Home Blood sampling frequency as a proxy for comorbidity indices when identifying patient samples for review of reference intervals
Article
Licensed
Unlicensed Requires Authentication

Blood sampling frequency as a proxy for comorbidity indices when identifying patient samples for review of reference intervals

  • Simon Lykkeboe EMAIL logo , Stine Linding Andersen , Claus Gyrup Nielsen , Peter Vestergaard and Peter Astrup Christensen
Published/Copyright: December 3, 2021

Abstract

Objectives

Indirect data mining methods have been proposed for review of published reference intervals (RIs), but methods for identifying patients with a low likelihood of disease are needed. Many indirect methods extract test results on patients with a low frequency blood sampling history to identify putative healthy individuals. Although it is implied there has been no attempt to validate if patients with a low frequency blood sampling history are healthy and if test results from these patients are suitable for RI review.

Methods

Danish nationwide health registers were linked with a blood sample database, recording a population of 316,337 adults over a ten-year period. Comorbidity indexes were defined from registrations of hospital diagnoses and redeemed prescriptions of drugs. Test results from patients identified as having a low disease burden were used for review of RIs from the Nordic Reference Interval Project (NORIP).

Results

Blood sampling frequency correlated with comorbidity Indexes and the proportion of patients without disease conditions were enriched among patients with a low number of blood samples. RIs based on test results from patients with only 1–3 blood samples per decade were for many analytes identical compared to NORIP RIs. Some analytes showed expected incongruences and gave conclusive insights into how well RIs from a more than 10 years old multi-center study (NORIP) performed on current pre-analytical and analytical methods.

Conclusions

Blood sampling frequency enhance the selection of healthy individuals for reviewing reference intervals, providing a simple method solely based on laboratory data without the addition of clinical information.


Corresponding author: Simon Lykkeboe, MSc, Department of Clinical Biochemistry, Aalborg University Hospital, Hobrovej 18-22, 9000 Aalborg, Denmark, Phone: +45 97 66 48 68, E-mail:

  1. Research funding: None declared.

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

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Not applicable.

  5. Ethical approval: The study was a technical and quality investigation in accordance with the guidelines of the Northern Denmark Regional Science and Ethics Committee.

References

1. CLSI. Defining, establishing, and verifying reference intervals in the clinical laboratory; approved guideline, document EP28-A3c, 3rd ed. Clin. Lab. Stand. Inst.; 2010.Search in Google Scholar

2. Jones, GRD, Haeckel, R, Loh, TP, Sikaris, K, Streichert, T, Katayev, A, et al.. Indirect methods for reference interval determination – review and recommendations. Clin Chem Lab Med 2019;57:20–9. https://doi.org/10.1515/cclm-2018-0073.Search in Google Scholar PubMed

3. Farrell, CJL, Nguyen, L. Indirect reference intervals: harnessing the power of stored laboratory data. Clin Biochem Rev 2019;40:99–111.10.33176/AACB-19-00022Search in Google Scholar

4. Bock, BJ, Dolan, CT, Miller, GC, Fitter, WF, Hartsell, BD, Crowson, AN, et al.. The data warehouse as a foundation for population-based reference intervals. Am J Clin Pathol 2003;120:662–70. https://doi.org/10.1309/w8j85ag4wdg6jgj9.Search in Google Scholar

5. Grossi, E, Colombo, R, Cavuto, S, Franzini, C. The REALAB project: a new method for the formulation of reference intervals based on current data. Clin Chem 2005;51:1232–40. https://doi.org/10.1373/clinchem.2005.047787.Search in Google Scholar PubMed

6. Bakan, E, Polat, H, Ozarda, Y, Ozturk, N, Baygutalp, NK, Umudum, FZ, et al.. A reference interval study for common biochemical analytes in Eastern Turkey: a comparison of a reference population with laboratory data mining. Biochem Med Zagreb 2016;26:210–23. https://doi.org/10.11613/bm.2016.023.Search in Google Scholar PubMed PubMed Central

7. Kallner, A, Gustavsson, E, Hendig, E, Hendig, E. Can age and sex related reference intervals be derived for non-healthy and non-diseased individuals from results of measurements in primary health care? Clin Chem Lab Med 2000;38:633–54. https://doi.org/10.1515/CCLM.2000.093.Search in Google Scholar PubMed

8. Brinkworth, RSA, Whitham, E, Nazeran, H. Establishment of paediatric biochemical reference intervals. Ann Clin Biochem 2004;41:321–9. https://doi.org/10.1258/0004563041201572.Search in Google Scholar PubMed

9. Lykkeboe, S, Nielsen, CG, Christensen, PA. Indirect method for validating transference of reference intervals. Clin Chem Lab Med 2018;56:463–70. https://doi.org/10.1515/cclm-2017-0574.Search in Google Scholar PubMed

