2019.103748.Suche in Google Scholar PubMed PubMed Central

7. Yin, T, Herskovits, AZ. The impact of hemolysis-index thresholds on plasma and serum potassium measurements. J Appl Lab Med 2022;7:788–93. https://doi.org/10.1093/jalm/jfab156.Suche in Google Scholar PubMed PubMed Central

8. Lippi, G, Blanckaert, N, Bonini, P, Green, S, Kitchen, S, Palicka, V, et al.. Haemolysis: an overview of the leading cause of unsuitable specimens in clinical laboratories. Clin Chem Lab Med 2008;46:764–72. https://doi.org/10.1515/CCLM.2008.170.Suche in Google Scholar PubMed

9. Omar, E, Allen, JC, Jamil, AKBM, Iskandar, MFKB, Norbu, K, Tsang, C, et al.. Reducing blood sample hemolysis in the emergency department using S-Monovette® in aspiration mode. Pract Lab Med 2023;35:e00315. https://doi.org/10.1016/j.plabm.2023.e00315.Suche in Google Scholar PubMed PubMed Central

10. Phelan, MP, Hustey, FM, Good, DM, Reineks, EZ. Seeing red: blood sample hemolysis is associated with prolonged emergency department throughput. J Appl Lab Med 2020;5:732–7. https://doi.org/10.1093/jalm/jfaa073.Suche in Google Scholar PubMed

11. Green, SF. The cost of poor blood specimen quality and errors in preanalytical processes. Clin Biochem 2013;46:1175–9. https://doi.org/10.1016/j.clinbiochem.2013.06.001.Suche in Google Scholar PubMed

12. Cadamuro, J, Fiedler, GM, Mrazek, C, Felder, TK, Oberkofler, H, Kipman, U, et al.. In-vitro hemolysis and its financial impact using different blood collection systems. LaboratoriumsMedizin 2016;40:49–55. https://doi.org/10.1515/labmed-2015-0078.Suche in Google Scholar

13. Simundic, A-M, Baird, G, Cadamuro, J, Costelloe, SJ, Lippi, G. Managing hemolyzed samples in clinical laboratories. Crit Rev Clin Lab Sci 2020;57:1–21. https://doi.org/10.1080/10408363.2019.1664391.Suche in Google Scholar PubMed

14. Schlüter, K, Cadamuro, J. Erroneous potassium results: preanalytical causes, detection, and corrective actions. Crit Rev Clin Lab Sci 2023;60:442–65. https://doi.org/10.1080/10408363.2023.2195936.Suche in Google Scholar PubMed

15. Dimeski, G, Clague, AE, Hickman, PE. Correction and reporting of potassium results in haemolysed samples. Ann Clin Biochem 2005;42:119–23. https://doi.org/10.1258/0004563053492739.Suche in Google Scholar PubMed

16. Clifford-Mobley, O, Sheerin, S. Verifying the haemolysis index limit for non-reporting potassium. Ann Clin Biochem 2021;58:385–7. https://doi.org/10.1177/0004563220978686.Suche in Google Scholar PubMed

17. Hawkins, R. Variability in potassium/hemoglobin ratios for hemolysis correction. Clin Chem 2002;48:796. https://doi.org/10.1093/clinchem/48.5.796.Suche in Google Scholar

18. Mansour, MMH, Azzazy, HME, Kazmierczak, SC. Correction factors for estimating potassium concentrations in samples with in vitro hemolysis: a detriment to patient safety. Arch Pathol Lab Med 2009;133:960–6. https://doi.org/10.5858/133.6.960.Suche in Google Scholar PubMed

19. Lippi, G, Avanzini, P, Da Pavesi, F, Bardi, M, Ippolito, L, Aloe, R, et al.. Studies on in vitro hemolysis and utility of corrective formulas for reporting results on hemolyzed specimens. Biochem Med 2011:297–305. https://doi.org/10.11613/BM.2011.040.Suche in Google Scholar

20. Monneret, D, Mestari, F, Atlan, G, Corlouer, C, Ramani, Z, Jaffre, J, et al.. Hemolysis indexes for biochemical tests and immunoassays on roche analyzers: determination of allowable interference limits according to different calculation methods. Scand J Clin Lab Invest 2015;75:162–9. https://doi.org/10.3109/00365513.2014.993691.Suche in Google Scholar PubMed

21. Martínez-Morillo, E, Álvarez, FV. Management of potassium results in haemolysed plasma samples at the emergency department laboratory. Clin Chem Lab Med 2019;57:e271–3. https://doi.org/10.1515/cclm-2019-0393.Suche in Google Scholar PubMed

