MALDI-TOF MS identification and antimicrobial susceptibility testing directly from positive enrichment broth

Tré-Hardy, Marie; Lambert, Barbara; Despas, Noémie; Bressant, Florian; Laurenzano, Clémentine; Rodríguez-Villalobos, Hector; Verroken, Alexia

doi:10.1016/j.mimet.2017.07.012

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…In general this takes 1 day for growth and an additional day for identification and susceptibility testing. During the last 10 years we could witness the triumph of MALDI-TOF MS over biochemistry for bacterial identification from bacterial cultures (solid or liquid) (Clark et al, 2013 ; Patel, 2015 ; Singhal et al, 2015 ; Angeletti, 2016 ; Arca-Suárez et al, 2017 ; Tré-Hardy et al, 2017 ) or directly from patient material (Sandalakis et al, 2017 ). One of its main advantages is that identification using MALDI-TOF MS takes minutes.…”

Section: Background and Aim Of The Review: Why Do We Need Susceptibilmentioning

confidence: 99%

Susceptibility Testing of Bacteria Using Maldi-Tof Mass Spectrometry

Burckhardt

Zimmermann

2018

Front. Microbiol.

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Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) was introduced into the microbiological routine more than 10 years ago. Since then it has almost replaced biochemical identification. It is unrivaled in terms of accuracy and cost. From a laboratory's perspective it would be an ideal method to replace classic susceptibility testing, that is Kirby-Baur agardiffusion or determination of minimal inhibitory concentrations (MICs). First reports on possible assays for susceptibility testing are more than 10 years old. However, the developments during the last 5 years were substantial. This review focuses with some exceptions on the progress, which was achieved during the last decade.

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Section: Background and Aim Of The Review: Why Do We Need Susceptibilmentioning

confidence: 99%

Susceptibility Testing of Bacteria Using Maldi-Tof Mass Spectrometry

Burckhardt

Zimmermann

2018

Front. Microbiol.

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“…Nowadays, utilizing the MALDI-TOF MS could significantly shorten the testing time and lower the inspection cost. Achievements have been made in both identifying antibiotic-resistant bacteria from laboratory plate cultures and directly from the specimens of patients ( Clark et al, 2013 ; Patel, 2015 ; Singhal et al, 2015 ; Angeletti, 2016 ; Arca-Suárez et al, 2017 ; Sandalakis et al, 2017 ; Tré-Hardy et al, 2017 ). In 2016, spectrum peak at 11.109 m/z was confirmed related to plasmid-mediated CRKP with gene ( Gaibani et al, 2016 ).…”

Section: Background and Introductionmentioning

confidence: 99%

Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance in Klebsiella pneumoniae Using Machine Learning Methods

Wang

et al. 2022

Front. Microbiol.

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Klebsiella pneumoniae is one of the most common causes of hospital- and community-acquired pneumoniae. Resistance to the extensively used quinolone antibiotic, such as ciprofloxacin, has increased in Klebsiella pneumoniae, which leads to the increase in the risk of initial antibiotic selection for Klebsiella pneumoniae treatment. Rapid and precise identification of ciprofloxacin-resistant Klebsiella pneumoniae (CIRKP) is essential for clinical therapy. Nowadays, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is another approach to discover antibiotic-resistant bacteria due to its shorter inspection time and lower cost than other current methods. Machine learning methods are introduced to assist in discovering significant biomarkers from MALDI-TOF MS data and construct prediction models for rapid antibiotic resistance identification. This study examined 16,997 samples taken from June 2013 to February 2018 as part of a longitudinal investigation done by Change Gung Memorial Hospitals (CGMH) at the Linkou branch. We applied traditional statistical approaches to identify significant biomarkers, and then a comparison was made between high-importance features in machine learning models and statistically selected features. Large-scale data guaranteed the statistical power of selected biomarkers. Besides, clustering analysis analyzed suspicious sub-strains to provide potential information about their influences on antibiotic resistance identification performance. For modeling, to simulate the real antibiotic resistance predicting challenges, we included basic information about patients and the types of specimen carriers into the model construction process and separated the training and testing sets by time. Final performance reached an area under the receiver operating characteristic curve (AUC) of 0.89 for support vector machine (SVM) and extreme gradient boosting (XGB) models. Also, logistic regression and random forest models both achieved AUC around 0.85. In conclusion, models provide sensitive forecasts of CIRKP, which may aid in early antibiotic selection against Klebsiella pneumoniae. The suspicious sub-strains could affect the model performance. Further works could keep on searching for methods to improve both the model accuracy and stability.

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“…Several studies highlight the need of the shortening the sample preparation and incubation time for MALDI-TOF MS but until to date, only a few direct measurement methods have been developed [42,43]. Direct identification from the selective enrichment has successfully been adapted to detect, among others, Salmonella sp.…”

Section: Development Of a Maldi-tof Ms Methods For Rapid Detection Of S Agalactiae Colonization Of Pregnant Womenmentioning

confidence: 99%

Streptococcus agalactiae screening of pregnant women and detection of methicillin-resistant Staphylococcus aureus using MALDI-TOF mass spectrometry

Ábrók¹

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MALDI-TOF MS identification and antimicrobial susceptibility testing directly from positive enrichment broth

Cited by 6 publications

References 11 publications

Susceptibility Testing of Bacteria Using Maldi-Tof Mass Spectrometry

Susceptibility Testing of Bacteria Using Maldi-Tof Mass Spectrometry

Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance in Klebsiella pneumoniae Using Machine Learning Methods

Streptococcus agalactiae screening of pregnant women and detection of methicillin-resistant Staphylococcus aureus using MALDI-TOF mass spectrometry

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