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OBJECTIVES:A rapid test to diagnose Clostridium difficile infection (CDI) on hospital wards could minimize common but critical diagnostic delay. Field asymmetric ion mobility spectrometry (FAIMS) is a portable mass spectrometry instrument that quickly analyses the chemical composition of gaseous mixtures (e.g., above a stool sample). Can FAIMS accurately distinguish C. difficile-positive from -negative stool samples?METHODS:We analyzed 213 stool samples with FAIMS, of which 71 were C. difficile positive by microbiological analysis. The samples were divided into training, test, and validation samples. We used the training and test samples (n=135) to identify which sample characteristics discriminate between positive and negative samples, and to build machine learning algorithms interpreting these characteristics. The best performing algorithm was then prospectively validated on new, blinded validation samples (n=78). The predicted probability of CDI (as calculated by the algorithm) was compared with the microbiological test results (direct toxin test and culture).RESULTS: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.CONCLUSIONS:FAIMS analysis of unprocessed stool samples can differentiate between C. difficile-positive and -negative samples with high diagnostic accuracy.