22. Owens, H, Siparsky, G, Bajaj, LC. Hampers, L. Correction of factitious hyperkalemia in hemolyzed specimens. Am J Emerg Med 2005;23:872–5. https://doi.org/10.1016/j.ajem.2005.05.011.Suche in Google Scholar PubMed

23. Shepherd, J, Warner, MH, Poon, P, Kilpatrick, ES. Use of haemolysis index to estimate potassium concentration in in-vitro haemolysed serum samples. Clin Chem Lab Med 2006;44:877–9. https://doi.org/10.1515/CCLM.2006.140.Suche in Google Scholar PubMed

24. DiToro, DF, Conrad, MJ, Jarolim, P. Hemolysis index and potassium reporting. Am J Clin Pathol 2022;157:809–13. https://doi.org/10.1093/ajcp/aqab217.Suche in Google Scholar PubMed

25. Maimaiti, M, Yang, B, Xu, T, Cui, L, Yang, S. Accurate correction model of blood potassium concentration in hemolytic specimens. Clin Chim Acta 2024;554:117762. https://doi.org/10.1016/j.cca.2024.117762.Suche in Google Scholar PubMed

26. Cai, F, Luu, H, Logan, S, Reid, M, Gifford, JL. B-127 the effect of hemolysis on potassium measurement in a community setting: the marching error. Clin Chem 2024;70:hvae106. https://doi.org/10.1093/clinchem/hvae106.488.Suche in Google Scholar

27. Ilardo, C, Lancien, A, Barthes, J. Study of haemolysis interference limit on serum potassium assay on roche® cobas 8000 and evaluation of corrected potassium. Scand J Clin Lab Invest 2021;81:82–4. https://doi.org/10.1080/00365513.2020.1855363.Suche in Google Scholar PubMed

28. Mukerji, S, Jones, GRD. Uncertainty should not be neglected in potassium correction for haemolysis. Scand J Clin Lab Invest 2021;81:423. https://doi.org/10.1080/00365513.2021.1931710.Suche in Google Scholar PubMed

29. van Rossum, HH. Demonstrating the feasibility of accurately and reliably correcting potassium results for mildly hemolytic samples using a new experimental design. Clin Chim Acta 2021;522:83–7. https://doi.org/10.1016/j.cca.2021.08.019.Suche in Google Scholar PubMed

30. Lee, TS, Kim, J, Uh, Y, Park, YC, Yoo, GS, Yoon, KJ. Correction equation for estimation of actual potassium concentration in hemolyzed specimen. Clin Lab 2017;63:271–5. https://doi.org/10.7754/Clin.Lab.2016.160733.Suche in Google Scholar PubMed

31. Maner, BS, Killeen, RB, Moosavi, L. Mean corpuscular volume. StatPearls. Treasure Island (FL): StatPearls Publishing; 2025.Suche in Google Scholar

32. Gidske, G, Aakre, KM, Rustad, P, Sandberg, S, Norling, A, Pelanti, J, et al.. Handling of hemolyzed serum samples in clinical chemistry laboratories: the nordic hemolysis project. Clin Chem Lab Med 2019;57:1699–711. https://doi.org/10.1515/cclm-2019-0366.Suche in Google Scholar PubMed

33. Monneret, D, Godmer, A, Le Guen, R, Bravetti, C, Emeraud, C, Marteau, A, et al.. Stability of routine biochemical analytes in whole blood and plasma from lithium heparin gel tubes during 6-hr storage. J Clin Lab Anal 2016;30:602–9. https://doi.org/10.1002/jcla.21909.Suche in Google Scholar PubMed PubMed Central

34. Wang, S, Zhao, M, Su, Z, Mu, R. Annual biological variation and personalized reference intervals of clinical chemistry and hematology analytes. Clin Chem Lab Med 2022;60:606–17. https://doi.org/10.1515/cclm-2021-0479.Suche in Google Scholar PubMed

35. Aarsand, A, Fernandez-Calle, P, Webster, C, Coskun, A, Gonzales-Lao, E, Diaz-Garzon, J, et al.. The EFLM biological variation database; n.d. https://biologicalvariation.eu/[Accessed 10 Jan 2024].Suche in Google Scholar

36. Faul, F, Erdfelder, E, Buchner, A, Lang, A-G. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods 2009;41:1149–60. https://doi.org/10.3758/BRM.41.4.1149.Suche in Google Scholar PubMed

