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Using Electronic Health Information to Risk-Stratify Rates of Clostridium difficile Infection in US Hospitals

Published online by Cambridge University Press:  02 January 2015

Marya D. Zilberberg ,
Ying P. Tabak ,
Dawn M. Sievert ,
Karen G. Derby ,
Richard S. Johannes ,
Xiaowu Sun  and
L. Clifford McDonald
Marya D. Zilberberg
Affiliation:
University of Massachusetts and EviMed Research Group, Amherst, Massachusetts
Ying P. Tabak
Affiliation:
Clinical Research, MedMined Services, CareFusion, Marlborough, Massachusetts
Dawn M. Sievert
Affiliation:
Centers for Disease Control and Prevention, Atlanta, Georgia
Karen G. Derby
Affiliation:
Clinical Research, MedMined Services, CareFusion, Marlborough, Massachusetts
Richard S. Johannes
Affiliation:
Clinical Research, MedMined Services, CareFusion, Marlborough, Massachusetts Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
Xiaowu Sun
Affiliation:
Clinical Research, MedMined Services, CareFusion, Marlborough, Massachusetts
L. Clifford McDonald*
Affiliation:
Centers for Disease Control and Prevention, Atlanta, Georgia
*
Prevention and Response Branch, Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, 1600 Clifton Road NE, MS A-35, Atlanta, GA 30333 (cmcdonald1@cdc.gov)
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Abstract

Background.

Expanding hospitalized patients' risk stratification for Clostridium difficile infection (CDI) is important for improving patient safety. We applied definitions for hospital-onset (HO) and community-onset (CO) CDI to electronic data from 85 hospitals between January 2007 and June 2008 to identify factors associated with higher HO CDI rates.

Methods.

Nonrecurrent CDI cases were identified among adult (≥18-year-old) inpatients by a positive C. difficile toxin assay result more than 8 weeks after any previous positive result. Case categories included HO, CO-hospital associated (CO-HA), CO-indeterminate hospital association (CO-IN), and CO–non–hospital associated (CO-NHA). C. difficile testing intensity (CDTI) was defined as the total number of C. difficile tests performed, normalized to the number of patients with at least 1 C. difficile toxin test recorded. We calculated both the incidence density and the prevalence of CDI where appropriate. We fitted a multivariable Poisson model to identify factors associated with higher HO CDI rates.

Results.

Among 1,351,156 unique patients with 2,022,213 admissions, 9,803 cases of CDI were identified; of these, 50.6% were HO, 17.4% were CO-HA, 9.0% were CO-IN, and 23.0% were CO-NHA. The incidence density of HO was 6.3 per 10,000 patient-days. The prevalence of CO CDI on admission was, per 10,000 admissions, 8.4 for CO-HA, 4.4 for CO-IN, and 11.1 for CO-NHA. Factors associated (P< .0001) with higher HO CDI rates included older age, higher CO-NHA prevalence on admission, and increased CDTI.

Conclusion.

Electronic health information can be leveraged to risk-stratify HO CDI rates by patient age and CO-NHA prevalence on admission. Hospitals should optimize diagnostic testing to improve patient care and measured CDI rates.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 32 , Issue 7 , July 2011 , pp. 649 - 655
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2011

