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Multivariate statistical interpretation of laboratory clinical data

  • Research Article
  • Published:
Central European Journal of Medicine

Abstract

Laboratory aids are extensively used in the diagnosis of diseases, in preventive medicine, and as management tools. Reference values of clinically healthy people serve as a guide to the clinician in evaluating biochemical parameters. Determination of 21 biochemical parameters of healthy persons using standard methods of analysis. Cluster analysis and principal components analysis were applied on the above 21 biochemical parameters data. The application of a typical classification approach as cluster analysis proved that four major groups of similarity between all 21 clinical parameters are formed, which correspond to the authors assumption of the existence of several summarizing pattern of clinical parameters such as “enzyme,” “major component excretion”, “general health state,” and “blood specific” pattern. These patterns appear also in the subsets obtained by separation of the general dataset into “male”, “female”, “young”, and “adult” healthy groups. The results obtained from principal components analysis have additionally proved the validity of a similar assumption. The intelligent data analysis on the clinical parameter dataset has shown that when a complex system is considered as a multivariate one, the information about the system substantially increases. All these results support an idea that probably a general health indicator could be constructed taking into account the existing classification groups in the list of clinical parameters.

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References

  1. IFFC: “Approved recommendation on the theory of reference values. Part 1. The Concept of reference values”, Clin. Chim. Acta, Vol. 165, (1987a), pp. 111–118.

    Article  Google Scholar 

  2. IFFC: “Approved recommendation on the theory of reference values. Part 5. Statistical treatment of collected reference values. Determination of reference limits”, J. Clin. Chem. Biochem., Vol. 25, (1987c), pp. 645–656.

    Google Scholar 

  3. W. Oosterhuis, D. Bruns, J. Watine, S. Sandberg and A. Horvath: “Evidence-Based guidelines in Laboratory Medicine: Principles and methods”, Clin. Chem., Vol. 50, (2004), pp. 806–818.

    Article  PubMed  CAS  Google Scholar 

  4. IFFC: “Approved recommendation on the theory of reference values. Part 6. Presentation of observed values related to reference values”, J. Clin. Chem. Biochem., Vol. 25, (1987d), pp. 657–662.

    Google Scholar 

  5. IFFC: “Approved recommendation on the theory of reference values. Part 3. Preparation of individuals and collection of speciments for the production of reference values”, Clin. Chim. Acta, Vol. 177, (1988), pp. S1–S12.

    Article  Google Scholar 

  6. IFFC: “Approved recommendation on the theory of reference values. Part 2. Selection of individuals for the production of reference values”, J. Clin. Chem. Biochem., Vol. 25, (1987b), pp. 639–644.

    Google Scholar 

  7. E.K. Harris: “Some theory of reference values. I. Stratified (categorized)normal ranges and a method for following an individuals clinical laboratory values”, Clin. Chem., Vol. 21, (1975), pp. 1457–1464.

    PubMed  CAS  Google Scholar 

  8. E.K. Harris, E.T. Wohg and S.T. Shaq: “Statistical criteria for separate reference intervals: race and gender groups in creatine kinase”, Clin. Chem., Vol. 37, (1991), pp. 1580–1582.

    PubMed  CAS  Google Scholar 

  9. H.E. Sölberg and R. Gräsbeck: “Reference values”, Adv. Clin. Chem., Vol. 27, (1989), pp. 2–79.

    Google Scholar 

  10. E. Grossi, R. Colombo, S. Cavuto and C. Franzini: “The REALAB Project: A New Method for the Formulation of Reference Intervals Based on Current Data”, Clin. Chem., Vol. 51, (2005), pp. 1232–1240.

    Article  PubMed  CAS  Google Scholar 

  11. IFFC: “Approved recommendation on the theory of reference values. Part 4. Theory of reference values. Control of analytical variation in the production, transfer and application of reference values”, Clin. Chim. Acta, Vol. 202, (1991), pp. S5–S12.

    Article  Google Scholar 

  12. NCCLS: “How to Define, Determine, and Utilize Reference Intervals in the Clinical Laboratory; Proposed Guideline”, Villanova, Pennsylvania: NCCLS, Vol. 19, (1992), pp. 21, 39-40.

    Google Scholar 

  13. R.R. Grams, E. Johnson and E. Benson: “Laboratory data analysis system — section II — analytic error limits”, Am. J. Clin. Pathol., Vol. 58, (1972), pp. 182–187.

    PubMed  CAS  Google Scholar 

  14. B. Vanderginste, D.L. Massart, L. Buydens, S. De Jong, P. Lewi and J. Smeyers-Verbeke: Handbook of Chemometrics and Qualimetrics, Elsevier, Amsterdam, 1998.