37. Simundic, A-M, Bölenius, K, Cadamuro, J, Church, S, Cornes, MP, Van Dongen-Lases, EC, et al.. Joint EFLM-COLABIOCLI recommendation for venous blood sampling. Clin Chem Lab Med 2018;56:2015–38. https://doi.org/10.1515/cclm-2018-0602.Suche in Google Scholar PubMed

38. Garcia-Castrillo, L, Cadamuro, J, Dodt, C, Lauwaert, D, Hachimi-Idrissi, S, Van Der Linden, C, et al.. Recommendations for blood sampling in emergency departments from the European Society for Emergency Medicine (EUSEM), European Society for Emergency Nursing (EuSEN), and European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for the Preanalytical Phase. Executive summary. Clin Chem Lab Med 2024;62:1538–47. https://doi.org/10.1515/cclm-2024-0059.Suche in Google Scholar PubMed

39. Heiligers-Duckers, C, Peters, NALR, Van Dijck, JJP, Hoeijmakers, JMJ, Janssen, MJW. Low vacuum and discard tubes reduce hemolysis in samples drawn from intravenous catheters. Clin Biochem 2013;46:1142–4. https://doi.org/10.1016/j.clinbiochem.2013.04.005.Suche in Google Scholar PubMed

40. Phelan, MP, Reineks, EZ, Berriochoa, JP, Schold, JD, Hustey, FM, Chamberlin, J, et al.. Impact of use of smaller volume, smaller vacuum blood collection tubes on hemolysis in emergency department blood samples. Am J Clin Pathol 2017;148:330–5. https://doi.org/10.1093/ajcp/aqx082.Suche in Google Scholar PubMed

41. Yalçınlı, S, Akarca, FK, Can, Ö, Uz, İ, Konakçı, G. Comparison of standard technique, ultrasonography, and near-infrared light in difficult peripheral vascular access: a randomized controlled trial. Prehospital Disaster Med 2022;37:65–70. https://doi.org/10.1017/S1049023X21001217.Suche in Google Scholar PubMed

42. Lippi, G, Cadamuro, J, Von Meyer, A, Simundic, A-M. Practical recommendations for managing hemolyzed samples in clinical chemistry testing. Clin Chem Lab Med 2018;56:718–27. https://doi.org/10.1515/cclm-2017-1104.Suche in Google Scholar PubMed

43. Von Meyer, A, Cadamuro, J, Lippi, G, Simundic, A-M. Call for more transparency in manufacturers declarations on serum indices: on behalf of the working group for preanalytical phase (WG-PRE), European Federation of clinical chemistry and laboratory medicine (EFLM). Clin Chim Acta 2018;484:328–32. https://doi.org/10.1016/j.cca.2018.03.043.Suche in Google Scholar PubMed

44. Lippi, G, Luca Salvagno, G, Blanckaert, N, Giavarina, D, Green, S, Kitchen, S, et al.. Multicenter evaluation of the hemolysis index in automated clinical chemistry systems. Clin Chem Lab Med 2009;47:934–9. https://doi.org/10.1515/CCLM.2009.218.Suche in Google Scholar PubMed

45. Dietzen, DJ, Jackups, R, Zaydman, MA. Clinical implications of inaccurate potassium determination in hemolyzed pediatric blood specimens. Clin Chim Acta 2024;557:117862. https://doi.org/10.1016/j.cca.2024.117862.Suche in Google Scholar PubMed

46. Zengi, O, Boz, M, Yesil, BS, Gumus, A. Hemolysis detection for ethanol measurement in whole blood samples before centrifugation: hemcheck device evaluation. J Med Biochem 2023;42:600–6. https://doi.org/10.5937/jomb0-41574.Suche in Google Scholar PubMed PubMed Central

47. Balasubramanian, S, McDowell, EJ, Laryea, ET, Blankenstein, G, Pamidi, PVA, Winkler, AM, et al.. Novel In-Line hemolysis detection on a blood gas analyzer and impact on whole blood potassium results. Clin Chem 2024;70:1485–93. https://doi.org/10.1093/clinchem/hvae135.Suche in Google Scholar PubMed

48. Pighi, L, Salvagno, GL, Lippi, G. Limitations of hemolysis-based correction for potassium measurement in hemolyzed whole blood samples. Clin Chim Acta 2025;574:120353. https://doi.org/10.1016/j.cca.2025.120353.Suche in Google Scholar PubMed

49. Gómez Rioja, R, Ventura, M, Llopis, MA, Bauça, JM, Caballero Garralda, A, Ibarz, M, et al.. External quality assessment of serum indices: spanish SEQC-ML program. Clin Chem Lab Med 2022;60:66–73. https://doi.org/10.1515/cclm-2021-0786.Suche in Google Scholar PubMed

50. Plebani, M, Lippi, G. Hemolysis index: quality indicator or criterion for sample rejection? Clin Chem Lab Med 2009;47:899–902. https://doi.org/10.1515/CCLM.2009.229.Suche in Google Scholar PubMed

Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2025-0330).

