Advertisement

Scoresysteme bei Sepsis und ihre Wertigkeit für die Stratifizierung von Patienten mit Gerinnungsstörungen und Sepsis

  • G. Deutschinoff
  • C. Friedrich
  • R. Markgraf
  • T. Scholten
Conference paper

Zusammenfassung

In verschiedenen Bereichen der Medizin werden Scoresysteme seit längerem zur quantitativen Erfassung von Befunden eingesetzt. So dient z. B. der Apgar-Score der Beurteilung der Vitalität des Neugeborenen, der Glasgow-Coma-Score wird zur Abschätzung des Schweregrades einer Bewusstlosigkeit herangezogen.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. 1.
    Angus DC (1998) Discourse on method: Measuring the value of new therapies in intensive care. In: Vincent JL (ed) Yearbook of intensive care and emergency medicine. Springer, Berlin Heidelberg New York Tokyo, p 263–79.Google Scholar
  2. 2.
    Angus DC, Schmitz RJ (2000) Current and projected workforce requirements for care of the critically ill and patients with pulmonary disease — Can we meet the requirements of an aging population? JAMA 284: 2762–2770PubMedCrossRefGoogle Scholar
  3. 3.
    Bastos PG, Sun X, Wagner DP, Knaus WA, Zimmermann JE (1996) Application of the APACHE III prognostic system in Brazilian intensive care units: A prospective multi-center study. Intensive Care Med 22 (6): 564–570PubMedCrossRefGoogle Scholar
  4. 4.
    Carlet J, Montuclard L, Garrouste-Oregas M (2001) Disaggregating data: From groups to individuals. In: Sibbald WJ (ed) Evaluating critical care. Update in intensive care and emergency medicine, vol 35. Springer, Berlin Heidelberg New York Tokyo, p 309–320Google Scholar
  5. 5.
    Cohen J, Guyatt G, Bernard GR et al. (2001) New strategies for clinical trials in patients with sepsis and septic shock. Crit Care Med 29 (4): 880–885PubMedCrossRefGoogle Scholar
  6. 6.
    Dragstedt L, Jorgensen J, Jensen NH (1989) Interhospital comparisons of patients outcome from intensive care: Importance of lead time bias. Crit Care Med 17: 418–422Google Scholar
  7. 7.
    Escare JJ, Kelley MA (1990) Admission source to the medical intensive care unit predicts hospital death independent of the APACHE II score. JAMA 264: 2389–2394CrossRefGoogle Scholar
  8. 8.
    Fery-Lemonnier E, Landais P, Loirat P, Kleinknecht D, Brivet F (1995) Evaluation of severity scoring systems in ICUs-translation, conversion and definition ambiguities as a source of inter-observer variability in Apache II, SAPS and OSF. Intensive Care Med 21 (4): 356–360PubMedCrossRefGoogle Scholar
  9. 9.
    Friedland JS, Porter JC, Daryanani S et al. (1996) Plasma proinflammatory cytokine concentrations, Acute Physiology and Chronic Health Evaluation (APACHE) III scores and survival in patients in an intensive care unit. Crit Care Med 24 (11): 1775–1781PubMedCrossRefGoogle Scholar
  10. 10.
    Hadorn DC, Keeler EB, Rogers WH (1993) Assessing the performance of mortality prediction models. Rand, Santa MonicaGoogle Scholar
  11. 11.
    Hosmer DJ, Lemeshow S (1989) Model-building strategies and methods for logistic regression. In: Hosmer DJ, Lemeshow S (eds) Applied logistic regression. John Wiley & Sons, New York, p 82–134Google Scholar
  12. 12.
    Knaus WA, Draper EA, Wagner DP (1989) The development of APACHE III, Crit Care Med 17 (Suppl 12): 169–219CrossRefGoogle Scholar
  13. 13.
    Knaus WA, Harreil FE, Fisher CJ Jr et al. (1993) The clinical evaluation of new drugs for sepsis. A prospective study design based on survival analysis. JAMA 270 (10): 1233–1241PubMedCrossRefGoogle Scholar
  14. 14.
    Knaus WA, Sun X, Nystrom PO, Wagner DP (1992) Evaluation of definitions for sepsis. Chest 101 (6): 1656–1662PubMedCrossRefGoogle Scholar
  15. 15.
    Knaus WA, Wagner DP, Draper EA et al. (1991) The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 100 (6): 1619–1636PubMedCrossRefGoogle Scholar
  16. 16.
    Knaus WA, Wagner DP, Zimmermann JE, Draper EA (1993) Variations in mortality and length of stay in intensive care units. Ann Intern Med 118 (10): 753–761PubMedGoogle Scholar
  17. 17.
    Lamb FJ, Rhodes A, Bennett ED (1997) Can intensive care units be compared? In: Vincent JL (ed) Yearbook of intensive care and emergency medicine. Springer, Berlin Heidelberg New York Tokyo, p 896–905Google Scholar
  18. 18.
    Le Gall JR, Lemeshow S, Leleu G et al. (1995) Customized probability models for early severe sepsis in adult intensive care patients. JAMA 273 (8): 644–650PubMedCrossRefGoogle Scholar
  19. 19.