    Google Scholar 

  15. D.L. Massart and L. Kaufman: The Interpretation of analytical chemical data by the Use of Cluster Analysis, J. Wiley, New York, 1983.

    Google Scholar 

  16. P. Winkel: “Patterns and Clusters-Multivariate Approach for Interpreting Clinical Chemistry Results”, Clin. Chem., Vol. 19, (1973), pp. 1329–1338.

    PubMed  CAS  Google Scholar 

  17. R. Grams, D. Lezotte and J. Gudat: “Establishing a Multivariate Clinical Laboratory Data Base”, J. Med. Syst., Vol. 2, (1978), pp. 355–362.

    Article  PubMed  CAS  Google Scholar 

  18. G. Plomteux: “Multivariate Analysis of an Enzymic Profile for the Differential Diagnosis of Viral Hepatitis”, Clin. Chem., Vol. 26, (1980), pp. 1897–1899.

    PubMed  CAS  Google Scholar 

  19. J. Poupard, B. Gagnon, M. Stanhope and C. Stewart: “Methods for Data Mining from Large multinational Surveillance Studies”, Antimicrob. Agents Ch., Vol. 46, (2002), pp. 2409–2419.

    Article  CAS  Google Scholar 

  20. H. Kraemer, J. Measelle, M. Essex, T. Boyce and D. Kupfer: “A New Approach to Integrating Data From Multiple Informants in Psychiatric Assessment and Research: Mixing and Matching Contexts and Perspectives”, Am. J. Psychiatry, Vol. 160, (2003), pp. 1566–1577.

    Article  PubMed  Google Scholar 

  21. G. Rowlands, A. Musoke, S. Morzaria, S. Nagda, K. Ballingall and D. McKeever: “A statistically derived index for classifying East Coast fever reactions in cattle challenged with Theileria parva under experimental conditions”, Parasitology, Vol. 120, (2000), pp. 371–381.

    Article  PubMed  Google Scholar 

  22. S. Skrede, H. Solberg, S. Ritland, J. Blomhoff, E. Schrumpf, K. Elgjo and E. Gjone: “Diagnostic and Prognostic Value of Laboratory Tests Assessed in a Follow-up Study of 200 Patients with Liver Disease”, Clin. Chem., Vol. 28, (1982), pp. 1177–1181.

    PubMed  CAS  Google Scholar 

  23. T. Alström, R. Gräsbeck, M. Hjelm and S. Skandsen: “Recommendations concerning the collection of reference values in clinical chemistry and activity report by the Committee on Reference Values of the Scandinavian Society for Clinical Chemistry and Clinical Physiology”, Scand. J. Clin. Lab. Inv., Vol. 35, (1975), Suppl. 144.

    Google Scholar 

  24. J.M. Slockbower and T.A. Blumenfeld (ed): Collection and handling of Laboratory Speciments, Philadelphic: Lippincott Co; 1983, p. 201.

    Google Scholar 

  25. NCCLS: Procedures for the Collection of Diagnostic Blood Specimens by Venipuncture, NCCLS, Document H3-A3 Wayne, PA: NCCLS; 1991.

    Google Scholar 

  26. NCCLS: Procedures for the Collection of Diagnostic Blood Specimens by Skin Puncture, NCCLS Document H4-A3 Wayne, PA: NCCLS; 1991.

    Google Scholar 

  27. NCCLS: Internal Quality Control Testing: Principles and Definitions, NCCLS, Document C24-A Wayne, PA: NCCLS; 1991.

    Google Scholar 

  28. M.T. Kafka: “Internal quality control, proficiency testing and the clinical relevance of laboratory testing”, Arch. Pathol. Lab. Med., Vol. 112, (1988), pp. 449–453.

    PubMed  CAS  Google Scholar 

  29. S. Deming: “Chemometrics: an Overview”, Clin. Chem., Vol. 32, (1986), pp. 1702–1706.

    PubMed  CAS  Google Scholar 

  30. A. Schoots, J. Dijkstra, S. Ringoir, R. Vanholder and C. Cramers: “Are the Classical Markers Sufficient to Describe Uremic Solute Accumulation in Dialyzed Patients? Hippurates Reconsidered”, Clin. Chem., Vol. 34, (1988), pp. 1022–1029.

    PubMed  CAS  Google Scholar 

  31. W. Vogt, D. Nagel: “Cluster Analysis in Diagnosis”, Clin. Chem., Vol. 38, (1992), pp. 182–198.

    PubMed  CAS  Google Scholar 

  32. S. Bruehl, K. Lofland, E. Semenchuk, L. Rokicki and D. Penzien: “Use of Cluster Analysis to Validate HIS Diagnostic Criteria for Migraine and Tension-Type Headache”, Headache, Vol. 39, (1999), pp. 181–189.

