ArticleJournal of clinical microbiology2023
Direct Detection of Carbapenemase-Producing Klebsiella pneumoniae by MALDI-TOF Analysis of Full Spectra Applying Machine Learning.
Article in Journal of clinical microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
- Prediction of KPC-producingJournal of clinical microbiology · 2026Article
- [Advances in integrated antimicrobial resistance diagnostics: quantitative, qualitative and AI-driven approaches].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2026Review
- Review
- AI-powered rapid detection of multidrug-resistantBioinformatics advances · 2026Article
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- Enterobacter cloacae complex taxonomic incongruence may lead to misidentification of outbreaks and hinder infection control in real time.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025Article
- Interpretable machine learning of clinical MALDI-TOF spectra discriminates carbapenem-resistantJournal of mass spectrometry and advances in the clinical lab · 2025Article
- CarbaDetector: a machine learning model for detecting carbapenemase-producing Enterobacterales from disk diffusion tests.Nature communications · 2025Article
- A Review on the Current and Future State of Urinary Tract Infection Diagnostics.International journal of molecular sciences · 2025Review
- Rapid detection of antibiotic resistance in Burkholderia pseudomallei using MALDI-TOF mass spectrometry.Scientific reports · 2025Article
- Rapid Identification and Typing of Carbapenem-Resistant Klebsiella pneumoniae Using MALDI-TOF MS and Machine Learning.Microbial biotechnology · 2025Article
- Analysis of high-molecular-weight proteins using MALDI-TOF MS and machine learning for the differentiation of clinically relevant Clostridioides difficile ribotypes.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025Article
- Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning models.BMC microbiology · 2025Article
- MALDI-TOF MS in conjunction with machine learning: toward a new era for antimicrobial susceptibility testing.Frontiers in cellular and infection microbiology · 2025Review
- Development and validation of a quick, automated, and reproducible ATR FT-IR spectroscopy machine-learning model forJournal of clinical microbiology · 2024Article
- Poor Sensitivity of the MALDI BiotyperAntibiotics (Basel, Switzerland) · 2023Article
- MALDI-TOF MS-Based KPC Direct Detection from Patients' Positive Blood Culture Bottles, Short-Term Cultures, and Colonies at the Hospital.Pathogens (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MALDI-TOF MS is considered to be an important tool for the future development of rapid microbiological techniques. We propose the application of MALDI-TOF MS as a dual technique for the identification of bacteria and the detection of resistance, with no extra hands-on procedures. We have developed a machine learning approach that uses the random forest algorithm for the direct prediction of carbapenemase-producing Klebsiella pneumoniae (CPK) isolates, based on the spectra of complete cells. For this purpose, we used a database of 4,547 mass spectra profiles, including 715 unduplicated clinical isolates that are represented by 324 CPK with 37 different ST. The impact of the culture medium was determinant in the CPK prediction, being that the isolates were tested and cultured in the same media, compared to the isolates used to build the model (blood agar). The proposed method has an accuracy of 97.83% for the prediction of CPK and an accuracy of 95.24% for the prediction of OXA-48 or KPC carriage. For the CPK prediction, the RF algorithm yielded a value of 1.00 for both the area under the receiver operating characteristic curve and the area under the precision-recall curve. The contribution of individual mass peaks to the CPK prediction was determined using Shapley values, which revealed that the complete proteome, rather than a series of mass peaks or potential biomarkers (as previously suggested), is responsible for the algorithm-based classification. Thus, the use of the full spectrum, as proposed here, with a pattern-matching analytical algorithm produced the best outcome. The use of MALDI-TOF MS coupled with machine learning algorithm processing enabled the identification of CPK isolates within only a few minutes, thereby reducing the time to detection of resistance.
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