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Use of Multistate Models to Assess Prolongation of Intensive Care Unit Stay Due to Nosocomial Infection

  • J. Beyersmann (a1) (a2), P. Gastmeier (a3), H. Grundmann (a4), S. Bärwolff (a5), C. Geffers (a5), M. Behnke (a5), H. Rüden (a5) and M. Schumacher (a1)...

Abstract

Background.

Reliable data on the costs attributable to nosocomial infection (NI) are crucial to demonstrating the real cost-effectiveness of infection control measures. Several studies investigating this issue with regard to intensive care unit (ICU) patients have probably overestimated, as a result of inappropriate study methods, the part played by NIs in prolonging the length of stay.

Methods.

Data from a prospective study of the incidence of NI in 5 ICUs over a period of 18 months formed the basis of this analysis. For describing the temporal dynamics of the data, a multistate model was used. Thus, ICU patients were counted as case patients as soon as an NI was ascertained on any particular day. All patients were then regarded as control subjects as long as they remained free of NI (time-to-event data analysis technique).

Results.

Admitted patients (n = 1,876) were observed for the development of NI over a period of 28,498 patient-days. In total, 431 NIs were ascertained during the study period (incidence density, 15.1 NIs per 1,000 patient-days). The influence of NI as a time-dependent covariate in a proportional hazards model was highly significant (P< .0001, Wald test). NI significantly reduced the discharge hazard (hazard ratio, 0.72 [95% confidence interval, 0.63-0.82])—that is, it prolonged the ICU stay. The mean prolongation of ICU length of stay due to NI ( ± standard error) was estimated to be 5.3 ± 1.6 days.

Conclusions.

Further studies are required to enable comparison of data on prolongation of ICU length of stay with the results of various study methods.

Copyright

Corresponding author

Freiburg Centre for Data Analysis and Modeling, University of Freiburg, Eckerstrasse 1, Freiburg D-79104, Germany (jan@fdm.uni-freiburg.de)

