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Personalized laboratory medicine in the digital health era: recent developments and future challenges

  • Abdurrahman Coskun ORCID logo EMAIL logo and Giuseppe Lippi ORCID logo
Published/Copyright: September 28, 2023
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 62 Issue 3

Abstract

Interpretation of laboratory data is a comparative procedure and requires reliable reference data, which are mostly derived from population data but used for individuals in conventional laboratory medicine. Using population data as a “reference” for individuals has generated several problems related to diagnosing, monitoring, and treating single individuals. This issue can be resolved by using data from individuals’ repeated samples, as their personal reference, thus needing that laboratory data be personalized. The modern laboratory information system (LIS) can store the results of repeated measurements from millions of individuals. These data can then be analyzed to generate a variety of personalized reference data sets for numerous comparisons. In this manuscript, we redefine the term “personalized laboratory medicine” as the practices based on individual-specific samples and data. These reflect their unique biological characteristics, encompassing omics data, clinical chemistry, endocrinology, hematology, coagulation, and within-person biological variation of all laboratory data. It also includes information about individuals’ health behavior, chronotypes, and all statistical algorithms used to make precise decisions. This approach facilitates more accurate diagnosis, monitoring, and treatment of diseases for each individual. Furthermore, we explore recent advancements and future challenges of personalized laboratory medicine in the context of the digital health era.

Keywords: artificial intelligence; big data; chronobiology; personalized laboratory medicine
Corresponding author: Abdurrahman Coskun, Department of Medical Biochemistry, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Kayisdagi cad., No: 32, 34752 Atasehir – Istanbul, Türkiye, Phone: +90 216 5004960, E-mail:

Acknowledgments

The English of some sentences in this manuscript was edited by ChatGPT.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

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

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: None declared.

  6. Data availability: Not applicable.

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Received: 2023-07-28
Accepted: 2023-09-18
Published Online: 2023-09-28
Published in Print: 2024-02-26

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2023-0808
Keywords for this article
artificial intelligence; big data; chronobiology; personalized laboratory medicine

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Unraveling the mystery of blood groups and COVID-19
  4. Reviews
  5. Serum hepcidin levels in chronic liver disease: a systematic review and meta-analysis
  6. Platelet distribution width (PDW) as a significant correlate of COVID-19 infection severity and mortality
  7. Mini Reviews
  8. ABO blood group-related mechanism of infection of SARS-CoV-2: an overview of systematic reviews
  9. Opinion Paper
  10. Personalized laboratory medicine in the digital health era: recent developments and future challenges
  11. Guidelines and Recommendations
  12. Algorithm of differential diagnosis of anemia involving laboratory medicine specialists to advance diagnostic excellence
  13. General Clinical Chemistry and Laboratory Medicine
  14. Rescaling creatinine makes GFR estimation equations generally applicable across populations – validation results for the Lund-Malmö equation in a French cohort of sub-Saharan ancestry
  15. Periodic verification of results’ comparability between several analyzers: experience in the application of the EP31-A-IR guideline
  16. Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction
  17. Diagnostic performance of automated red cell parameters in predicting bone marrow iron stores
  18. Reliability of hemoglobin A2 value as measured by the Premier Resolution system for screening of β-thalassemia carriers
  19. Amino acid sequence homology of monoclonal serum free light chain dimers and tissue deposited light chains in AL amyloidosis: a pilot study
  20. Development of high-performance point-of-care aqueous VEGF detection system and proof-of-concept validation in RVO patients
  21. Detection rate of IGF-1 variants and their implication to protein binding: study of over 240,000 patients
  22. Analysis of a second-tier test panel in dried blood spot samples using liquid chromatography-tandem mass spectrometry in Catalonia’s newborn screening programme
  23. Targeted quantitative lipidomic uncovers lipid biomarkers for predicting the presence of compensated cirrhosis and discriminating decompensated cirrhosis from compensated cirrhosis
  24. Reference Values and Biological Variations
  25. Establishment of reference intervals for free light chains and immunoglobulins in Saudi population
  26. Cancer Diagnostics
  27. A predictive and prognostic model for surgical outcome and prognosis in ovarian cancer computed by clinico-pathological and serological parameters (CA125, HE4, mesothelin)
  28. M-protein diagnostics in multiple myeloma patients using ultra-sensitive targeted mass spectrometry and an off-the-shelf calibrator
  29. Cardiovascular Diseases
  30. Bioactive adrenomedullin (bio-ADM) is associated with endothelial dysfunction in infants and children with complex congenital heart disease undergoing open-heart surgery on cardiopulmonary bypass
  31. Infectious Diseases
  32. Monocyte distribution width as an early predictor of short-term outcome in adult patients with sepsis
  33. Analytical and clinical evaluations of SNIBE Maglumi chemiluminescent immunoassay for the detection of SARS-CoV-2 antigen in salivary samples
  34. Letters to the Editor
  35. Predicting hemoglobinopathies using ChatGPT
  36. Managing the Quality Control of multiple instruments
  37. Lipid droplets may interfere with urinary red blood cell and crystal counts by urinary flow cytometry
  38. Assessment of the lipemia index determined by the Atellica CH 930 analyzer for the detection of monoclonal immunoglobulins
  39. Concerning quality demands of arterial partial pressure of oxygen
  40. Navigating between perpendicular drop and tangent skimming methods for M-protein quantification: a call for clarification of guidelines
  41. Early detection of peripheral invasive candidiasis further to cytographic interferences in Sysmex XN-9000 hematology analyzer
  42. Congress Abstracts
  43. Annual meeting of the Royal Belgian Society of Laboratory Medicine: “Symphony of the Heart”
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