Impact of a C. difficile infection (CDI) reduction bundle and its components on CDI diagnosis and prevention

Am J Infect Control. 2021 Mar;49(3):319-326. doi: 10.1016/j.ajic.2020.10.020.

Abstract

Background: Published bundles to reduce Clostridioides difficile Infection (CDI) frequently lack information on compliance with individual elements. We piloted a computerized clinical decision support-based intervention bundle and conducted detailed evaluation of several intervention-related measures.

Methods: A quasi-experimental study of a bundled intervention was performed at 2 acute care community hospitals in Maryland. The bundle had five components: (1) timely placement in enteric precautions, (2) appropriate CDI testing, (3) reducing proton-pump inhibitor (PPI) use, (4) reducing high-CDI risk antibiotic use, and (5) optimizing use of a sporicidal agent for environmental cleaning. Chi-square and Kruskal-Wallis tests were used to compare measure differences. An interrupted time series analysis was used to evaluate impact on hospital-onset (HO)-CDI.

Results: Placement of CDI suspects in enteric precautions before test results did not change. Only hospital B decreased the frequency of CDI testing and reduced inappropriate testing related to laxative use. Both hospitals reduced the use of PPI and high-risk antibiotics. A 75% decrease in HO-CDI immediately postimplementation was observed for hospital B only.

Conclusion: A CDI reduction bundle showed variable impact on relevant measures. Hospital-specific differential uptake of bundle elements may explain differences in effectiveness, and emphasizes the importance of measuring processes and intermediate outcomes.

Keywords: Antibiotic resistance; Computerized clinical decision support; Hospital infection control.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Clostridioides difficile*
  • Clostridium Infections* / diagnosis
  • Clostridium Infections* / prevention & control
  • Cross Infection* / diagnosis
  • Cross Infection* / prevention & control
  • Hospitals
  • Humans
  • Maryland