Received: 2025-03-17
Accepted: 2025-07-21
Published Online: 2025-08-08
Published in Print: 2025-10-27

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. Advancing diagnostic stewardship through claims-based utilization analysis: toward a system-wide vision of diagnostic excellence
  4. Review
  5. Biomarkers in body fluids and their detection techniques for human intestinal permeability assessment
  6. Mini Review
  7. Challenges of using natriuretic peptides to screen for the risk of developing heart failure in patients with diabetes: a report from the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Committee on Clinical Applications of Cardiac Bio-Markers (C-CB)
  8. Opinion Papers
  9. Reference intervals in value-based laboratory medicine: a shift from single-point measurements to metabolic variation-based models
  10. Overview of laboratory diagnostics for immediate management of patients presenting to the emergency department with acute bleeding
  11. What Matters Most: an Age-Friendly approach to pathology and laboratory medicine
  12. No fault or negligence after an adverse analytical finding due to a contaminated supplement: mission impossible. Two examples involving trimetazidine
  13. General Clinical Chemistry and Laboratory Medicine
  14. Utilization analysis of laboratory tests using health insurance claims data: advancing nationwide diagnostic stewardship monitoring systems
  15. Evaluating large language models as clinical laboratory test recommenders in primary and emergency care: a crucial step in clinical decision making
  16. A novel corrective model based on red blood cells indices and haemolysis index enables accurate unhaemolysed potassium determination in haemolysed samples – Hemokalc project
  17. Validation of (self-collected) capillary blood using a topper collection system as alternative for venous sampling for 15 common clinical chemistry analytes
  18. Acoustophoresis-based blood sampling and plasma separation for potentially minimizing sampling-related blood loss
  19. Clinical validation of a liquid chromatography single quadrupole mass spectrometry (LC-MS) method using Waters Kairos™ Amino Acid Kit reagents
  20. Robustness of steroidomics-based machine learning for diagnosis of primary aldosteronism: a laboratory medicine perspective
  21. Investigation of the possible cause of over-estimation of human aldosterone in plasma, using a unique, non-synthetic human aldosterone-free matrix
  22. Performance of afternoon (16:00 h) serum cortisol for the diagnosis of Cushing’s syndrome
  23. MAGLUMI® Tacrolimus (CLIA) assay: analytical performances and comparison with LC-MS/MS and ARCHITECT Tacrolimus (CMIA) assay
  24. Assessment of 2023 ACR/EULAR antiphospholipid syndrome classification criteria in a Spanish cohort
  25. Comprehensive evaluation of antiphospholipid antibody testing methodologies in APS diagnosis: performance comparisons across assay systems and clinical subtypes
  26. Candidate Reference Measurement Procedures and Materials
  27. Exploring commutable materials for serum folate measurement: challenges in cross-method harmonization
  28. Reference Values and Biological Variations
  29. Reference ranges for ionized calcium in plasma in Danish children aged 0 days to 3 years using laboratory registry data
  30. A step forward in pediatric hemophagocytic lymphohistiocytosis and autoimmune disease: pediatric reference interval for serum soluble IL-2 receptor and soluble CD163
  31. Cancer Diagnostics
  32. Cellular expression of PD-1, PD-L1 and CTLA-4 in patients with JAK2V617F mutated myeloproliferative disorders
  33. Diabetes
  34. Serum N-glycans as independent predictors of the incidence of type 2 diabetes: a prospective investigation in the AEGIS cohort
  35. Infectious Diseases
  36. An assessment of molecular diagnosis of tuberculosis and multi-drug resistant tuberculosis testing and quality assessment: findings of an international survey
  37. Letters to the Editor
  38. Targeting low-value laboratory care
  39. Is time a significant factor in the release of potassium from lithium heparin plasma and serum?
  40. External quality assessment in resource-constrained laboratories: a survey of practices and perceptions in Nepal
  41. Is successfulness of platelet clump disaggregation by vortexing influenced by platelet measurement methods?
  42. Oligoclonal banding analysis: assessing plasma use and time interval requirements for paired CSF and blood
https://doi.org/10.1515/cclm-2025-0330
Schlagwörter für diesen Artikel
haemolysis; spurious hyperkalaemia; pseudohyperkalaemia; corrective model

