Automatic antibiotic resistance prediction in Klebsiella pneumoniae based on MALDI-TOF mass spectra. (February 2023)

• Record Type: Journal Article
• Authors: Guerrero-López, Alejandro; Sevilla-Salcedo, Carlos; Candela, Ana; Hernández-García, Marta; Cercenado, Emilia; Olmos, Pablo M.; Cantón, Rafael; Muñoz, Patricia; Gómez-Verdejo, Vanessa; del Campo, Rosa; Rodríguez-Sánchez, Belén
• Is Part Of: Engineering applications of artificial intelligence. Volume 118(2023)
• Publication Date: 2023-02
• Subjects: 00-01 -- 99-00; Bayesian model -- ESBL -- CP -- MALDI-TOF -- Antibiotic Resistance -- Klebsiella pneumoniae; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105644
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Matrix-Assisted Laser Desorption Ionization Time-Of-Flight (MALDI-TOF) Mass Spectrometry (MS) is a reference method for microbial identification and it can be used to predict Antibiotic Resistance (AR) when combined with artificial intelligence methods. However, current solutions need time-costly preprocessing steps, are difficult to reproduce due to hyperparameter tuning, are hardly interpretable, and do not pay attention to epidemiological differences inherent to data coming from different centres, which can be critical. We propose using a multi-view heterogeneous Bayesian model (KSSHIBA) for the prediction of AR using MALDI-TOF MS data together with their epidemiological differences. KSSHIBA is the first model that removes the ad-hoc preprocessing steps that work with raw MALDI-TOF data. In addition, due to its Bayesian probabilistic nature, it does not require hyperparameter tuning, provides interpretable results, and allows exploiting local epidemiological differences between data sources. To test the proposal, we used data from 402 Klebsiella pneumoniae isolates coming from two different domains and 20 different hospitals located in Spain and Portugal. KSSHIBA outperforms current state-of-the-art approaches in antibiotic susceptibility prediction, obtaining a 0.78 AUC score in Wild Type classification and a 0.90 AUC score in Extended-Spectrum Beta-Lactamases (ESBL)+Carbapenemases (CP)-producers. The proposal consistently removes the need for ad-hoc preprocessing by working with raw MALDI-TOF data, which, in turn, reduces the time needed to obtain the results of the resistance mechanism in microbiological laboratories. The proposed model implementation as well as both data domains are publicly available. Graphical abstract: Highlights: Antibiotic Resistance in K. pneumoniae is predicted by using KSSHIBA. Raw MALDI-TOF MS data is used, getting rid of time-costly external preprocessing. Double dimensionality reduction is performed by kernel methods and factor analysis. Hyperparameter tuning is eliminated using a Bayesian model. The bacteria epidemiological differences are tackled by a multiview model.