Abstract
Early use of infection control methods is critical for preventing the spread of antimicrobial resistance. Whole-genome sequencing (WGS) is considered the gold standard for investigating outbreaks; however, the turnaround time is usually too long for clinical decision-making and the method is also costly. The aim of this study was to evaluate the performance of Fourier transform infrared (FTIR) and artificial intelligence tools as a first-line typing tool for typing carbapenemase-producing Klebsiella pneumoniae (CPK) in the hospital setting. For this purpose, we analyzed 365 CPK isolates from two tertiary hospitals in Spain in parallel by applying unsupervised principal component analysis (PCA) and supervised algorithms (artificial neural network [ANN], support vector machine [SVM] linear, SVM radial basis function in the IR Biotyper software, and random forest in the Clover MSDAS software). Concordance with FTIR clustering considering the sequence type (ST) and the clonal cluster, obtained by cgMLST for reference purposes, was measured using the adjusted Wallace index (AWI), yielding values of 0.611 and 0.652, respectively. Different regions of the spectra were studied in relation to repeatability and reproducibility, and the polysaccharides region proved the best for FTIR differential analysis. The best results for accuracy were obtained using the ANN algorithm in the IR Biotyper software, with 80.5% of correct prediction. Regarding accuracy, the poorest results were obtained for isolates belonging to ST392 (55.5%) and the best results for ST307 (94.4%). The findings demonstrate the utility of the FTIR method as a rapid, inexpensive, first-line typing tool for detecting CPK, preserving WGS for confirmation and further characterization.