gms | German Medical Science

51. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (gmds)

10. - 14.09.2006, Leipzig

Attributable mortality due to nosocomial infections: a simple and useful application of multistate models

Meeting Abstract

Suche in Medline nach

  • Martin Schumacher - Institut für Med. Biometrie und Med. Informatik, Freiburg

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (gmds). 51. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. Leipzig, 10.-14.09.2006. Düsseldorf, Köln: German Medical Science; 2006. Doc06gmds075

Die elektronische Version dieses Artikels ist vollständig und ist verfügbar unter: http://www.egms.de/de/meetings/gmds2006/06gmds149.shtml

Veröffentlicht: 1. September 2006

© 2006 Schumacher.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Background

Nosocomial infections constitute a major medical problem leading to increased morbidity and mortality of patients. Besides prolongation of length of hospital stay, mortality attributable to those infections is often the quantity of interest when describing their impact and consequences [1]. Since occurrence of nosocomial infections is a time-dynamic process, estimation of this quantity might be hampered by that fact. In addition, discharge of patients acting as competing risk and potential censoring of observation time has to be taken into account.

Methods

Since the term “attributable mortality” is used in a variety of meanings [2] we first review basic definitions; then we derive the quantities of interest in terms of transition probabilities arising in a suitably defined multistate model that allows straightforward estimation and interpretation [3], [4]. Bootstrap resampling is used to calculate corresponding standard errors and confidence intervals.

Results

The methodology is applied to the data of the SIR-3 study, a prospective cohort study on the incidence of nosocomial infections in intensive care unit patients [5], [6], where besides mortality attributable to nosocomial infections mortality attributable to infections already present at admission to ICU is considered, too.

Conclusion

Application of a multistate model turns out to be a useful and easily understandable approach for the estimation of attributable mortality in the setting of a prospective cohort study when the risk factor of interest is time-dependent and competing events as well as censoring have to be taken into account. Analysis can be performed by using our R-package change LOS (www.r-project.org).


Literatur

1.
Garcia-Martin H, Lardelli-Clavet P, Jiminéz-Moleón JJ, Bueno-Cavanillas A, Luna-del-Castillo JD, Gálvez-Vargas R. Proportion of hospital deaths potentially attributable to nosocomial infections. Infect Control Hosp Epidemiol 2001; 22: 708-714.
2.
Gefeller O. Definitions of attributable risk-revisited. Pub Health Rev 1995; 23:343-355.
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Escolano S, Golenard JL, Korinck AM, Mallet A. A multi-state model for evolution of intensive care unit patients: prediction of nosocomial infections and deaths. Statist Med 2000; 19:3465-3482.
4.
Hougaard P. Analysis of multivariate survival data. New York: Springer; 2000.
5.
Beyersmann J, Gastmeier P, Grundmann H, Bärwolff S, Geffers C, Behnke M, Rüden H, Schumacher M. Use of multistate models to assess prolongation of intensive care unit stay due to nosocomial infections. Infect Control Hosp Epidemiol 2006 (in press)
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Grundmann H, Bärwolff S, Tami A, Behnke M, Schwab F, Geffers C, Halle E, Göbel UB, Schiller R, Jonas D, Klare I, Weist K, Witte W, Beck-Beilecke K, Schumacher M, Rüden H, Gastmeier P. How many infections are caused by patient-to-patient transmission in intensive care units? Crit Care Med 2005; 33: 946-951.