Rapid detection of<i>cfiA</i>metallo-β-lactamase-producing<i>Bacteroides fragilis</i>by the combination of MALDI-TOF MS and CarbaNP

Ho, Pak‐Leung; Yau, Chong-Yee; Ho, Lok-Yan; Chen, Jonathan Hon-Kwan; Lai, Eileen L.; Lo, Stephanie; Tse, Cindy Wing-Sze; Chow, Kam-Pui

doi:10.1136/jclinpath-2017-204335

Cited by 30 publications

(22 citation statements)

References 34 publications

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“…The lengthy nature of conventional methods for bacterial identification and susceptibility testing purposes has resulted in the empirical use of broad-spectrum antibiotics and led to the spread of resistance [85]. Aiming at rapid and reliable detection of AMR, numerous studies have recently been reported on the use of ML algorithms in combination with the output of several analytical techniques, such as MALDI-TOF MS [89,96,[110][111][112][113][114][115][116][117][118], vibrational spectroscopy [102,103,119], whole-genome sequencing [120][121][122][123][124], a microscopy-based platform [125], and acousticenhanced flow cytometry [126].…”

Section: Detection Of Antimicrobial Resistancementioning

confidence: 99%

“…Using an RF model on MALDI-TOF MS spectra, Huang et al [115] were able to correctly identify 93% of carbapenem-resistant and all carbapenem-sensitive Klebsiella pneumoniae isolates. Moreover, successful systems using a combination of MALDI-TOF MS data and ML algorithms were recently reported for the detection of extended-spectrum betalactamase-producing Escherichia coli (E. coli) strains [117], rapid detection of cfiA metallo-b-lactamase-producing Bacteroides fragilis strains [118], and identification of fluconazole resistance in Candida albicans strains [96].…”

Section: Detection Of Antimicrobial Resistancementioning

confidence: 99%

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Recent evolutions of machine learning applications in clinical laboratory medicine

Bruyne

Speeckaert

Biesen

et al. 2020

Critical Reviews in Clinical Laboratory Sciences

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Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.

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Section: Detection Of Antimicrobial Resistancementioning

confidence: 99%

Section: Detection Of Antimicrobial Resistancementioning

confidence: 99%

Recent evolutions of machine learning applications in clinical laboratory medicine

Bruyne

Speeckaert

Biesen

et al. 2020

Critical Reviews in Clinical Laboratory Sciences

View full text Add to dashboard Cite

show abstract

“…This approach has been successfully applied to Gram-negative bacteria, mycobacteria and to Staphylococcus aureus. The parallel use of MALDI-TOF MS and of a vector machine model as a supporting tool for the generation of a reliable algorithm (ClinProTools) has allowed the rapid identification of Bacillus fragilis and the differentiation of cfiA-positive and cfiAnegative subgroups [13].…”

Section: Antibiotic Resistance Determinationmentioning

confidence: 99%

Use of MALDI-TOF mass spectrometry in the battle against bacterial infectious diseases: recent achievements and future perspectives

Psaroulaki

Chochlakis

2018

Expert Review of Proteomics

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“…Having antimicrobial resistance phenotyping at the time of MALDI-TOF measurement would eliminate this critical time delay. Previous work ( Ho et al , 2017 ; Mather et al , 2016 ; Sogawa et al , 2017 ) already recognized the potential of applying machine learning to MALDI-TOF mass spectra for antibiotic resistance prediction. However, the scope of these studies is limited by (i) relatively small numbers of spectra of often less than 100 isolates, (ii) the use of machine learning algorithms that are not specifically adapted to the application problem, and (iii) a focus on detecting single peaks (or small subsets of peaks) for the purpose of providing a full discrimination between resistant and susceptible samples.…”

Section: Introductionmentioning

confidence: 99%

Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra

et al. 2020

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Motivation Microbial species identification based on matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has become a standard tool in clinical microbiology. The resulting MALDI-TOF mass spectra also harbour the potential to deliver prediction results for other phenotypes, such as antibiotic resistance. However, the development of machine learning algorithms specifically tailored to MALDI-TOF MS-based phenotype prediction is still in its infancy. Moreover, current spectral pre-processing typically involves a parameter-heavy chain of operations without analyzing their influence on the prediction results. In addition, classification algorithms lack quantification of uncertainty, which is indispensable for predictions potentially influencing patient treatment. Results We present a novel prediction method for antimicrobial resistance based on MALDI-TOF mass spectra. First, we compare the complex conventional pre-processing to a new approach that exploits topological information and requires only a single parameter, namely the number of peaks of a spectrum to keep. Second, we introduce PIKE, the peak information kernel, a similarity measure specifically tailored to MALDI-TOF mass spectra which, combined with a Gaussian process classifier, provides well-calibrated uncertainty estimates about predictions. We demonstrate the utility of our approach by predicting antibiotic resistance of three clinically highly relevant bacterial species. Our method consistently outperforms competitor approaches, while demonstrating improved performance and security by rejecting out-of-distribution samples, such as bacterial species that are not represented in the training data. Ultimately, our method could contribute to an earlier and precise antimicrobial treatment in clinical patient care. Availability and implementation We make our code publicly available as an easy-to-use Python package under https://github.com/BorgwardtLab/maldi_PIKE.

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Rapid detection ofcfiAmetallo-β-lactamase-producingBacteroides fragilisby the combination of MALDI-TOF MS and CarbaNP

Abstract: The combination of MALDI-TOF MS and the CarbaNP assay can be applied in diagnostic clinical laboratory for rapid identification of with IS element-activated gene.

Cited by 30 publications

References 34 publications

Recent evolutions of machine learning applications in clinical laboratory medicine

Recent evolutions of machine learning applications in clinical laboratory medicine

Use of MALDI-TOF mass spectrometry in the battle against bacterial infectious diseases: recent achievements and future perspectives

Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra

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