Clinically Applicable System for Rapidly Predicting Enterococcus faecium Susceptibility to Vancomycin

MALDI-TOF MS profiling to predict resistance or biofilm production in gram-positive ESKAPE pathogens from healthcare-associated infections

Antimicrobial resistance and biofilm production in healthcare-associated infections is a health issue worldwide. This study aimed to identify potential biomarker peaks for resistance or biofilm production in ESKAPE pathogens using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Antimicrobial susceptibility and biofilm production were assessed on selected isolates. Biomarker peaks were identified using MALDI Biotyper and ClinProTools software. Among resistant strains, 90.0 % were carbapenem-resistant Acinetobacter baumannii (CRAB), 39.0 % were methicillin-resistant Staphylococcus aureus (MRSA), 17.98 % were multidrug-resistant (MDR) Pseudomonas aeruginosa, 21.6 % were vancomycinresistant Enterococcus (VRE), and 2.55 % were carbapenem-resistant Enterobacterales (CRE). Biofilm production was 40.0 % in VRE and 45.8 % in MRSA. Although no potential biomarker peaks for biofilm production were detected, several potential biomarker peaks for drug resistance in VRE (n=5), MRSA (n=4), and MDR P. aeruginosa (n=4) were detected, suggesting avenues for the development of rapid diagnostic tools.

Machine learning and matrix-assisted laser desorption/ionization time-of-flight mass spectra for antimicrobial resistance prediction: A systematic review of recent advancements and future development

BACKGROUND

The use of matrix-assisted laser desorption/ionization time-of-flight mass spectra (MALDI-TOF MS) combined with machine learning techniques has recently emerged as a method to address the public health crisis of antimicrobial resistance. This systematic review, conducted following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, aims to evaluate the current state of the art in using machine learning for the detection and classification of antimicrobial resistance from MALDI-TOF mass spectrometry data.

METHODS

A comprehensive review of the literature on machine learning applications for antimicrobial resistance detection was performed using databases such as Web Of Science, Scopus, ScienceDirect, IEEE Xplore, and PubMed. Only original articles in English were included. Studies applying machine learning without using MALDI-TOF mass spectra were excluded.

RESULTS

Forty studies met the inclusion criteria. Staphylococcus aureus, Klebsiella pneumoniae and Escherichia coli were the most frequently cited bacteria. The antibiotics resistance most studied corresponds to methicillin for S. aureus, cephalosporins for K. pneumoniae, and aminoglycosides for E. coli. Random forest, support vector machine and logistic regression were the most employed algorithms to predict antimicrobial resistance. Additionally, seven studies reported using artificial neural networks. Most studies reported metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (AUROC) above 0.80.

CONCLUSIONS

Our study indicates that random forest, support vector machine, and logistic regression are effective for predicting antimicrobial resistance using MALDI-TOF MS data. Recent studies also highlight the potential of deep learning techniques in this area. We recommend further exploration of deep learning and multi-label supervised learning for comprehensive antibiotic resistance prediction in clinical practice.

Artificial Intelligence to Close the Gap between Pharmacokinetic/Pharmacodynamic Targets and Clinical Outcomes in Critically Ill Patients: A Narrative Review on Beta Lactams

Antimicrobial dosing can be a complex challenge. Although a solid rationale exists for a link between antibiotic exposure and outcome, conflicting data suggest a poor correlation between pharmacokinetic/pharmacodynamic targets and infection control. Different reasons may lead to this discrepancy: poor tissue penetration by β-lactams due to inflammation and inadequate tissue perfusion; different bacterial response to antibiotics and biofilms; heterogeneity of the host's immune response and drug metabolism; bacterial tolerance and acquisition of resistance during therapy. Consequently, either a fixed dose of antibiotics or a fixed target concentration may be doomed to fail. The role of biomarkers in understanding and monitoring host response to infection is also incompletely defined. Nowadays, with the ever-growing stream of data collected in hospitals, utilizing the most efficient analytical tools may lead to better personalization of therapy. The rise of artificial intelligence and machine learning has allowed large amounts of data to be rapidly accessed and analyzed. These unsupervised learning models can apprehend the data structure and identify homogeneous subgroups, facilitating the individualization of medical interventions. This review aims to discuss the challenges of β-lactam dosing, focusing on its pharmacodynamics and the new challenges and opportunities arising from integrating machine learning algorithms to personalize patient treatment.

