title: Overcoming barriers to effective recognition and diagnosis of Clostridium difficile infection.
abstract: With the frequency of cases of Clostridium difficile infection (CDI) increasing in many developed countries, accurate and reliable laboratory diagnosis of CDI is more important than ever. However, the diagnosis of CDI has been handicapped by the existence of two reference standards, one of which detects C. difficile toxin (cytotoxin assay) and the other only toxigenic strains (cytotoxigenic culture). Being relatively slow and laborious to perform, these reference methods were largely abandoned as routine diagnostic methods for toxin detection in favour of stand-alone rapid enzyme immunoassays (EIAs), which have suboptimal sensitivity and specificity. The management of CDI is undermined by high rates of both false-positive and false-negative test results. More recently developed nucleic acid amplification tests (NAATs) for toxin gene detection offer improved sensitivity over immunoassays, but fail to discriminate between CDI and asymptomatic colonization with C. difficile, and have clear drawbacks as stand-alone diagnostic tests. Two-step or three-step diagnostic algorithms have been proposed as a solution. In a large study of the effectiveness of currently available tests, a diagnostic algorithm was developed that combines available tests to more effectively distinguish patients with CDI from uninfected patients. This two-test protocol, which is now used in National Health Service laboratories in England, comprises an EIA for glutamate dehydrogenase detection or NAATs for toxin gene detection, followed by a relatively sensitive toxin EIA. This algorithm also identifies 'potential C. difficile excretors', individuals with diarrhoeal samples that contain C. difficile but without demonstrable toxin, who may be a source of transmission of C. difficile to susceptible patients.
authors.affiliation: Department of Microbiology, Old Medical School, Leeds Teaching Hospitals NHS Trust & University of Leeds, Leeds General Infirmary, Leeds, UK. mark.wilcox@leedsth.nhs.uk
authors.forename: M H
authors.initials: MH
authors.lastname: Wilcox
mesh_terms: D000465:Algorithms; D015373:Bacterial Typing Techniques; D016360:Clostridioides difficile / Q000145:classification / Q000235:genetics / Q000302:isolation & purification*; D003015:Clostridium Infections / Q000175:diagnosis* / Q000382:microbiology; D006801:Humans; D007124:Immunoenzyme Techniques; D016133:Polymerase Chain Reaction