Laboratory diagnostics in medicine

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Abstract


The development of clinical laboratory diagnostics is in line with the evidence-based medicine, which requires that clinical decisions have to be based on diagnostic methods with proven informativity. This creates a request for the scientific validity of the use of laboratory researches and application of probabilistic interpretation tools corresponding to the tasks. The concept of indefiniteness (analytical, biological and clinical) is at the heart of interpretation of laboratory results. The inclusion of laboratory research in clinical guidelines, the choice and appointment of this research to the patient should not be made from the position of ideas about increasing or decreasing the laboratory index in the disease, but on the basis of its scientifically proven characteristics as a laboratory biomarker – sensitivity, specificity, predictive value, as well as the relationship with certain clinical events, outcomes, risks. These characteristics are probabilistic and can be defined.


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A. G. Kochetov

National Medical Research Center for Cardiology; Institute of Laboratory Medicine; People’s Friendship University of Russia

Author for correspondence.
Email: ag_kochetov@dpo-ilm.ru
ORCID iD: 0000-0003-3632-291X

Russian Federation, Moscow

рук. отд. лабораторной диагностики; ректор; д.м.н., проф. каф. госпитальной терапии с курсом клинической лабораторной диагностики

O. V. Lyang

Institute of Laboratory Medicine; People’s Friendship University of Russia; Federal Center for Cerebrovascular Pathology and Stroke

Email: ag_kochetov@dpo-ilm.ru
ORCID iD: 0000-0002-1023-5490

Russian Federation, Moscow

проректор по учебной работе; д.м.н., доц. каф. госпитальной терапии с курсом клинической лабораторной диагностики; зав. отд-нием клинической лабораторной диагностики

I. A. Zhirova

People’s Friendship University of Russia

Email: ag_kochetov@dpo-ilm.ru
ORCID iD: 0000-0002-6621-2052

Russian Federation, Moscow

к.м.н., доц. каф. госпитальной терапии с курсом клинической лабораторной диагностики

O. O. Ivoilov

National Medical Research Center for Cardiology; People’s Friendship University of Russia

Email: ag_kochetov@dpo-ilm.ru
ORCID iD: 0000-0002-4684-8440

Russian Federation, Moscow

к.м.н., ст. науч. сотр. отд. лабораторной диагностики; ассистент каф. госпитальной терапии с курсом клинической лабораторной диагностики

