Advertisement

Klinische Wochenschrift

, Volume 66, Issue 1, pp 32–36 | Cite as

Noninvasive diagnosis of renal allograft rejections — application of an information-theoretical model

  • H. Heiss
  • W. Wild
  • R. Margreiter
  • W. Pfaller
  • P. Kotanko
Originalien

Summary

This paper describes an information-theoretical model developed for detection of renal allograft rejection on the basis of various laboratory data. In this report the mathematical background of the model is described in detail and the rationale of its use is discussed. An example is given for the practical application of the model in kidney grafted patients. In the 30 patients of the test collective, seven rejection episodes were diagnosed by the clinicians and vertified histologically. All seven rejection episodes were detected by the model, in the mean 2.4 days (median; 3 days) before the clinical diagnosis.

Key words

Renal transplantation Medical decision making Information theory 

Abbreviatons

CsA

cyclosporine

EC

enzyme commission

FBP

fructose-1,6-bisphosphatase

FN

false negative

FP

false positive

HPLC

high performance liquid chromatography

nr

no rejection

PCr

plasma creatinine

r

rejection

TN

true negative

TP

true positive

UNeo

urinary neopterin

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Büttner J (1977) Die Beurteilung des diagnostischen Wertes klinisch-chemischer Untersuchungen. J Clin Chem Clin Biochem 15:1–12Google Scholar
  2. 2.
    Delong ER, Vernon WB, Bollinger RR (1985) Sensitivity and specificity of a monitoring test. Biometrics 41:947–958Google Scholar
  3. 3.
    Diamond GA, Hirsch M, Forrester JS, Staniloff HM, Vas R, Halpern SW, Swan HJC (1982) Application of information theory to clinical diagnostic testing. Circulation 63:915–921Google Scholar
  4. 4.
    Galen RS (1983) The predictive value of laboratory diagnosis. Bull Mol Biol Med 8:159–169Google Scholar
  5. 5.
    Hausen A, Fuchs D, König K, Wachter H (1982) Determination of neopterin in human urine by reversed-phase high performance liquid chromatography. J Chromatogr 227:61–64Google Scholar
  6. 6.
    Howard RJ (1986) Definition, diagnosis and treatment of acute kidney rejection — the first 30 days. Transpl Proc, [Suppl 1] 18:92–100Google Scholar
  7. 7.
    Jung K, Scholz D, Diego J, May G (1983) Harnenzymausscheidung bei nierentransplantierten Patienten Z Ges Inn Med 38:581–592Google Scholar
  8. 8.
    Jung K, Diego J, Strobelt V (1985) Diagnostic significance of urinary enzymes in detecting acute rejection crisis in renal transplant recipients depending on expression of results illustrated through the example of alanine aminopeptidase. Clin Biochem 18:257–260Google Scholar
  9. 9.
    Kotanko P, Gstraunthaler G, Pfaller W (1984) Urinary enzymes for the non-invasive diagnosis of epithelial damage in acute renal failure. Wiener Klin Wochenschr 96:625–629Google Scholar
  10. 10.
    Kotanko P, Keiler R, Knabl L, Aulitzky W, Margreiter R, Gstraunthaler G, Pfaller W (1986) Urinary enzyme analysis in renal allograft transplantation. Clin Chim Acta 160:137–144Google Scholar
  11. 11.
    Latzko E, Gibbs M (1974) “Alkalische” Fructose-1,6-diphosphatase. In: Bergmeyer HU (ed) Methoden der enzymatischen Analyse. Verlag Chemie, Weinheim, S 914–917Google Scholar
  12. 12.
    Loertscher R, Scholer A, Brunner F, Harder F, Thiel G (1982) Klinische Relevanz der N-Acetyl-Glucosaminidase-Bestimmung im Urin bei Nierentransplantatempfängern mit und ohne Cyclosporin A. Schweiz Med Wochenschr 112:1658–1664Google Scholar
  13. 13.
    Margreiter R, Fuchs D, Hausen A, Huber C, Reibnegger G, Spielberger M, Wachter H (1983) Neopterin as a new biochemical marker for diagnosis of allograft rejection. Transplantation 36:650–653Google Scholar
  14. 14.
    Price RG (1982) Urinary enzymes, nephrotoxicity and renal disease. Toxicology 23:99–134Google Scholar
  15. 15.
    Shannon CE, Weaver W (1949) The mathematical theory of communication. Urbana. University of Illinois PressGoogle Scholar
  16. 16.
    Trimble IM, West M, Knapp MS, Pownall R, Smith AFM (1983) Detection of renal allograft rejection by computer. Br Med J 286:1695–1699Google Scholar
  17. 17.
    Whiting PH, Nicholls AJ, Catto GRD, Edward N, Engeset J (1980) Patterns of N-acetyl-β-D-glucosaminidase excretion after renal transplantation. Clin Chim Acta 108:1–7Google Scholar
  18. 18.
    Whiting PH, Petersen J, Power DA, Stewart RDM, Catto GRD, Edward N (1983) Diagnostic value of urinary N-acetyl-β-D-glucosaminidase, its isoenzymes and the fractional excretion of sodium following renal transplantation. Clin Chim Acta 130:369–376Google Scholar
  19. 19.
    Wild W, Heiss H, Kotanko P (1984) Die computergestützte Auswertung zur Erkennung von Nierentransplantatabstoßungen unter Verwendung des Kanalmodells der Informationstheorie. In: Gell G, Eichtinger Ch (eds) Medizinische Informatik '84 R. Oldenburg, Wien München, S 45–48Google Scholar
  20. 20.
    Wolf J, Musch E, Neuss H, Klehr U (1987) Neopterin im Serum und Urin zur Differentialdiagnose von Nierenfunktions-Störungen nach Nierentransplantation. Klin Wochenschr 65:225–231Google Scholar

Copyright information

© Springer-Verlag 1988

Authors and Affiliations

  • H. Heiss
    • 1
  • W. Wild
    • 1
  • R. Margreiter
    • 1
  • W. Pfaller
    • 1
  • P. Kotanko
    • 1
  1. 1.Institut für Physiologie und I. Universitäts-Klinik für Chirurgie, Abteilung TransplantationschirurgieUniversität InnsbruckÖsterreich

Personalised recommendations