References

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1.De Clercq, H, De Decker, G, Alexander, JP, Huyghens, L. Cost evaluation of infections in intensive care. Acta Anaesthesiolo Belg 1983; 34:179189.
2.Girou, E, Stephan, F, Novara, A, Safar, M, Fagon, J-V. Risk factors and outcome of nosocomial infections: results of a matched case-control study of ICU patients. Am J Respir Crit Care Med 1998; 157:11511158.
3.Craig, CP, Connelly, S. Effect of intensive care unit nosocomial pneumonia on duration of stay and mortality. Am J Infect Control 1984; 12:233238.
4.Leu, HS, Kaiser, DL, Mori, M, Woolson, RF, Wenzel, RP. Hospital acquired pneumonia—attributable mortality and morbidity. Am J Epidemiol 1989; 129:12581267.
5.Kappstein, I, Schulgen, G, Beyer, U, Geiger, K, Schumacher, M, Daschner, F. Prolongation of hospital stay and extra costs due to ventilator-associated pneumonia in an intensive care unit. Eur J Clin Microbiol Infect Dis 1992; 11:504508.
6.Fagon, JY, Chastre, J, Hance, AJ, Montravers, P, Novara, A, Gilbert, C. Nosocomial pneumonia in ventilated patients: a cohort study evaluating attributable mortality and hospital stay. Am J Med 1993; 94:281288.
7.Papazian, L, Bregeon, F, Thirion, X, et al. Effect of ventilator-associated pneumonia on mortality and morbidity. Am J Respir Crit Care Med 1996; 154:9197.
8.Baker, AM, Meredith, JW, Haponik, EF. Pneumonia in intubated trauma patients: microbiology and outcomes. Am J Respir Crit Care Med 1996; 153:343349.
9.Pittet, D, Tarara, D, Wenzel, R. Nosocomial bloodstream infection in critically ill patients: excess length of stay, extra costs, and attributable mortality. JAMA 1994; 271:15981601.
10.Digiovine, B, Chenoweth, C, Watts, C, Higgins, M. The attributable mortality and costs of primary nosocomial bloodstream infections in the intensive care unit. Am J Respir Crit Care Med 1999; 160:976981.
11.Rello, J, Ochagavia, A, Sabanes, E, et al. Evaluation of outcome of intravenous catheter-related infections in critically ill patients. Am J Respir Crit Care Med 2000; 162:10271030.
12.Renaud, B, Brun-Buisson, C. ICU-Bacterremia Study Group. Outcomes of primary and catheter-related bacteremia. Am J Respir Crit Care Med 2001; 163:15841590.
13.Dimick, J, Pelz, R, Consunji, R, Swoboda, SM, Hendrix, CW, Lipsett, P. Increased resource use associated with catheter-related bloodstream infection in the surgical intensive care unit. Arch Surg 2001; 136:229234.
14.van Walraven, C, Davis, D, Forster, A, Wells, GA. Time-dependent bias was common in survival analyses published in leading clinical journals. J Clin Epidemiol 2004; 57:672682.
15.Grundmann, H-J, Bärwolff, S, Schwab, F, et al. How many infections are caused by transmission in intensive care units?. Crit Care Med 2005; 53:135146.
16.Garner, JS, Emori, WR, Horan, TC, Hughes, JM. CDC definitions for nosocomial infections. Am J Infect Control 1988; 16:128140.
17.Horan, TC, Gaynes, RP, Martone, WJ, Jarvis, WR, Emori, TG. CDC definitions of surgical site infections: a modification of CDC definitions of surgical wound infections. Infect Control Hosp Epidemiol 1992; 13:606608.
18.Gastmeier, P, Geffers, C, Sohr, D, Dettenkofer, M, Daschner, F, Rüden, H. Five years working with the German Nosocomial Infection Surveillance System KISS. Am J Infect Control 2003; 31:316321.
19.Andersen, P, Keiding, N. Multi-state models for event history analysis. Stat Methods Med Res 2002; 11:91115.
20.Schulgen, G, Schumacher, M. Estimation of prolongation of hospital stay attributable to nosocomial infections: new approaches based on multi-state models. Lifetime Data Anal 1996; 2:219240.
21.Marubini, E, Valsecchi, GM. The scope of survival analysis. In: Analysing Survival Data from Clinical Trials and Observational Studies. Chichester: Wiley & Sons; 1995:110.
22.Crowder, MJ. Classical Competing Risks. Boca Raton, FL: Chapman & Hall; 2001.
23.Efron, B, Tibshirani, R. An Introduction to the Bootstrap. New York: Chapman & Hall; 1993.
24.Therneau, T, Grambsch, P. Modeling survival data: extending the Cox model. In: Statistics for Biology and Health. New York: Springer; 2000.
25.Andersen, P, Abildstrom, S, Rosthoj, S. Competing risks as a multi-state model. Stat Methods Med Res 2002; 11:203215.
26.R Development Core team. A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2004.
27.Roberts, R, Scott, R, Cordell, R, et al. The use of economic modeling to determine the hospital costs associated with nosocomial infections. Clin Infect Dis 2003; 36:14241432.
28.Schulgen, G, Kropec, A, Kappstein, I, Daschner, F, Schumacher, M. Estimation of extra hospital stay attributable to nosocomial infections: heterogeneity and timing of events. J Clin Epidemiol 2000; 53:409417.
29.Klein, J, Rizzo, J, Zhang, M, et al. Statistical methods for the analysis and presentation of the results of bone marrow transplants. I. Unadjusted analysis. Bone Marrow Transplant 2001; 28:909915.
30.Beyersmann, J. On change in length of stay associated with an intermediate event: estimation within multi-state models and large sample properties [PhD thesis]. Freiburg, Germany: University of Freiburg; 2005. Available at: http://www.freidok.uni-freiburg.de/volltexte/1843.
31.Diaz-Molina, C, Garcia, MM, Bueno, CA, Lopez, LA, Delgado, RM, Galvez, VR. The estimation of the cost of nosocomial infection in an intensive care unit. Med Clin (Bare) 1993; 1993:329332.
32.Heyland, D, Cook, D, Griffith, L, Keenan, S, Brun-Buisson, C. Canadian Critical Care Trial Group. The attributable morbidity and mortality of ventilator-associated pneumonia in the critically ill patient. Am J Respir Crit Care Med 1999; 159:12491256.
33.Kappstein, I, Schulgen, G, Fraedrich, G, Schlosser, V, Schumacher, M, Daschner, FD. Added hospital stay due to wound infections following cardiac surgery. Thorac Cardiovasc Surg 1992; 40:148151.
34.Rello, J, Ollendorf, D, Oster, G, et al. VAP Outcomes Scientific Advisory Group. Epidemiology and outcomes of ventilator-associated pneumonia in a large US database. Chest 2002; 122:21152121.
35.Federal statistical office. Statistisches Jahrbuch 2005. Wiesbaden, Germany: Statistisches Bundesamt; 2005.
36.Zuschneid, I, Geifers, C, Sohr, D, et al. Validation of the surveillance in the intensive care unit component of the German Nosocomial Infection Surveillance System KISS. Infect Control Hosp Epidemiol(in press).
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Infection Control & Hospital Epidemiology
  • ISSN: 0899-823X
  • EISSN: 1559-6834
  • URL: /core/journals/infection-control-and-hospital-epidemiology
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