References

  1. CLSI. Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory; Approved Guideline – Third Edition. CLSI document EP28-A3c. Wayne, PA: Clinical and Laboratory Standards Institute; 2008;28(30). https://clsi.org/standards/products/method-evaluation/documents/ep28/
  2. Risch M, Nydegger U, Risch L. SENIORLAB: a prospective observational study investigating laboratory parameters and their reference intervals in the elderly. Medicine (Baltimore). 2017;96(1):e5726. doi: 10.1097/MD.0000000000005726
  3. Ozarda, Y. Reference intervals: current status, recent developments and future considerations. Biochem Med. 2016;26(1):5-11. doi: 10.11613/ BM.2016.001
  4. Ichihara K, Ozarda Y, Barth JH, et al. A global multicenter study on reference values: 1. Assessment of methods for derivation and comparison of reference intervals. Clin Chim Acta. 2017;467:70-82. doi: 10.1016/j.cca.2016.09.016
  5. Ichihara K, Ozarda Y, Barth JH, et al. A global multicenter study on reference values: 2. Exploration of sources of variation across the countries. Clin Chim Acta. 2017;467:83-97. doi: 10.1016/j.cca.2016.09.015
  6. ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. European Heart Journal. 2016;(37):267-315. doi: 10.1093/eurheartj/ehv320
  7. Ozarda Y, Sikaris K, Streichert T. Distinguishing reference intervals and clinical decision limits – A review by the IFCC Committee on Reference Intervals and Decision Limits. Crit Rev Clin Lab Sci. 2018;55(6):420-31. doi: 10.1080/10408363.2018.1482256
  8. Braga F, Panteghini M. Generation of data on within-subject biological variation in laboratory medicine: an update. Crit Rev Clin Lab Sci. 2016;53(5):313-25. doi: 10.3109/10408363.2016
  9. Lund F, Petersen PH, Fraser CG. A dynamic reference change value model applied to ongoing assessment of the steady state of a biomarker using more than two serial results. Ann Clin Biochem. 2019;56(2):283-94. doi: 10.1177/0004563219826168
  10. Garner A, Lewington A, Barth J. Detection of patients with acute kidney injury by the clinical laboratory using rises in serum creatinine: comparison of proposed definitions and a laboratory delta check. Ann Clin Biochem. 2012;49:59-62. doi: 10.1258/acb.2011.011125
  11. NIH Biomarkers Definitions Working Group Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69:89-95. doi: 10.1067/mcp.2001.113989
  12. Дон Е.С., Тарасов А.В., Эпштейн О.И., Тарасов С.А. Биомаркеры в медицине: поиск, выбор, изучение и валидация. Клиническая лабораторная диагностика. 2017;62(1):52-9 [Don ES, Tarasov AV, Epshtein OI, Tarasov SA. The biomarkers in medicine: search, choice, study and validation. Klinicheskaja laboratornaja diagnostika. 2017;62(1):52-9 (In Russ.)]. doi: 10.18821/0869-2084-2017-62-1-52-59
  13. Осипова Т.В., Бухман В.М. Биомаркеры трансляционной медицины. Российский биотерапевтический журнал. 2018;17(1):6-13. [Osipova TV, Bukhman VM. Biomarkers of translational medicine. Rossijskij bioterapevticheskij zhurnal. 2018;17(1):6-13 (In Russ.)]. doi: 10.17650/1726-9784-2018-17-1-6-13
  14. Ansari D, Aronsson L, Sasor A, et al. The role of quantitative mass-spectrometry in the discovery of pancreatic cancer biomarkers for translational science. J Transl Med. 2014;12:87. doi: 10.1186/1479-5876-12-87
  15. Dieterle F, Sistare F, Goodsaid F, et al. Renal biomarker qualification submission: a dialog between the FDA-EMEA and Predictive Safety Testing Consortium. Nat Biotechnol. 2010;5:455-62. doi: 10.1038/nbt.1625
  16. McDermott JE, Wang J, Mitchell H, et al. Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data. Expert Opin Med Diagn. 2013;1:37-51. doi: 10.1517/17530059.2012.718329
  17. Allinson JL. Clinical biomarker validation. Bioanalysis. 2018;12:957-68. doi: 10.4155/bio-2018-0061
  18. Beger RD, Bhattacharyya S, Yang X, et al. Translational biomarkers of acetaminophen-induced acute liver injury. Arch Toxicol. 2015;9:1497-522. doi: 10.1007/s00204-015-1519-4
  19. Antoranz A, Sakellaropoulos T, Saez-Rodriguez J, Alexopoulos LG. Mechanism-based biomarker discovery. Drug Discov Today. 2017;8:1209-15. doi: 10.1016/j.drudis.2017.04.013
  20. Супильников А.А., Шатохина С.Н., Нуждин Е.В. и др. Изучение закономерностей распределения химических элементов в твердофазных структурах сыворотки крови человека и экспериментальных животных по данным рентгеноспектрального микроанализа. Вестник медицинского института «Реавиз»: реабилитация, врач и здоровье. 2016;4(24):84-94 [Supilnikov AA, Shatohina SN, Nuzhdin EV, et al. Study of regularities of distribution of chemical elements in solid structures of human and experimental animals serum according to x-ray microanalysis. Vestnik medicinskogo instituta “Reaviz”: reabilitacija, vrach i zdorov’e. 2016;4(24):84-94 (In Russ.)].
  21. Borchers CH, Parker CE. Improving the biomarker pipeline. Clin Chem. 2010;12:1786-8. doi: 10.1373/clinchem.2010.155705
  22. Witkowska HE, Hall SC, Fisher SJ. Breaking the bottleneck in the protein biomarker pipeline. Clin Chem. 2012;2:321-3. doi: 10.1373/clinchem.2011.175034
  23. Guo F, Capaldi D, Kirby M, et al. Development of a pulmonary imaging biomarker pipeline for phenotyping of chronic lung disease. J Med Imag. 2018;5(2):026002. doi: 10.1117/1.JMI.5.2.026002
  24. Ioannidis JPA, Bossuyt PMM. Waste, Leaks, and Failures in the Biomarker Pipeline. Clin Chem. 2017;5:963-72. doi: 10.1373/clinchem.2016.254649
  25. Kao WT, Chang CL, Lung FW. 5-HTT mRNA level as a potential biomarker of treatment response in patients with major depression in a clinical trial. J Affect Dis. 2018;238:597-608. doi: 10.1016/j.jad. 2018.06.035
  26. Zhang J, Han X, Gao C, et al. 5-Hydroxymethylome in Circulating Cell-free DNA as A Potential Biomarker for Non-small-cell Lung Cancer. Genom Proteom Bioinform. 2018;3:187-99. doi: 10.1016/j.gpb. 2018.06.002
  27. Xiao K, Su L, Yan P, et al. α-1-Acid glycoprotein as a biomarker for the early diagnosis and monitoring the prognosis of sepsis. Crit Care. 2015;4:744-51. doi: 10.1016/j.jcrc.2015.04.007
  28. Harel E, Shoji J, Abraham V, et al. Identifying a potential biomarker for primary focal segmental glomerulosclerosis and its association with recurrence after transplantation. Clin Transplant. 2019;28:e13487. doi: 10.1111/ctr.13487

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