    Lemeshow S (1988) Refining intensive care unit outcome prediction by using changing probablities of mortality. Crit Care Med 16 (5): 470–477PubMedCrossRefGoogle Scholar
  20. 20.
    Markgraf R, Deutschinoff G, Pientka L, Scholten T (2000) Comparison of acute physiology and chronic health evaluations II and III and simplified acute physiology score II: A prospective cohort study evaluating these methods to predict outcome in a German interdisciplinary intensive care unit. Crit Care Med 28 (1): 26–33PubMedCrossRefGoogle Scholar
  21. 21.
    Markgraf R, Deutschinoff G, Pientka L, Scholten T, Lorenz C (2001) Performance of the score systems Acute Physiology and Chronic Health Evaluation. Crit Care 5 (1): 31–36PubMedCrossRefGoogle Scholar
  22. 22.
    McLauchlan GH, Anderson ID, Grant IS, Pearson KCH (1995) Outcome of patients with abdominal sepsis treated in an intensive care unit. Br J Surg 82: 524–529PubMedCrossRefGoogle Scholar
  23. 23.
    Metz CE (1978) Basic principles of ROC analysis. Sem Nuc Med 8 (4): 283–298CrossRefGoogle Scholar
  24. 24.
    Moreno R, Apolone G (1997) Impact of different customization strategies in the performance of a general severity score. Crit Care Med 25 (12): 2001–2008PubMedCrossRefGoogle Scholar
  25. 25.
    Moreno R, Apolone G, Miranda DR (1998) Evaluation of the uniformity of fit of general outcome prediction models. Intensive Care Med 24: 40–47PubMedCrossRefGoogle Scholar
  26. 26.
    Perl TM, Dvorak L, Hwang T, Wenzel RP (1995) Longterm survival and function after suspected Gram-negative sepsis. JAMA 274: 338–345PubMedCrossRefGoogle Scholar
  27. 27.
    Pilz G, Werdan K (1989) Scoresysteme in der Intensivmedizin. Internist 30: 82–87PubMedGoogle Scholar
  28. 28.
    Quartin AA, Roland MH, Kett DH, Peduzzi PN (for the Department of Veterans Affairs Systemic Sepsis Cooperative Study Group) (1997) Magnitude and duration of the effect of sepsis on survival. JAMA 277: 1058–1063PubMedCrossRefGoogle Scholar
  29. 29.
    Rapoport J, Teres D, Lemeshow S, Gehlbach S (1994) A method for assessing the clinical performance and cost-effectiveness of intensive care units: A multicenter inception cohort study. Crit Care Med 22 (9): 1385–1391PubMedCrossRefGoogle Scholar
  30. 30.
    Rivera-Fernández R, Vázquez-Mata G, Bravo M, Zimmermann JE, Wagner D, Knaus W (1998) The APACHE III prognostic system: Customized mortality predictions for Spanish ICU patients. Intensive Care Med 24: 574–581Google Scholar
  31. 31.
    Rowan KM, Kerr JH, Major E, McPherson K, Short A, Vessey MP (1993) Intensive Care Society’s APACHE II study in Britain and Ireland — II: Outcome comparisons of intensive care units after adjustment for case mix by the American APACHE II method. BMJ 307: 977–981Google Scholar
  32. 32.
    Rowan KM, Major E, McPherson K, Short A, Vessey MP (1993) Intensive Care Society’s APACHE II study in Britain and Ireland — I: Variations in case mix of adult admissions to general intensive care units and impact on outcome. BMJ 307: 972–977Google Scholar
  33. 33.
    Rue M (1997) Statistical issues related to applying severity models. Curr Op Crit Care 3: 175–178CrossRefGoogle Scholar
  34. 34.
    Schuster HP (1988) Hypothese: Score-Systeme optimieren die Intensivmedizin. Med Klin 83 (2): 68–72Google Scholar
  35. 35.
    Silva AM, Nap RE, Miranda DR (1999) Monitoring adverse events in the ICU and patient outcome. Intensive Care Med 25 (S1): S46Google Scholar
  36. 36.
    Sirio CA, Rotondi AJ (1999) Community-wide assessment of intensive care outcomes using a physiologically based prognostic measure: Implications for critical care delivery from Cleveland health quality choice. Chest 115 (3): 793–801PubMedCrossRefGoogle Scholar
  37. 37.
    Suistomaa M Ruokonen E (2000) Sampling rate causes bias in APACHE II and SAPS II scores. Intensive Care Med 26 (12): 1727–1729CrossRefGoogle Scholar
  38. 38.
    Zimmermann JE, Draper EA, Wagner DP (2001) Comparing ICU populations: Background and current methods. In: Sibbald WJ, Bion JF (eds) Evaluating critical care. Update in intensive care and emergency medicine, vol 35. Springer, Berlin Heidelberg New York Tokyo, p 121–139Google Scholar
  39. 39.
    Zimmermann JE, Wagner DP, Draper EA, Wright L, Alzola C, Knaus WA (1998) Evaluation of acute physiology and chronic health evaluation III predictions of hospital mortality in an independent database. Crit Care Med 26 (8): 1317–1326CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • G. Deutschinoff
  • C. Friedrich
  • R. Markgraf
  • T. Scholten

There are no affiliations available

Personalised recommendations