ABSTRACT
Objectives MALDI-TOF Mass Spectrometry (MS) is a reference method for microbial identification at clinical microbiology laboratories. We have designed and validated a new multiview model based on machine learning from MS spectra to predict antibiotic resistance mechanisms 24 h before phenotypic results are available.
Methods Antibiotic susceptibility of 402 clinical Klebsiella pneumoniae isolates was determined in two collections, discriminating among Wild Type (WT), Extended-Spectrum Beta-Lactamases (ESBL) producers, and ESBL and Carbapenemases (ESBL+CP) producers. Each isolate was subcultured 3 consecutive days and 2 independent spectra were acquired in each replica (6 MS spectra/isolate). Spectra were automatically classified by a kernelized Bayesian factor analysis model (KSSHIBA), using two independent strategies: 1) the model was designed with isolates from a single centre and validated with isolates from the other centre; and 2) in a second stage all isolates were used at the same time for design and validation processes.
Results Higher prediction values were obtained when integrating all isolates with hospital collection of origin information. Our model exhibited higher prediction capability than current state-of-the-art models, particularly in intercollection scenarios because local epidemiology could introduce relevant variables affecting prediction accuracy.
Conclusions Compared to previously reported studies, our model demonstrated the highest ability to predict ESBL and/or CP production in clinical K. pneumoniae isolates and it provided an efficient way to combine information from different centres. Its implementation in microbiological laboratories could improve the detection of multi-drug resistant isolates, optimizing the therapeutic decision.
Competing Interest Statement
The authors have declared no competing interest.