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. Advancing diagnostic stewardship through claims-based utilization analysis: toward a system-wide vision of diagnostic excellence
  4. Review
  5. Biomarkers in body fluids and their detection techniques for human intestinal permeability assessment
  6. Mini Review
  7. Challenges of using natriuretic peptides to screen for the risk of developing heart failure in patients with diabetes: a report from the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Committee on Clinical Applications of Cardiac Bio-Markers (C-CB)
  8. Opinion Papers
  9. Reference intervals in value-based laboratory medicine: a shift from single-point measurements to metabolic variation-based models
  10. Overview of laboratory diagnostics for immediate management of patients presenting to the emergency department with acute bleeding
  11. What Matters Most: an Age-Friendly approach to pathology and laboratory medicine
  12. No fault or negligence after an adverse analytical finding due to a contaminated supplement: mission impossible. Two examples involving trimetazidine
  13. General Clinical Chemistry and Laboratory Medicine
  14. Utilization analysis of laboratory tests using health insurance claims data: advancing nationwide diagnostic stewardship monitoring systems
  15. Evaluating large language models as clinical laboratory test recommenders in primary and emergency care: a crucial step in clinical decision making
  16. A novel corrective model based on red blood cells indices and haemolysis index enables accurate unhaemolysed potassium determination in haemolysed samples – Hemokalc project
  17. Validation of (self-collected) capillary blood using a topper collection system as alternative for venous sampling for 15 common clinical chemistry analytes
  18. Acoustophoresis-based blood sampling and plasma separation for potentially minimizing sampling-related blood loss
  19. Clinical validation of a liquid chromatography single quadrupole mass spectrometry (LC-MS) method using Waters Kairos™ Amino Acid Kit reagents
  20. Robustness of steroidomics-based machine learning for diagnosis of primary aldosteronism: a laboratory medicine perspective
  21. Investigation of the possible cause of over-estimation of human aldosterone in plasma, using a unique, non-synthetic human aldosterone-free matrix
  22. Performance of afternoon (16:00 h) serum cortisol for the diagnosis of Cushing’s syndrome
  23. MAGLUMI® Tacrolimus (CLIA) assay: analytical performances and comparison with LC-MS/MS and ARCHITECT Tacrolimus (CMIA) assay
  24. Assessment of 2023 ACR/EULAR antiphospholipid syndrome classification criteria in a Spanish cohort
  25. Comprehensive evaluation of antiphospholipid antibody testing methodologies in APS diagnosis: performance comparisons across assay systems and clinical subtypes
  26. Candidate Reference Measurement Procedures and Materials
  27. Exploring commutable materials for serum folate measurement: challenges in cross-method harmonization
  28. Reference Values and Biological Variations
  29. Reference ranges for ionized calcium in plasma in Danish children aged 0 days to 3 years using laboratory registry data
  30. A step forward in pediatric hemophagocytic lymphohistiocytosis and autoimmune disease: pediatric reference interval for serum soluble IL-2 receptor and soluble CD163
  31. Cancer Diagnostics
  32. Cellular expression of PD-1, PD-L1 and CTLA-4 in patients with JAK2V617F mutated myeloproliferative disorders
  33. Diabetes
  34. Serum N-glycans as independent predictors of the incidence of type 2 diabetes: a prospective investigation in the AEGIS cohort
  35. Infectious Diseases
  36. An assessment of molecular diagnosis of tuberculosis and multi-drug resistant tuberculosis testing and quality assessment: findings of an international survey
  37. Letters to the Editor
  38. Targeting low-value laboratory care
  39. Is time a significant factor in the release of potassium from lithium heparin plasma and serum?
  40. External quality assessment in resource-constrained laboratories: a survey of practices and perceptions in Nepal
  41. Is successfulness of platelet clump disaggregation by vortexing influenced by platelet measurement methods?
  42. Oligoclonal banding analysis: assessing plasma use and time interval requirements for paired CSF and blood
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2025 De Gruyter Brill
Heruntergeladen am 30.10.2025 von https://www.degruyterbrill.com/document/doi/10.1515/cclm-2025-0330/html?lang=de
Button zum nach oben scrollen