FAIMS may allow rapid and accurate detection of C. difficile in stool samples

VOCs found in headspace afforded impressive discriminatory abilities

Publication information: Bomers, Marije K. et al., Rapid, Accurate, and On-Site Detection of C. diffi cile in Stool Samples, American Journal of Gastroenterology. 100 (2015). DOI: 10.1038/ajg.2015.90

Disease Area: C. difficile

Application: Rapid diagnostics

Sample medium: Feces

Products: Lonestar VOC Analyzer

Analysis approach: FAIMS

Summary:

  • C. difficile is increasingly disruptive in medical environments, requiring extensive infection control measures

  • FAIMS could distinguish C. difficile in stool samples from controls via headspace VOCs with 92% sensitivity and 86% specificity

  • FAIMS could be an ideal point-of-care test, reducing diagnostic and treatment delay

 

Clostridium difficile is a bacterium that can infect the bowel, causing diarrhea. It commonly affects the elderly and patients who have recently received antibiotics. In recent years, the infection has become increasingly prevalent, and more severe forms of the disease have emerged. More serious symptoms include severe pseudomembranous colitis and toxic megacolon. Large hospital outbreaks have required ward closures and extensive infection control measures.

Lonestar is a portable FAIMS (field asymmetric ion mobility spectrometry) instrument that rapidly analyses the chemical composition of gaseous mixtures, e.g. the gases emitted by stool samples. The aim of this study was to test whether FAIMS can accurately distinguish C. difficile-positive stool samples from healthy ones.

Both healthy and C. difficile positive stool samples were analyzed using a Lonestar fitted with an ATLAS headspace sampler. A total of 213 samples were analyzed, of which 71 were C. difficile positive by microbiological analysis.

FAIMS spectrum from C. difficile positive stool sample
FAIMS spectra of a C. difficile positive stool sample

Training and test samples ( n=135) were used to identify which characteristics discriminate between positive and negative samples, and to build machine learning algorithms interpreting these characteristics. The best performing algorithm was then validated on new, blinded validation samples (n=78). The predicted probability of C. difficile (as calculated by the algorithm) was compared with the microbiological test results (direct toxin test and culture).

Using a Random Forest classification algorithm, FAIMS had a high discriminatory ability on the training and test samples (C-statistic 0.91 (95% confidence interval (CI): 0.86–0.97)). When applied to the blinded validation samples, the C-statistic was 0.86 (0.75–0.97). For samples analyzed ≤7 days of collection ( n=76), diagnostic accuracy was even higher (C-statistic: 0.93 (0.85–1.00)). A cutoff value of 0.32 for predicted probability corresponded with a sensitivity of 92.3% (95% CI: 77.4–98.6%) and specificity of 86.0% (78.3–89.3%). For even fresher samples, discriminatory ability further increased.

The receiver operating characteristic (ROC) curve for negative and positive samples.
The receiver operating characteristic (ROC) curve for the (toxin and culture) negative and (toxin and culture) positive samples. 

This pilot study shows that FAIMS analysis of unprocessed stool samples can differentiate between C. difficile-positive and -negative samples with high diagnostic accuracy. FAIMS analysis is also quick, easy to use, relatively cheap, and can be used on the ward. FAIMS may be the ideal point-of-care test for C. difficile in stool samples, reducing diagnostic and treatment delay.

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