Journal:Existing data sources in clinical epidemiology: Laboratory information system databases in Denmark

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Full article title Existing data sources in clinical epidemiology: Laboratory information system databases in Denmark
Journal Clinical Epidemiology
Author(s) Arendt, Johan F.H.; Hansen, Anette T.; Ladefoged, Søren A.; Sørensen, Henrik T.; Pedersen, Lars; Adelborg, Kasper
Author affiliation(s) Aarhus University Hospital, Aalborg University Hospital
Primary contact Email: jfba at clin dot au dot dk
Editors Dr. Eyal Cohen
Year published 2020
Volume and issue 12
Page(s) 469–75
DOI 10.2147/CLEP.S245060
ISSN 1179-1349
Distribution license Creative Commons Attribution-NonCommercial 3.0 Unported
Website https://www.dovepress.com/existing-data-sources-in-clinical-epidemiology-laboratory-information
Download https://www.dovepress.com/getfile.php?fileID=58155 (PDF)
Emblem-important-yellow.svg This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed.

Abstract

Routine biomarker results from hospital laboratory information systems (LIS)—covering hospitals and general practitioners—in Denmark are available to researchers through access to the regional Clinical Laboratory Information System Research Database at Aarhus University and the nationwide Register of Laboratory Results for Research. This review describes these two data sources. The laboratory databases have different geographical and temporal coverage. They both include individual-level biomarker results that are electronically transferred from LISs. The biomarker results can be linked to all other Danish registries at the individual level using the unique identifier, the CPR number. The databases include variables such as the CPR number, date and time (hour and minute) of sampling, NPU code, and name of the biomarker, identification code for the laboratory and the requisitioner, the test result with the corresponding unit, and the lower and upper reference limits. Access to the two databases differs since they are hosted by two different institutions. Data cannot be transferred outside Denmark, and direct access is provided only to Danish institutions. It is concluded that access to data on routine biomarkers expands the detailed biological and clinical information available on patients in the Danish healthcare system. The full potential is enabled through linkage to other Danish healthcare registries.

Keywords: biomarkers, database, data resource, laboratory information systems

Data resource basics

Danish healthcare registries

Denmark has a large network of population-based healthcare registries.[1] Several supplementary issues of international medical journals have been devoted to describe these registries to facilitate their use in research.[2][3] The registries contain data on the Danish population of approximately 5.8 millions inhabitants and include data on healthcare contacts, hospital diagnoses, redeemed prescriptions, childbirths, causes of death, and a wide variety of other information. The Civil Registration System and the healthcare system in Denmark have been described in detail elsewhere.[1][4] In short, all Danish residents are assigned a unique 10-digit personal identification number, the CPR number, upon birth or immigration. The CPR number serves as the key identifier in the nationwide registration of personal data, including contacts with the healthcare system. The CPR number also allows Statistics Denmark to produce updated population statistics at the individual level on a variety of different topics, including but not limited to demographics, income, education, and healthcare use.

The Danish healthcare system provides free healthcare to all residents at general practices and hospitals through a tax-paid system. This allows for long-term and virtually complete follow-up at the individual level across the different sources of healthcare data.

This review provides an overview of the two Danish healthcare research databases that collect routine individual-level biomarker data from general practitioners and hospital encounters: the regional Clinical Laboratory Information System Research (LABKA) Database at Aarhus University[5] and the nationwide Register of Laboratory Results for Research (RLRR).[6] These specific databases enable researchers to obtain very detailed and comprehensive biological information for research purposes.

Hospital laboratories are an integrated part of the public hospital system. Private medical laboratories play a minor role for routine biomarker analyses in Denmark, as they only provide a very limited number of the test results, and mainly for low throughput and highly specialized biomarkers, e.g., lactate dehydrogenase isoenzymes and metabolic intermediates.

Routine clinical biomarkers in the Danish healthcare system

Biomarkers are used in the everyday clinical workflow in general medical practices and in the hospital setting for diagnosing, screening, monitoring, and assessing prognoses of patients, as well as evaluating treatment effects and safety for a given patient. Upon physician request, biological samples (blood, urine, cerebrospinal fluid, and other body fluids) undergo biomarker analysis in public hospital laboratories, and thus biomarker data are recorded in the registries as a byproduct of healthcare provision. The biomarker results are registered in the electronic laboratory information systems (LISs) using the International System of Nomenclature, Properties, and Units (the NPU system).[7] Only a select few biomarker results are recorded by non-electronic methods, including some of the highly specialized biomarkers. The NPU system, an international standardised terminology for reporting on laboratory results, was established in 1960 by the International Union of Pure and Applied Chemistry (IUPAC) and the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). The NPU system is equivalent to the Logical Observation Identifiers Names and Codes (LOINC) used in other parts of the world.[8] The organization of the NPU system complies with recommendations from these two leading international laboratory organizations. In Denmark, the NPU system was implemented in 2001, but some biomarkers do not comply with the NPU system and are coded using national or local codes. The results for these biomarkers are also included in the LISs.