10. Frank, L. When an entire country is a cohort. Science 2000;287:2398–9. https://doi.org/10.1126/science.287.5462.2398.Search in Google Scholar PubMed

11. Sørensen, HT. Regional administrative health registries as a resource in clinical epidemiology. Int J Risk Saf Med 1997;10:1–22.10.3233/JRS-1997-10101Search in Google Scholar PubMed

12. Schmidt, M, Schmidt, SAJ, Sandegaard, JL, Ehrenstein, V, Pedersen, L, Sørensen, HT. The Danish National patient registry: a review of content, data quality, and research potential. Clin Epidemiol 2015;7:449–90. https://doi.org/10.2147/clep.s91125.Search in Google Scholar

13. Kildemoes, H, Sørensen, H, Hallas, J. The Danish national prescription registry. Scand J Publ Health 2011;39:38–41. https://doi.org/10.1177/1403494810394717.Search in Google Scholar PubMed

14. Pedersen, CB, Gøtzsche, H, Møller, JØ, Mortensen, PB. The Danish Civil Registration System. A cohort of eight million persons. Dan Med Bull 2006;53:441–9.Search in Google Scholar

15. Arendt, JFH, Hansen, AT, Ladefoged, SA, Sørensen, HT, Pedersen, L, Adelborg, K. Existing data sources in clinical epidemiology: laboratory information system databases in Denmark. Clin Epidemiol 2020;12:469–75. https://doi.org/10.2147/clep.s245060.Search in Google Scholar PubMed PubMed Central

16. Thygesen, SK, Christiansen, CF, Christensen, S, Lash, TL, Sørensen, HT. The predictive value of ICD-10 diagnostic coding used to assess Charlson comorbidity index conditions in the population-based Danish National Registry of Patients. BMC Med Res Methodol 2011;11. https://doi.org/10.1186/1471-2288-11-83.Search in Google Scholar PubMed PubMed Central

17. Huber, CA, Szucs, TD, Rapold, R, Reich, O. Identifying patients with chronic conditions using pharmacy data in Switzerland: an updated mapping approach to the classification of medications. BMC Publ Health 2013;13. https://doi.org/10.1186/1471-2458-13-1030.Search in Google Scholar PubMed PubMed Central

18. Rustad, P, Felding, P, Franzson, L, Kairisto, V, Lahti, A, Mårtensson, A, et al.. The Nordic Reference Interval Project 2000: recommended reference intervals for 25 common biochemical properties. Scand J Clin Lab Invest 2004;64:271–84. https://doi.org/10.1080/00365510410006324.Search in Google Scholar PubMed

19. Pedersen, MM, Örnemark, U, Rustad, P, Steensland, H, Loikkanen, M, Ólafsdóttir, E, et al.. The Nordic Trueness Project 2002: use of reference measurement procedure values in a general clinical chemistry survey. Scand J Clin Lab Invest 2004;64:309–20. https://doi.org/10.1080/00365510410002805.Search in Google Scholar PubMed

20. Rustad, P. Evaluation spreadsheet for serum X [Internet]. Available from: http://nyenga.net/norip/X/x.htm [Cited 24 May 2017].Search in Google Scholar

21. Horn, PS, Feng, L, Li, Y, Pesce, AJ. Effect of outliers and nonhealthy individuals on reference interval estimation. Clin Chem 2001;47:2137–45. https://doi.org/10.1093/clinchem/47.12.2137.Search in Google Scholar

22. Beastall, GH. Adding value to laboratory medicine: a professional responsibility. Clin Chem Lab Med 2013;51:221–7. https://doi.org/10.1515/cclm-2012-0630.Search in Google Scholar PubMed

23. Jørgensen, LGM, Brandslund, I, Petersen, PH. Should we maintain the 95 percent reference intervals in the era of wellness testing? A concept paper. Clin Chem Lab Med 2004;42:747–51.10.1515/CCLM.2004.126Search in Google Scholar PubMed

24. Henriksen, LO, Faber, NR, Moller, MF, Nexo, E, Hansen, AB. Stability of 35 biochemical and immunological routine tests after 10 hours storage and transport of human whole blood at 21 °C. Scand J Clin Lab Invest 2014;74:603–10. https://doi.org/10.3109/00365513.2014.928940.Search in Google Scholar PubMed PubMed Central

25. Kristiansen, S, Friis-Hansen, L, Antonio Juel Jensen, C, Ingemann Hansen, S. Verification study on the NORIP LDH reference intervals with a proposed new upper reference limit. Scand J Clin Lab Invest 2018;78:421–7. https://doi.org/10.1080/00365513.2018.1481223.Search in Google Scholar PubMed

26. Christensen, PA. Reference intervals for the P-Albumin bromocresol purple method. Scand J Clin Lab Invest 2017;77:472–6. https://doi.org/10.1080/00365513.2017.1337217.Search in Google Scholar PubMed

