Clostridium difficile colonizes the gastrointestinal (GI) tract, resulting in either asymptomatic carriage or a spectrum of diarrheal illness. If clinical suspicion for C. difficile is low, stool samples are often submitted for analysis by multiplex molecular assays capable of detecting multiple GI pathogens, and some institutions do not report this organism due to concerns for high false-positive rates. Since clinical disease correlates with organism burden and molecular assays yield quantitative data, we hypothesized that numerical cutoffs could be utilized to improve the specificity of the Luminex xTAG GI pathogen panel (GPP) for C. difficile infection. Analysis of cotested liquid stool samples (n = 1,105) identified a GPP median fluorescence intensity (MFI) value cutoff of ≥1,200 to be predictive of two-step algorithm (2-SA; 96.4% concordance) and toxin enzyme immunoassay (EIA) positivity. Application of this cutoff to a second cotested data set (n = 1,428) yielded 96.5% concordance. To determine test performance characteristics, concordant results were deemed positive or negative, and discordant results were adjudicated via chart review. Test performance characteristics for the MFI cutoff of ≥150 (standard), MFI cutoff of ≥1,200, and 2-SA were as follows (respectively): concordance, 95, 96, and 97%; sensitivity, 93, 78, and 90%; specificity, 95, 98, and 98%; positive predictive value, 67, 82, and 81%;, and negative predictive value, 99, 98, and 99%. To capture the high sensitivity for organism detection (MFI of ≥150) and high specificity for active infection (MFI of ≥1,200), we developed and applied a reporting algorithm to interpret GPP data from patients (n = 563) with clinician orders only for syndromic panel testing, thus enabling accurate reporting of C. difficile for 95% of samples (514 negative and 5 true positives) irrespective of initial clinical suspicion and without the need for additional testing.
Keywords: Clostridium difficile; community-associated infections; quantitative thresholds; syndromic panels; two-step algorithm.
Copyright © 2018 American Society for Microbiology.