Automatic antibiotic resistance prediction inKlebsiella pneumoniaebased on MALDI-TOF mass spectra

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 center and validated with isolates from the other center; 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 centers. Its implementation in microbiological laboratories could improve the detection of multi-drug resistant isolates, optimizing the therapeutic decision.