Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-29T11:11:14.505Z Has data issue: false hasContentIssue false
  • English
  • Français

Developing a Clinical Prediction Rule for First Hospital-Onset Clostridium difficile Infections: A Retrospective Observational Study

Published online by Cambridge University Press:  28 April 2016

Anne Press ,
Benson S Ku ,
Lauren McCullagh ,
Lisa Rosen ,
Safiya Richardson  and
Thomas McGinn
Anne Press*
Affiliation:
Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York
Benson S Ku
Affiliation:
Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York
Lauren McCullagh
Affiliation:
Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York
Lisa Rosen
Affiliation:
Feinstein Institute for Medical Research, Manhasset, New York
Safiya Richardson
Affiliation:
Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York
Thomas McGinn
Affiliation:
Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York
*
Address correspondence to Anne Press, BS, Hofstra North Shore-LIJ School of Medicine, 300 Community Dr, Manhasset, NY 11030 (anniepress@gmail.com).
Get access Rights & Permissions [Opens in a new window]

Abstract

BACKGROUND

The healthcare burden of hospital-acquired Clostridium difficile infection (CDI) demands attention and calls for a solution. Identifying patients’ risk of developing a primary nosocomial CDI is a critical first step in reducing the development of new cases of CDI.

OBJECTIVE

To derive a clinical prediction rule that can predict a patient’s risk of acquiring a primary CDI.

DESIGN

Retrospective cohort study.

SETTING

Large tertiary healthcare center.

PATIENTS

Total of 61,482 subjects aged at least 18 admitted over a 1-year period (2013).

INTERVENTION

None.

METHODS

Patient demographic characteristics, evidence of CDI, and other risk factors were retrospectively collected. To derive the CDI clinical prediction rule the patient population was divided into a derivation and validation cohort. A multivariable analysis was performed in the derivation cohort to identify risk factors individually associated with nosocomial CDI and was validated on the validation sample.

RESULTS

Among 61,482 subjects, CDI occurred in 0.46%. CDI outcome was significantly associated with age, admission in the past 60 days, mechanical ventilation, dialysis, history of congestive heart failure, and use of antibiotic medications. The sensitivity and specificity of the score, in the validation set, were 82.0% and 75.7%, respectively. The area under the receiver operating characteristic curve was 0.85.

CONCLUSION

This study successfully derived a clinical prediction rule that will help identify patients at high risk for primary CDI. This tool will allow physicians to systematically recognize those at risk for CDI and will allow for early interventional strategies.

Infect Control Hosp Epidemiol 2016;37:896–900

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 37 , Issue 8 , August 2016 , pp. 896 - 900
Copyright
© 2016 by The Society for Healthcare Epidemiology of America. All rights reserved 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Tillotson, GS, Tillotson, J. Clostridium difficile—a moving target. F1000 Med Rep 2011;3:6.10.3410/M3-6Google Scholar
2. Mitchell, BG, Gardner, A. Prolongation of length of stay and Clostridium difficile infection: a review of the methods used to examine length of stay due to healthcare associated infections. Antimicrob Resist Infect Control 2012;1:14.10.1186/2047-2994-1-14Google Scholar
3. Rupnik, M, Wilcox, MH, Gerding, DN. Clostridium difficile infection: new developments in epidemiology and pathogenesis. Nat Rev Microbiol 2009;7:526–536.10.1038/nrmicro2164Google Scholar
4. Cohen, SH, Gerding, DN, 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;31:431–455.10.1086/651706Google Scholar
5. Pépin, 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;171:466–472.Google Scholar
6. McDonald, LC, Killgore, GE, Thompson, A, et al. An epidemic, toxin gene-variant strain of Clostridium difficile . N Engl J Med 2005;353:2433–2441.Google Scholar
7. Loo, VG, Poirier, L, Miller, MA, et al. A predominantly clonal multi-institutional outbreak of Clostridium difficile-associated diarrhea with high morbidity and mortality. N Engl J Med 2005;353:2442–2449.10.1056/NEJMoa051639Google Scholar
8. Freeman, J, Bauer, MP, Baines, SD, et al. The changing epidemiology of Clostridium difficile infections. Clin Microbiol Rev 2010;23:529–549.Google Scholar
9. Gerding, DN, Johnson, S, Peterson, LR, Mulligan, ME, Silva, J. Clostridium difficile–associated diarrhea and colitis. Infect Control Hosp Epidemiol 1995;16:459–477.Google Scholar
10. Johnson, S, Gerding, DN, Olson, MM, et al. Prospective, controlled study of vinyl glove use to interrupt Clostridium difficile nosocomial transmission. Am J Med 1990;88:137–140.10.1016/0002-9343(90)90462-MGoogle Scholar
11. Kaatz, GW, Gitlin, SD, Schaberg, DR, et al. Acquisition of Clostridium difficile from the hospital environment. Am J Epidemiol 1988;127:1289–1294.10.1093/oxfordjournals.aje.a114921Google Scholar
12. Mayfield, JL, Leet, T, Miller, J, Mundy, LM. Environmental control to reduce transmission of Clostridium difficile . Clin Infect Dis 2000;31:995–1000.Google Scholar
13. McMullen, KM, Zack, J, Coopersmith, CM, Kollef, M, Dubberke, E, Warren, DK. Use of hypochlorite solution to decrease rates of Clostridium difficile–associated diarrhea. Infect Control Hosp Epidemiol 2007;28:205–207.Google Scholar
14. Smith, LA, Chan, CK, Halm, M, Slattery, W, Lindquist, R, Savik, K. Development and validation of a Clostridium difficile risk assessment tool. AACN Adv Crit Care 2014;25:334–346.10.1097/NCI.0000000000000046Google Scholar
15. Chandra, S, Thapa, R, Marur, S, Jani, N. Validation of a clinical prediction scale for hospital-onset Clostridium difficile infection. J Clin Gastroenterol 2014;48:419–422.10.1097/MCG.0000000000000012Google Scholar
16. Dubberke, ER, Yan, Y, Reske, KA, et al. Development and validation of a Clostridium difficile infection risk prediction model. Infect Control Hosp Epidemiol 2011;32:360–366.Google Scholar
17. Garey, KW, Dao-Tran, TK, Jiang, ZD, Price, MP, Gentry, LO, Dupont, HL. A clinical risk index for Clostridium difficile infection in hospitalised patients receiving broad-spectrum antibiotics. J Hosp Infect 2008;70:142–147.10.1016/j.jhin.2008.06.026Google Scholar
18. Tanner, J, Khan, D, Anthony, D, Paton, J. Waterlow score to predict patients at risk of developing Clostridium difficile-associated disease. J Hosp Infect 2009;71:239–244.10.1016/j.jhin.2008.11.017Google Scholar