Implementation of an Algorithm Decision Tree Increases Compliance with Clostridium difficile Testing

Abstract

BACKGROUND Clostridium difficile infection (CDI) is life-threatening disease that causes diarrhea. Risk factors include hospitalized patients who are receiving antibiotics. Laboratory tests commonly used to diagnose CDI include molecular tests, which is highly sensitive and specific, and antigen detection, which uses a two-step testing algorithm. While molecular tests do provide rapid identification, they do not differentiate between colonization and infection, and as a result patients may be unnecessarily treated for CDI. The objective of this initiative was to simplify the decision-making process for the front-line staff, by creating an algorithm to aid in proper CDI testing. METHODS A CDI algorithm was implemented in January 2017 and it consisted of a flowchart with check boxes. If all boxes were checked, then patient met criteria for testing. All specimens sent to the laboratory required the algorithm filled out appropriately with two signatures. If the algorithm was not submitted with the specimen, the laboratory called the unit to request a completed algorithm. The study compared the total number of tests performed and hospital-onset (HO) cases (Pre: January-December 2016, Post: January-December 2017). RESULTS During the pre-intervention period, a total of 1873 tests were performed, and of those, 70 were HO cases. During the post-intervention period, a total of 1358 tests were performed, and of those, 39 were HO cases. Total number of tests performed was reduced by 27% and overall HO cases was reduced by 44% as a result of the intervention. CONCLUSIONS This initiative demonstrates that creating a CDI algorithm, increases compliance with CDI testing. The results were validated with a decrease in the overall number of tests performed and in HO cases. Use of the CDI algorithm decreases unnecessary testing, decreases unnecessary treatment for colonized patients, and increases patient safety.