    Article  PubMed  CAS  Google Scholar 

  33. J. Gottfries, K. Blennow, M. Lehmann, B. Regland and C. Gottfries: “One-Carbon Metabolism and Other Biochemical Correlates of Cognitive Impairment as Visualized by Principal Component Analysis”, J. Geriatr. Psych. Neur., Vol. 14, (2001), pp. 109–114.

    Article  CAS  Google Scholar 

  34. H. Nguyen, J. Altinger, V. Carrieri-Kohlman, J. Gormley, S. Paul and M. Stulbarg: “Factor Analysis of Laboratory and Clinical Measurements of Dyspnea in Patients with Chronic Obstructive Pulmonary Disease”, J. Pain. Symptom. Manag., Vol. 25, (2003), pp. 118–127.

    Article  Google Scholar 

  35. T. Thireou, L. Strauss, A. Dimitrakopoulou-Strauss, G. Kontaxakis, S. Pavlopoulos and A. Santos: “Performance evaluation of principal component analysis in dynamic FDG-PET studies of recurrent colorectal cancer”, Comput. Med. Imag. Grap., Vol. 27, (2003), pp. 43–51.

    Article  Google Scholar 

  36. A. Agarwal, R. Sharma and D. Nelson: “New Semen Quality Scores Developed by Principal Component Analysis of Semen Characteristics”, J. Androl., Vol. 24, (2003), pp. 343–351.

    PubMed  Google Scholar 

  37. K. Coombes, H. Fritsche, C. Clarke, J. Chen, K. Baggerly and J. Morris: “Quality Control and Peak Finding for Proteomics Data Collected from Nipple Aspirate Fluid by Surface-Enhanced Laser Desorption and ionization”, Clin. Chem., Vol. 49, (2003), pp. 1615–1623.

    Article  PubMed  CAS  Google Scholar 

  38. D. Clinton, E. Button, C. Norring and R. Palmer: “Cluster analysis of key diagnostic variables from two independent samples of eating-disorder patients: evidence for a consistent pattern”, Psychol. Med., Vol. 34, (2004), pp. 1035–1045.

    Article  PubMed  Google Scholar 

  39. M. Kesek, T. Jernberg, B. Lindahl, J. Xue and A. Englund: “Principal Component Analysis of the T Wave in Patients with Chest Pain and Conduction Disturbances”, Pace, Vol. 27, (2004), pp. 1378–1387.

    PubMed  Google Scholar 

  40. E. Juniper, M. Wisniewski, F. Cox, A. Emmett, K. Nielsen and P. O’Byrne: “Relationship between quality of life and clinical status in asthma: a factor analysis”, Eur. Respir. J., Vol. 23, (2004), pp. 287–291.

    Article  PubMed  CAS  Google Scholar 

  41. C. Lochner, S. Hemmings, C. Kinnear and D. Niehaus: “Cluster analysis of obsessive-compulsive spectrum disorders in patients with obsessive-compulsive disorder: clinical and genetic correlates”, Compr. Psychiat., Vol. 46, (2005), pp. 14–19.

    Article  PubMed  Google Scholar 

  42. R. Ness, K. Kip, S. Hillier, D. Soper, C. Stamm, R. Sweet, P. Rice and H. Richter: “A Cluster Analysis of Bacterial Vaginosis-associated Microflora and Pelvic Inflammatory Disease”, Am. J. Epidemiol., Vol. 162, (2005), pp. 585–590.

    Article  PubMed  Google Scholar 

  43. B. Shields, B. Knight, R. Powell, A. Hattersley and D. Wright: “Assessing newborn body composition using principal components analysis: differences in the determinants of fat and skeletal size”, BMC Pediatrics, Vol. 6, (2006), p. 24.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Medical Laboratories, Education & Technological Institute of Larissa, 41110, Larissa, Greece

    Agelos Papaioannou, Panagiotis Plageras & Eleni Dovriki

  2. Analytical Chemistry, Faculty of Chemistry, University of Sofia, “St. Kl. Okhridski”, 1164, Sofia, Bulgaria

    Vasili Simeonov

  3. Chemistry Department of Natural Sciences, Technical Educational Institution of Kavala, 654 04, Kavala, Greece

    Thomas Spanos

Authors
  1. Agelos Papaioannou
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  2. Vasili Simeonov
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  3. Panagiotis Plageras
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  4. Eleni Dovriki
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  5. Thomas Spanos
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Papaioannou, A., Simeonov, V., Plageras, P. et al. Multivariate statistical interpretation of laboratory clinical data. cent.eur.j.med 2, 319–334 (2007). https://doi.org/10.2478/s11536-007-0035-1

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  • DOI: https://doi.org/10.2478/s11536-007-0035-1

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