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Screening for Asymptomatic Clostridium difficile Among Bone Marrow Transplant Patients: A Mixed-Methods Study of Intervention Effectiveness and Feasibility

Published online by Cambridge University Press:  25 January 2018

Anna K. Barker
Affiliation:
Department of Population Health Sciences, University of Wisconsin–Madison, Madison, Wisconsin
Benjamin Krasity
Affiliation:
School of Medicine and Public Health, University of Wisconsin–Madison, Madison, Wisconsin
Jackson Musuuza
Affiliation:
William S. Middleton Memorial Veterans Affairs Hospital, Madison, Wisconsin
Nasia Safdar*
Affiliation:
William S. Middleton Memorial Veterans Affairs Hospital, Madison, Wisconsin Department of Medicine, Division of Infectious Disease, University of Wisconsin–Madison, Madison, Wisconsin
*
Address correspondence to Nasia Safdar, Department of Medicine, Division of Infectious Disease, University of Wisconsin–Madison, 1685 Highland Avenue, Madison, Wisconsin ([email protected]).

Abstract

OBJECTIVE

To identify facilitators and barriers to implementation of a Clostridium difficile screening intervention among bone marrow transplant (BMT) patients and to evaluate the clinical effectiveness of the intervention on the rate of hospital-onset C. difficile infection (HO-CDI).

DESIGN

Before-and-after trial

SETTING

A 505-bed tertiary-care medical center

PARTICIPANTS

All 5,357 patients admitted to the BMT and general medicine wards from January 2014 to February 2017 were included in the study. Interview participants included 3 physicians, 4 nurses, and 4 administrators.

INTERVENTION

All BMT patients were screened within 48 hours of admission. Colonized patients, as defined by a C. difficile–positive polymerase chain reaction (PCR) stool result, were placed under contact precautions for the duration of their hospital stay.

METHODS

Interview responses were coded according to the Systems Engineering Initiative for Patient Safety conceptual framework. We compared pre- and postintervention HO-CDI rates on BMT and general internal medicine units using time-series analysis.

RESULTS

Stakeholder engagement, at both the person and organizational level, facilitates standardization and optimization of intervention protocols. While the screening intervention was generally well received, tools and technology were sources of concern. The mean incidence of HO-CDI decreased on the BMT service postintervention (P<.0001). However, the effect of the change in the trend postintervention was not significantly different on BMT compared to the control wards (P=.93).

CONCLUSIONS

We report the first mixed-methods study to evaluate a C. difficile screening intervention among the BMT population. The positive nature by which the intervention was received by front-line clinical staff, laboratory staff, and administrators is promising for future implementation studies.

Infect Control Hosp Epidemiol 2018;39:177–185

Type
Original Articles
Copyright
© 2018 by The Society for Healthcare Epidemiology of America. All rights reserved 

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References

REFERENCES

1. Lessa, FC, Mu, Y, Bamberg, WM, et al. Burden of Clostridium difficile infection in the United States. N Engl J Med 2015;372:825834.Google Scholar
2. Weber, DJ, Rutala, WA, Miller, MB, Huslage, K, Sickbert-Bennett, E. Role of hospital surfaces in the transmission of emerging health care-associated pathogens: norovirus, Clostridium difficile, and Acinetobacter species. Am J Infect Control 2010;38:S25S33.CrossRefGoogle ScholarPubMed
3. Eyre, DW, Cule, ML, Wilson, DJ, et al. Diverse sources of C. difficile infection identified on whole-genome sequencing. N Engl J Med 2013;369:11951205.CrossRefGoogle ScholarPubMed
4. Alonso, CD, Treadway, SB, Hanna, DB, et al. Epidemiology and outcomes of Clostridium difficile infections in hematopoietic stem cell transplant recipients. Clin Infect Dis 2012;54:10531063.CrossRefGoogle ScholarPubMed
5. Cózar-Llistó, A, Ramos-Martinez, A, Cobo, J. Clostridium difficile infection in special high-risk populations. Infect Dis Ther 2016;5:253269.CrossRefGoogle ScholarPubMed
6. Balletto, E, Mikulska, M. Bacterial infections in hematopoietic stem cell transplant recipients. Mediterr J Hematol Infect Dis 2015;7:e2015045.Google Scholar
7. Dubberke, ER, Carling, P, Carrico, R, et al. Strategies to prevent clostridium difficile infections in acute care hospitals: 2014 update. Infect Control Hosp Epidemiol 2014;35:S48S65.Google Scholar
8. Lanzas, C, Dubberke, ER. Effectiveness of screening hospital admissions to detect asymptomatic carriers of Clostridium difficile: a modeling evaluation. Infect Control Hosp Epidemiol 2014;35:10431050.CrossRefGoogle ScholarPubMed
9. Longtin, Y, Paquet-Bolduc, B, Gilca, R, et al. Effect of detecting and isolating Clostridium difficile carriers at hospital admission on the incidence of C difficile infections: a quasi-experimental controlled study. JAMA Intern Med 2016;176:796804.CrossRefGoogle ScholarPubMed
10. Carayon, P, Hundt, AS, Karsh, B, et al. Work system design for patient safety: the SEIPS model. Qual Saf Health Care 2006;15:i50i58.CrossRefGoogle ScholarPubMed
11. Yanke, E, Zellmer, C, Van Hoof, S, Moriarty, H, Carayon, P, Safdar, N. Understanding the current state of infection prevention to prevent Clostridium difficile infection: a human factors and systems engineering approach. Am J Infect Control 2015;43:241247.CrossRefGoogle ScholarPubMed
12. Miles, M, Huberman, AM, Saldana, J. Qualitative Data Analysis: A Methods Sourcebook. 3rd ed. Thousand Oaks, CA: Sage; 2014.Google Scholar
13. Multidrug-resistant organism and Clostridium difficile infection (MDRO/CDI) module. Centers for Disease Control and Prevention website. https://www.cdc.gov/nhsn/pdfs/pscmanual/12pscmdro_ cdadcurrent.pdf. Published 2017. Accessed April 18, 2017.Google Scholar
14. Stevens, VW, Khader, K, Nelson, RE, et al. Excess length of stay attributable to Clostridium difficile infection (CDI) in the acute care setting: a multistate model. Infect Control Hosp Epidemiol 2015;36:10241030.Google Scholar
15. Hota, SS, Achonu, C, Crowcroft, NS, Harvey, BJ, Lauwers, A, Gardam, MA. Determining mortality rates attributable to Clostridium difficile infection. Emerg Infect Dis 2012;18:305307.CrossRefGoogle ScholarPubMed
16. Lewis, BB, Buffie, CG, Carter, RA, et al. Loss of microbiota-mediated colonization resistance to Clostridium difficile infection with oral vancomycin compared with metronidazole. J Infect Dis 2015;212:16561665.CrossRefGoogle ScholarPubMed
17. White, H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 1980;48:817838.Google Scholar
18. Huber, P. The behavior of maximum likelihood estimates under nonstandard conditions. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics. Berkeley, CA: University of California Press; 1967:221233.Google Scholar