Skip to main content Accessibility help
×
Home

Excess Length of Stay Attributable to Clostridium difficile Infection (CDI) in the Acute Care Setting: A Multistate Model

  • Vanessa W. Stevens (a1) (a2), Karim Khader (a1) (a3), Richard E. Nelson (a1) (a3), Makoto Jones (a1) (a3), Michael A. Rubin (a1) (a3), Kevin A. Brown (a1) (a3), Martin E. Evans (a4), Tom Greene (a1) (a3), Eric Slade (a5) and Matthew H. Samore (a1) (a3)...

Abstract

BACKGROUND

Standard estimates of the impact of Clostridium difficile infections (CDI) on inpatient lengths of stay (LOS) may overstate inpatient care costs attributable to CDI. In this study, we used multistate modeling (MSM) of CDI timing to reduce bias in estimates of excess LOS.

METHODS

A retrospective cohort study of all hospitalizations at any of 120 acute care facilities within the US Department of Veterans Affairs (VA) between 2005 and 2012 was conducted. We estimated the excess LOS attributable to CDI using an MSM to address time-dependent bias. Bootstrapping was used to generate 95% confidence intervals (CI). These estimates were compared to unadjusted differences in mean LOS for hospitalizations with and without CDI.

RESULTS

During the study period, there were 3.96 million hospitalizations and 43,540 CDIs. A comparison of unadjusted means suggested an excess LOS of 14.0 days (19.4 vs 5.4 days). In contrast, the MSM estimated an attributable LOS of only 2.27 days (95% CI, 2.14–2.40). The excess LOS for mild-to-moderate CDI was 0.75 days (95% CI, 0.59–0.89), and for severe CDI, it was 4.11 days (95% CI, 3.90–4.32). Substantial variation across the Veteran Integrated Services Networks (VISN) was observed.

CONCLUSIONS

CDI significantly contributes to LOS, but the magnitude of its estimated impact is smaller when methods are used that account for the time-varying nature of infection. The greatest impact on LOS occurred among patients with severe CDI. Significant geographic variability was observed. MSM is a useful tool for obtaining more accurate estimates of the inpatient care costs of CDI.

Infect. Control Hosp. Epidemiol. 2015;36(9):1024–1030

Copyright

Corresponding author

Address correspondence to Vanessa Stevens, PhD, Research Assistant Professor, IDEAS 2.0 Center, VA Salt Lake City Health Care System, Pharmacotherapy Outcomes Research Center, University of Utah College of Pharmacy, Skaggs Pharmacy Research Institute, 30 S 2000 E, Room 4961, Salt Lake City, UT 84121 (vanessa.stevens@pharm.utah.edu).

Footnotes

Hide All

PREVIOUS PRESENTATION: Selected results from this manuscript were presented in poster format at ID Week 2014 in Philadelphia, Pennsylvania, October 8–12, 2014.

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.

