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Number of results

Journal

2007 | 2 | 3 | 319-334

Article title

Multivariate statistical interpretation of laboratory clinical data

Content

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Languages of publication

EN

Abstracts

EN
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.

Publisher

Journal

Year

Volume

2

Issue

3

Pages

319-334

Physical description

Dates

published
1 - 9 - 2007
online
1 - 9 - 2007

Contributors

  • Department of Medical Laboratories, Education & Technological Institute of Larissa, 41110, Larissa, Greece
  • Analytical Chemistry, Faculty of Chemistry, University of Sofia, “St. Kl. Okhridski”, 1164, Sofia, Bulgaria
  • Department of Medical Laboratories, Education & Technological Institute of Larissa, 41110, Larissa, Greece
author
  • Department of Medical Laboratories, Education & Technological Institute of Larissa, 41110, Larissa, Greece
author
  • Chemistry Department of Natural Sciences, Technical Educational Institution of Kavala, 654 04, Kavala, Greece

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. http://dx.doi.org/10.1016/0009-8981(87)90224-5[Crossref]
  • [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.
  • [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. http://dx.doi.org/10.1373/clinchem.2003.025528[Crossref]
  • [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.
  • [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. http://dx.doi.org/10.1016/0009-8981(88)90301-4[Crossref]
  • [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.
  • [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]
  • [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]
  • [9] H.E. Sölberg and R. Gräsbeck: “Reference values”, Adv. Clin. Chem., Vol. 27, (1989), pp. 2–79.
  • [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. http://dx.doi.org/10.1373/clinchem.2005.047787[Crossref]
  • [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. http://dx.doi.org/10.1016/0009-8981(91)90266-F[Crossref]
  • [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.
  • [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.
  • [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.
  • [15] D.L. Massart and L. Kaufman: The Interpretation of analytical chemical data by the Use of Cluster Analysis, J. Wiley, New York, 1983.
  • [16] P. Winkel: “Patterns and Clusters-Multivariate Approach for Interpreting Clinical Chemistry Results”, Clin. Chem., Vol. 19, (1973), pp. 1329–1338.
  • [17] R. Grams, D. Lezotte and J. Gudat: “Establishing a Multivariate Clinical Laboratory Data Base”, J. Med. Syst., Vol. 2, (1978), pp. 355–362. http://dx.doi.org/10.1007/BF02221901[Crossref]
  • [18] G. Plomteux: “Multivariate Analysis of an Enzymic Profile for the Differential Diagnosis of Viral Hepatitis”, Clin. Chem., Vol. 26, (1980), pp. 1897–1899.
  • [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. http://dx.doi.org/10.1128/AAC.46.8.2409-2419.2002[Crossref]
  • [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. http://dx.doi.org/10.1176/appi.ajp.160.9.1566[Crossref]
  • [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. http://dx.doi.org/10.1017/S0031182099005600[Crossref]
  • [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.
  • [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.
  • [24] J.M. Slockbower and T.A. Blumenfeld (ed): Collection and handling of Laboratory Speciments, Philadelphic: Lippincott Co; 1983, p. 201.
  • [25] NCCLS: Procedures for the Collection of Diagnostic Blood Specimens by Venipuncture, NCCLS, Document H3-A3 Wayne, PA: NCCLS; 1991.
  • [26] NCCLS: Procedures for the Collection of Diagnostic Blood Specimens by Skin Puncture, NCCLS Document H4-A3 Wayne, PA: NCCLS; 1991.
  • [27] NCCLS: Internal Quality Control Testing: Principles and Definitions, NCCLS, Document C24-A Wayne, PA: NCCLS; 1991.
  • [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.
  • [29] S. Deming: “Chemometrics: an Overview”, Clin. Chem., Vol. 32, (1986), pp. 1702–1706.
  • [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.
  • [31] W. Vogt, D. Nagel: “Cluster Analysis in Diagnosis”, Clin. Chem., Vol. 38, (1992), pp. 182–198.
  • [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. http://dx.doi.org/10.1046/j.1526-4610.1999.3903181.x[Crossref]
  • [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. http://dx.doi.org/10.1177/089198870101400302[Crossref]
  • [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. http://dx.doi.org/10.1016/S0885-3924(02)00690-5[Crossref]
  • [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. http://dx.doi.org/10.1016/S0895-6111(02)00050-2[Crossref]
  • [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.
  • [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. http://dx.doi.org/10.1373/49.10.1615[Crossref]
  • [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. http://dx.doi.org/10.1017/S0033291703001508[Crossref]
  • [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. [Crossref]
  • [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. http://dx.doi.org/10.1183/09031936.04.00064204[Crossref]
  • [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. http://dx.doi.org/10.1016/j.comppsych.2004.07.020[Crossref]
  • [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. http://dx.doi.org/10.1093/aje/kwi243[Crossref]
  • [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. http://dx.doi.org/10.1186/1471-2431-6-24[Crossref]

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.-psjd-doi-10_2478_s11536-007-0035-1
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