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References

1.Warny, M, Pepin, J, Fang, A, et al. Toxin production by an emerging strain of Clostridium difficile associated with outbreaks of severe disease in North America and Europe. Lancet 2005;366(9491):1079–1084.Google Scholar
2.Stabler, RA, Dawson, LF, Phua, LTH, Wren, BW. Comparative analysis of BI/NAP1/027 hypervirulent strains reveals novel toxin B-encoding gene (tcdB) sequences. J Med Microbiol 2008;57(6):771–775.Google Scholar
3.Wilcox, MH, Fawley, WN. Hospital disinfectants and spore formation by Clostridium difficile. Lancet 2000;356(9238):1324.Google Scholar
4.Zilberberg, MD, Shorr, AF, Kollef, MH. Increase in adult Clostridium difficile-related hospitalizations and case-fatality rate, United States, 2000–2005. Emerg Infect Dis 2008;14(6):929–931.Google Scholar
5.Zilberberg, MD, Tillotson, GS, McDonald, C. Clostridium difficile infections among hospitalized children, United States, 1997–2006. Emerg Infect Dis 2010;16(4):604–609.Google Scholar
6.Noren, T, Akerlund, T, Back, E, et al. Molecular epidemiology of hospital-associated and community-acquired Clostridium difficile infection in a Swedish county. J Clin Microbiol 2004;42(8):3635–3643.Google Scholar
7.McDonald, LC, Coignard, B, Dubberke, E, et al. Recommendations for surveillance of Clostridium difficile-associated disease. Infect Control Hosp Epidemiol 2007;28(2):140–145.Google Scholar
8.Association for Professionals in Infection Control and Epidemiology (APIC). Legislation in progress. http://www.apic.org/map/index.htm. Accessed September 16, 2010.Google Scholar
9.Benoit, S, McDonald, L, English, R, Tokars, J. Automated surveillance of Clostridium difficile infections using BioSense. Infect Control Hosp Epidemiol 2011;32(1):26–33.CrossRefGoogle ScholarPubMed
10.Brossette, SE, Hacek, DM, Gavin, PJ, et al. A laboratory-based, hospital-wide, electronic marker for nosocomial infection: the future of infection control surveillance? Am J Clin Pathol 2006;125(1):34–39.Google Scholar
11.Brossette, SE, Sprague, AP, Hardin, JM, Waites, KB, Jones, WT, Moser, SA. Association rules and data mining in hospital infection control and public health surveillance. J Am Med Inform Assoc 1998;5(4):373–381.Google Scholar
12.Kilgore, ML, Ghosh, K, Beavers, CM, Wong, DY, Hymel, PA Jr, Brossette, SE. The costs of nosocomial infections. Med Care 2008;46(1):101–104.Google Scholar
13.American Hospital Association (AHA). Annual Survey Database, Chicago, 111. Fiscal Year 2007. http://www.aha.org/. Accessed September 16, 2010.Google Scholar
14.Peterson, LR, Robicsek, A. Does my patient have Clostridium difficile infection? Ann Intern Med 2009;151(3):176–179.CrossRefGoogle ScholarPubMed
15.Jarvis, WR, Schlosser, J, Jarvis, AA, Chinn, RY. National point prevalence of Clostridium difficile in US health care facility inpatients, 2008. Am J Infect Control 2009;37(4):263–270.Google Scholar
16.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(5):431–455.Google Scholar
17.Luo, RF, Banaei, N. Is repeat PCR needed for the diagnosis of Clostridium difficile infection? J Clin Microbiol 2010;48(10):3738–3741.Google Scholar
18.Campbell, RJ, Giljahn, L, Machesky, K, et al. Clostridium difficile infection in Ohio hospitals and nursing homes during 2006. Infect Control Hosp Epidemiol 2009;30(6):526–533.Google Scholar
19.Dubberke, ER, Butler, AM, Yokoe, DS, et al. Multicenter study of Clostridium difficile infection rates from 2000 to 2006. Infect Control Hosp Epidemiol 2010;31(10):1030–1037.Google Scholar
20.Agency for Healthcare Research and Quality (AHRQ). Healthcare Cost and Utilization Project (HCUP). http://www.ahrq.gov/data/hcup/. Accessed September 16, 2010.Google Scholar
21.Dubberke, ER, Butler, AM, Yokoe, DS, et al. Multicenter study of surveillance for hospital-onset Clostridium difficile infection by the use of ICD-9-CM diagnosis codes. Infect Control Hosp Epidemiol 2010;31(3):262–268.CrossRefGoogle ScholarPubMed
22.Centers for Disease Control and Prevention. Multidrug-Resistant Organism and Clostridium difficile-Associated Disease (MDRO/CDAD) Module. http://www.cdc.gov/nhsn/PDFs/pscManual/12pscMDRO_CDADcurrent.pdf. Accessed September 16, 2010.Google Scholar
23.Centers for Disease Control and Prevention. National Healthcare Safety Network (NHSN): clinical document architecture (CDA). http://www.cdc.gov/nhsn/CDA_eSurveillance.html. Accessed September 16, 2010Google Scholar