Footnotes

References

Hide All
1. Miller, BA, Chen, LF, Sexton, DJ, Anderson, DJ. Comparison of the burdens of hospital-onset, healthcare facility–associated Clostridium difficile infection and of healthcare-associated infection due to methicillin-resistant Staphylococcus aureus in community hospitals. Infect Control Hosp Epidemiol 2011;32:387390.
2. Butt, E, Foster, J, Keedwell, E, et al. Derivation and validation of a simple, accurate and robust prediction rule for risk of mortality in patients with Clostridium difficile infection. BMC Infect Dis 2013;13:316.
3. Pepin, J, Valiquette, L, Alary, ME, et al. Clostridium difficile-associated diarrhea in a region of Quebec from 1991 to 2003: a changing pattern of disease severity. CMAJ 2004;171:466472.
4. McDonald, LC, Owings, M, Jernigan, DB. Clostridium difficile infection in patients discharged from US short-stay hospitals, 1996–2003. Emerg Infect Dis 2006;12:409415.
5. Reveles, KR, Lee, GC, Boyd, NK, Frei, CR. The rise in Clostridium difficile infection incidence among hospitalized adults in the United States: 2001–2010. Am J Infect Control 2014;42:10281032.
6. McDonald, LC, Killgore, GE, Thompson, A, et al. An epidemic, toxin gene-variant strain of Clostridium difficile . New Engl J Med 2005;353:24332441.
7. Lim, SK, Stuart, RL, Mackin, KE, et al. Emergence of a ribotype 244 strain of Clostridium difficile associated with severe disease and related to the epidemic ribotype 027 strain. Clin Infect Dis 2014;58:17231730.
8. Dumyati, G, Stevens, V, Hannett, GE, et al. Community-associated Clostridium difficile infections, Monroe County, New York, USA. Emerg Infect Dis 2012;18:392400.
9. Ghantoji, SS, Sail, K, Lairson, DR, DuPont, HL, Garey, KW. Economic healthcare costs of Clostridium difficile infection: a systematic review. J Hosp Infect 2010;74:309318.
10. Dubberke, ER, Olsen, MA. Burden of Clostridium difficile on the healthcare system. Clin Infect Dis 2012;55:S88S92.
11. Gabriel, L, Beriot-Mathiot, A. Hospitalization stay and costs attributable to Clostridium difficile infection: a critical review. J Hosp Infect 2014;88:1221.
12. Barnett, AG, Beyersmann, J, Allignol, A, Rosenthal, VD, Graves, N, Wolkewitz, M. The time-dependent bias and its effect on extra length of stay due to nosocomial infection. Value Health 2011;14:381386.
13. Graves, N, Harbarth, S, Beyersmann, J, Barnett, A, Halton, K, Cooper, B. Healthcare epidemiology: estimating the cost of health care–associated infections: mind your p’s and q’s. Clin Infect Dis 2010;50:10171021.
14. Graves, NP, Barnett, AGP, Halton, KP, et al. The importance of good data, analysis, and interpretation for showing the economics of reducing healthcare-associated infection. Infect Control Hosp Epidemiol 2011;32:927928.
15. Beyersmann, J, Kneib, T, Schumacher, M, Gastmeier, P. Nosocomial infection, length of stay, and time-dependent bias. Infect Control Hosp Epidemiol 2009;30:273276.
16. Samore, MH, Harbarth, S. A methodologically focused review of the literature in hospital epidemiology and infection control. In: Mayhall CG, ed. Hospital Epidemiology and Infection Control, 3rd ed. Phildelphia, PA: Lipincott, Williams, and Wilkins, 2004.
17. Vrijens, F, Hulstaert, F, Van de Sande, S, Devriese, S, Morales, I, Parmentier, Y. Hospital-acquired, laboratory-confirmed bloodstream infections: linking national surveillance data to clinical and financial hospital data to estimate increased length of stay and healthcare costs. J Hosp Infect 2010;75:158162.
18. Commenges, D. Multi-state models in epidemiology. Lifetime Data Anal 1999;5:315327.
19. Mitchell, BG, Gardner, A, Barnett, AG, Hiller, JE, Graves, N. The prolongation of length of stay because of Clostridium difficile infection. Am J Infect Control 2014;42:164167.
20. van Kleef, E, Green, N, Goldenberg, SD, et al. Excess length of stay and mortality due to Clostridium difficile infection: a multi-state modelling approach. J Hosp Infect 2014;88:213217.
21. Lagu, T, Stefan, MS, Haessler, S, et al. The impact of hospital-onset Clostridium difficile infection on outcomes of hospitalized patients with sepsis. J Hosp Med 2014;9:411417.
22. Ampel, NM. Plagues: what’s past is present: thoughts on the origin and history of new infectious diseases. Rev Infect Dis 1991;13:658665.
23. Hebden, JN, Anttila, A, Allen-Bridson, K, Morrell, GC, Wright, M-O, Horan, T. Healthcare-associated infections studies project: an American Journal of Infection Control and National Healthcare Safety Network data quality collaboration—LabID Clostridium difficile event 2013. Am J Infect Control 2013;41:916917.
24. Cohen Stuart, H, Gerding Dale, N, Johnson, S, et al. Clinical practice guidelines for Clostridium difficile infection in adults: 2010 update by the Society for Healthcare Epidemiology of America (SHEA) and the Infectious Diseases Society of America (IDSA). Infect Control Hosp Epidemiol 2010;31:431455.
25. Gagne, JJ, Glynn, RJ, Avorn, J, Levin, R, Schneeweiss, S. A combined comorbidity score predicted mortality in elderly patients better than existing scores. J Clin Epidemiol 2011;64:749759.
26. Barnett, AG, Batra, R, Graves, N, Edgeworth, J, Robotham, J, Cooper, B. Using a longitudinal model to estimate the effect of methicillin-resistant Staphylococcus aureus infection on length of stay in an intensive care unit. Am J Epidemiol 2009;170:11861194.
27. Allignol, AS, Schumacher, M, Beyersmann, J. Empirical transition matrix of multi-state models: the etm package. J Stat Soft 2011;38:115.
28. DiCiccio, TJ, Efron, B. Bootstrap confidence intervals. Stat Sci 1996;11:189212.
29. Lofgren, ET, Cole, SR, Weber, DJ, Anderson, DJ, Moehring, RW. Hospital-acquired Clostridium difficile infections: estimating all-cause mortality and length of stay. Epidemiology 2014;25:570575.
30. Wolkewitz, MA, Allignol, A, Schumacher, M, Beyersmann, J. Two pitfalls in survival analyses of time-dependent exposure: a case study in a cohort of Oscar nominees. Am Statistician 2010;64:205211.
31. Cheknis, AK, Sambol, SP, Davidson, DM, et al. Distribution of Clostridium difficile strains from a North American, European and Australian trial of treatment for C. difficile infections: 2005–2007. Anaerobe 2009;15:230233.
32. Walker, AS, Eyre, DW, Wyllie, DH, et al. Relationship between bacterial strain type, host biomarkers, and mortality in Clostridium difficile infection. Clin Infect Dis 2013;56:15891600.
33. Baier, RMPH, Morphis, BBS, Marsella, MRNBS, Mermel, LADOS. Clostridium difficile surveillance: a multicenter comparison of LabID events and use of standard definitions. Infect Control Hosp Epidemiol 2013;34:653655.
34. Gase, KA, Haley, VB, Xiong, K, Van Antwerpen, C, Stricof, RL. Comparison of 2 Clostridium difficile surveillance methods: National Healthcare Safety Network's laboratory-identified event reporting module versus clinical infection surveillance. Infect Control Hosp Epidemiol 2013;34:284290.
35. NCHS. Data highlights: average length of stay and days of care in the National Hospital Discharge Survey. Atlanta, GA: Centers for Disease Control and Prevention, 2010.

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed