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A Protein Microarray Assay for Serological Determination of Antigen-specific Antibody Responses Following Clostridium difficile Infection
Published on: June 15, 2018
How to: diagnose infection caused by Clostridium difficile
C Gateau1, J Couturier2, J Coia3
1National Reference Laboratory for C. difficile, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Antoine, Paris, France.
Background:
Clostridium difficile is recognized as the major agent responsible for nosocomial diarrhoea. In the context of recent increase in the incidence and severity of C. difficile infections (CDI), an accurate diagnosis is essential for optimal treatment and prevention, but continues to be challenging.
Aims:
The present article reviews each key step of CDI diagnosis including stool selection, methods and strategies used, and interpretation of the results.
Sources:
The most recent guidelines for CDI diagnosis published by scientific societies were reviewed.
Content:
CDI diagnosis is based on clinical presentation and laboratory tests confirming the presence of toxigenic strain or toxins in stools. Stool selection is crucial and can be improved by implementing rejection criteria and a strict policy for appropriate testing. Multiple laboratory tests detecting different targets (free toxin or presence of a potentially toxigenic strain) are commercially available. However, none of these tests combine high sensitivity and specificity to diagnose CDI, low hands-on time and low cost. An optimized diagnosis can be achieved by implementing a two- or three-step algorithm. Algorithms currently recommended by the ESCMID comprise a screening test with high sensitivity followed by a more specific test to detect free toxins. Presence of free toxins in stools has been shown to better correlate with severe outcome whereas nucleic acid amplification tests may lead to an over-diagnosis by detecting asymptomatic carriers of a toxigenic strain.
Implication:
To date, no single test can accurately diagnose CDI. Guidelines from the ESCMID recommend a two- or three-step algorithm for optimal CDI detection.
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