27. Haeckel, R, Wosniok, W. The importance of correct stratifications when comparing directly and indirectly estimated reference intervals. Clin Chem Lab Med 2021;0. https://doi.org/10.1515/cclm-2021-0353.Search in Google Scholar PubMed

28. Zierk, J, Arzideh, F, Kapsner, LA, Prokosch, HU, Metzler, M, Rauh, M. Reference interval estimation from mixed distributions using truncation points and the Kolmogorov-Smirnov distance (kosmic). Sci Rep 2020;10:1–8. https://doi.org/10.1038/s41598-020-58749-2.Search in Google Scholar PubMed PubMed Central

29. Bohn, MK, Adeli, K. Application of the TML method to big data analytics and reference interval harmonization. J Lab Med 2021;45:79–85. https://doi.org/10.1515/labmed-2020-0133.Search in Google Scholar

30. Henny, J, Vassault, A, Boursier, G, Vukasovic, I, Mesko Brguljan, P, Lohmander, M, et al.. Recommendation for the review of biological reference intervals in medical laboratories. Clin Chem Lab Med 2016;54:1893–900. https://doi.org/10.1515/cclm-2016-0793.Search in Google Scholar PubMed

Received: 2021-09-07
Accepted: 2021-11-21
Published Online: 2021-12-03
Published in Print: 2022-01-27

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. The clinical value of assessing the inter-method bias: the lesson from prostate specific antigen measurement
  4. Mini Review
  5. Methods to reduce lipemic interference in clinical chemistry tests: a systematic review and recommendations
  6. Opinion Paper
  7. Troponin interference with special regard to macrocomplex formation
  8. Guidelines and Recommendations from Scientific Societies
  9. Use of high-sensitivity cardiac troponins in the emergency department for the early rule-in and rule-out of acute myocardial infarction without persistent ST-segment elevation (NSTEMI) in Italy
  10. Genetics and Molecular Diagnostics
  11. Effect of preexamination conditions in a centralized-testing model of non-invasive prenatal screening
  12. General Clinical Chemistry and Laboratory Medicine
  13. Comparative study of human growth hormone measurements: impact on clinical interpretation
  14. Establishing pre-analytical requirements and maximizing peptide recovery in the analytical phase for mass spectrometric quantification of amyloid-β peptides 1–42 and 1–40 in CSF
  15. Validation of the LUMIPULSE automated immunoassay for the measurement of core AD biomarkers in cerebrospinal fluid
  16. Targeted profiling of 24 sulfated and non-sulfated bile acids in urine using two-dimensional isotope dilution UHPLC-MS/MS
  17. High-resolution capillary electrophoresis for the determination of carbamylated albumin
  18. Real-time monitoring of drug laboratory test interactions: a proof of concept
  19. Afamin predicts the prevalence and incidence of nonalcoholic fatty liver disease
  20. Reference Values and Biological Variations
  21. Blood sampling frequency as a proxy for comorbidity indices when identifying patient samples for review of reference intervals
  22. Coagulation parameters in the newborn and infant – the Copenhagen Baby Heart and COMPARE studies
  23. Hematology and Coagulation
  24. Policies and practices in the field of laboratory hematology in Croatia – a current overview and call for improvement
  25. Cardiovascular Diseases
  26. Evaluation of the Atellica TnIH cardiac troponin I assay and assessment of biological equivalence
  27. Infectious Diseases
  28. Inadequate design of mutation detection panels prevents interpretation of variants of concern: results of an external quality assessment for SARS-CoV-2 variant detection
  29. Letters to the Editors
  30. The pronounced decline of anti-SARS-CoV-2 spike trimeric IgG and RBD IgG in baseline seronegative individuals six months after BNT162b2 vaccination is consistent with the need for vaccine boosters
  31. ACE polymorphism is a determinant for COVID-19 mortality in the post-vaccination era
  32. A look at the precision, sensitivity and specificity of SARS-CoV-2 RT-PCR assays through a dedicated external quality assessment round
  33. Value of hypocalcemia and thromboinflammatory biomarkers for prediction of COVID-19 severity during the second wave: were all the waves the same?
  34. Metagenomic next-generation sequencing (mNGS) confirmed a critical case of severe fever with thrombocytopenia syndrome virus (SFTSV)
  35. Biotin interference: evaluation of an updated thyroglobulin electrochemiluminescent immunoassay
  36. Evaluation of the Beckman Coulter Access Procalcitonin Assay: analytical and clinical performance
  37. Analytical performance evaluation of the new sST2 turbidimetric assay implemented in laboratory automation systems
  38. Pre-analytical recommendations and reference values for circulating calprotectin are sample type and assay dependent
  39. Congress Abstracts
  40. Annual Meeting of the Royal Belgian Society of Laboratory Medicine: “Women’s health: from puberty to menopause”
Downloaded on 4.7.2025 from https://www.degruyterbrill.com/document/doi/10.1515/cclm-2021-0987/html
Scroll to top button