The Danish hospital laboratories use a LIS for managing data on requisitions, specimen handling, transportation, and biomarker results. The LABKA database and the RLRR described in this review mainly cover routine biomarker results, including some results for therapeutic drug monitoring and genetic tests. Clinical microbiology and pathology are not included in these two research databases, but they are covered in other databases.[9][10][11]

Specifications for the LABKA database and the RLRR are described in detail below and in Table 1. The two databases show some degree of overlap, since some geographical regions of Denmark are represented in both databases.



References

  1. ↑ 1.0 1.1 Schmidt, M.; Schmidt, S.A.J.; Adelborg, K. et al. (2019). "The Danish health care system and epidemiological research: From health care contacts to database records". Clinical Epidemiology 11: 563–91. doi:10.2147/CLEP.S179083. PMC PMC6634267. PMID 31372058. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6634267. 
  2. ↑ Thygesen, L.C.; Ersbøll, A.K. (2011). "Danish population-based registers for public health and health-related welfare research: introduction to the supplement". Scandinavian Journal of Public Health 39 (7 Suppl.): 8–10. doi:10.1177/1403494811409654. PMID 21775344. 
  3. ↑ Sørensen, H.T.; Pedersen, L.; Jørgensen, J. et al. (2016). "Danish clinical quality databases - an important and untapped resource for clinical research". Clinical Epidemiology 8: 425-427. doi:10.2147/CLEP.S113265. PMID 27843338. 
  4. ↑ Schmidt, M.; Pedersen, L.; Sørensen, H.T. (2014). "The Danish Civil Registration System as a tool in epidemiology". European Journal of Epidemiology 29 (8): 541–9. doi:10.1007/s10654-014-9930-3. PMID 24965263. 
  5. ↑ Grann, A.F.; Erichsen, R.; Nielsen, A.G. et al. (2011). "Existing data sources for clinical epidemiology: The clinical laboratory information system (LABKA) research database at Aarhus University, Denmark". Clinical Epidemiology 3: 133-8. doi:10.2147/CLEP.S17901. PMID 21487452. 
  6. ↑ "Laboratoriedatabasen". Sundhedsdata-Styrelsen. 2018. https://sundhedsdatastyrelsen.dk/da/registre-og-services/om-de-nationale-sundhedsregistre/doedsaarsager-og-biologisk-materiale/laboratoriedatabasen. Retrieved 12 March 2018. 
  7. ↑ C-SC-NPU of the IFCC and UIPAC et al. (2009). "Clinical laboratory sciences data transmission: the NPU coding system". Studies in Helath Technology and Informatics 150: 265–9. PMID 19745311. 
  8. ↑ Forrey, A.W.; McDonald, C.J.; DeMoor, G. et al. (1996). "Logical observation identifier names and codes (LOINC) database: A public use set of codes and names for electronic reporting of clinical laboratory test results". Clinical Chemistry 42 (1): 81–90. doi:10.1093/clinchem/42.1.81. PMID 8565239. 
  9. ↑ Gradel, K.O.; Schønheyder, H.C.; Arpi, M. et al. (2014). "The Danish Collaborative Bacteraemia Network (DACOBAN) database". Clinical Epidemiology 6: 301–8. doi:10.2147/CLEP.S66998. PMC PMC4172047. PMID 25258557. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4172047. 
  10. ↑ Schønheyder, H.C.; Søgaard, M. (2010). "Existing data sources for clinical epidemiology: The North Denmark Bacteremia Research Database". Clinical Epidemiology 2: 171-8. doi:10.2147/clep.s10139. PMC PMC2943179. PMID 20865114. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2943179. 
  11. ↑ Christiansen, J.U.; Maruard, C.D.; Nielsen, H.C. (1989). "LABKA. A real-time computer system for the clinical laboratory". Scandinavian Journal of Clinical and Laboratory Investigation Supplementum 194: 57–61. PMID 2772556. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, grammar, and spelling. In some cases important information was missing from the references, and that information was added.

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