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Decrease in Nosocomial Clostridium difficile–Associated Diarrhea by Restricting Clindamycin Use

Suzanne M. Pear, RN; Theresa H. Williamson, RN; Kristine M. Bettin, BS; Dale N. Gerding, MD; and John N. Galgiani, MD
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From the Veterans Affairs Medical Center and the University of Arizona, Tucson, Arizona; the Veterans Affairs Lakeside Medical Center and Northwestern University, Chicago, Illinois; the Veterans Affairs Medical Center, Minneapolis, Minnesota. Requests for Reprints: John N. Galgiani, MD, Medical Service (111), Veterans Affairs Medical Center, 6th and Ajo, Tucson, AZ 85723. Acknowledgments: The authors thank Gretchen Cloud, Biostatistics Unit, Tumor Institute, University of Alabama at Birmingham, for her assistance with some of the analyses in our report. Grant Support: By the U.S. Department of Veterans Affairs.


Copyright ©2004 by the American College of Physicians


Ann Intern Med. 1994;120(4):272-277. doi:10.7326/0003-4819-120-4-199402150-00003
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Objective: To report the investigation and effective control of a nosocomial epidemic of Clostridium difficile–associated Diarrhea.

Design: Concurrent surveillance for identification of new nosocomial cases, retrospective case–control analysis, and hospital formulary control of antibiotic use.

Setting: University-affiliated Veterans Affairs Medical Center located in southwestern United States.

Patients: Hospitalized patients who developed diarrhea submitted stool specimens for cytotoxin assay. Patients who were positive for cytotoxin were compared with control patients without infection.

Measurements: Isolates of C. difficile were typed by restriction endonuclease analysis. Antimicrobial agent use from hospital pharmacy records and selected patient data from chart review were correlated with frequency of specific laboratory abnormalities.

Results: For 13 months, the monthly incidence of C. difficile infection averaged more than five times that for the previous 21 months. Stool specimens from 34 patients (59%) contained a single strain (restriction enzyme analysis type J7). Clindamycin was statistically associated with the epidemic as shown by the following: clindamycin use at our center compared with national normal values, clindamycin use for years before compared with during the epidemic, monthly use of clindamycin compared with monthly frequency of infection, frequency of infection in patients receiving clindamycin compared with that in patients receiving other antimicrobial agents, and amount of clindamycin used by infected patients compared with that used by control patients. Restricting clindamycin use led to a prompt decrease in infection rate and the type J7 organisms.

Conclusion: A nosocomial epidemic of C. difficile diarrhea was controlled by analysis of antibiotic use patterns and by subsequent restriction of clindamycin.

Figures

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Figure 1.
Number of cases of Clostridium difficile-associated diarrhea detected each month before and after clindamycin restriction.

The epidemic period is shown in black bars and the pre- and postepidemic periods by white bars. The dotted horizontal line represents a historical threshold of 2 SD above the mean. The number of type J7 isolates (identified using restriction endonuclease analysis) compared with the number tested are shown below the bars.

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Figure 2.
Restriction endonuclease analysis patterns of Clostridium difficile strains isolated during and after the epidemic.

The left-hand lane contains λDNA sizing markers; the other lanes contain digested DNA from different patients. The type J7 pattern (identified using restriction endonuclease analysis and shown in lanes 2 to 4) was evident in 59% of the patients, whereas other patients showed 16 other types, including type B12 (lane 5), type N1p (lane 6), and type D1 (lane 7).

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Figure 3.
Relation of monthly clindamycin use from pharmacy records to new cases of Clostridium difficile–associated diarrhea.rPrPC. difficile

The regression line bounded by its 95% CIs indicates a positive relation between either current (left panel; = 0.53, = 0.02) or previous (right panel; = 0.663, = 0.003) months' use and the number of infected patients. The stronger correlation obtained by comparison with previous months' data could be due to a time lag for development and detection of infections.

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