Machine Learning Assisted MALDI Mass Spectrometry for Rapid Antimicrobial Resistance Prediction in Clinicals

Antimicrobial susceptibility testing (AST) plays a critical role in assessing the resistance of individual microbial isolates and determining appropriate antimicrobial therapeutics in a timely manner. However, conventional AST normally takes up to 72 h for obtaining the results. In healthcare facilities, the global distribution of vancomycin-resistant Enterococcus fecium (VRE) infections underscores the importance of rapidly determining VRE isolates. Here, we developed an integrated antimicrobial resistance (AMR) screening strategy by combining matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) with machine learning to rapidly predict VRE from clinical samples. Over 400 VRE and vancomycin-susceptible E. faecium (VSE) isolates were analyzed using MALDI-MS at different culture times, and a comprehensive dataset comprising 2388 mass spectra was generated. Algorithms including the support vector machine (SVM), SVM with L1-norm, logistic regression, and multilayer perceptron (MLP) were utilized to train the classification model. Validation on a panel of clinical samples (external patients) resulted in a prediction accuracy of 78.07%, 80.26%, 78.95%, and 80.54% for each algorithm, respectively, all with an AUROC above 0.80. Furthermore, a total of 33 mass regions were recognized as influential features and elucidated, contributing to the differences between VRE and VSE through the Shapley value and accuracy, while tandem mass spectrometry was employed to identify the specific peaks among them. Certain ribosomal proteins, such as A0A133N352 and R2Q455, were tentatively identified. Overall, the integration of machine learning with MALDI-MS has enabled the rapid determination of bacterial antibiotic resistance, greatly expediting the usage of appropriate antibiotics.

Rapid detection of ceftriaxone-resistant Salmonella by matrix-assisted laser desorption-ionization time-of-flight mass spectrometry combined with the ratio of optical density

BACKGROUND

The increased resistance rate of Salmonella to third-generation cephalosporins represented by ceftriaxone (CRO) may result in the failure of the empirical use of third-generation cephalosporins for the treatment of Salmonella infection in children. The present study was conducted to evaluate a novel method for the rapid detection of CRO-resistant Salmonella (CRS).

METHODS

We introduced the concept of the ratio of optical density (ROD) with and without CRO and combined it with matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI-TOF MS) to establish a new protocol for the rapid detection of CRS.

RESULTS

The optimal incubation time and CRO concentration determined by the model strain test were 2 h and 8 µg/ml, respectively. We then conducted confirmatory tests on 120 clinical strains. According to the receiver operating characteristic curve analysis, the ROD cutoff value for distinguishing CRS and non-CRS strains was 0.818 [area under the curve: 1.000; 95% confidence interval: 0.970-1.000; sensitivity: 100.00%; specificity: 100%; P < 10- 3].

CONCLUSIONS

In conclusion, the protocol for the combined ROD and MALDI-TOF MS represents a rapid, accurate, and economical method for the detection of CRS.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review

The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.

Rapid AMR prediction in Pseudomonas aeruginosa combining MALDI-TOF MS with DNN model

BACKGROUND

Pseudomonas aeruginosa is a significant clinical pathogen that poses a substantial threat due to its extensive drug resistance. The rapid and precise identification of this resistance is crucial for effective clinical treatment. Although matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been used for antibiotic susceptibility differentiation of some bacteria in recent years, the genetic diversity of P. aeruginosa complicates population analysis. Rapid identification of antimicrobial resistance (AMR) in P. aeruginosa based on a large amount of MALDI-TOF-MS data has not yet been reported. In this study, we employed publicly available datasets for P. aeruginosa, which contain data on bacterial resistance and MALDI-TOF-MS spectra. We introduced a deep neural network model, synergized with a strategic sampling approach (SMOTEENN) to construct a predictive framework for AMR of three widely used antibiotics.

RESULTS

The framework achieved area under the curve values of 90%, 85%, and 77% for Tobramycin, Cefepime, and Meropenem, respectively, surpassing conventional classifiers. Notably, random forest algorithm was used to assess the significance of features and post-hoc analysis was conducted on the top 10 features using Cohen's d. This analysis revealed moderate effect sizes (d = 0.5-0.8) in Tobramycin and Cefepime models. Finally, putative AMR biomarkers were identified in this study.

CONCLUSIONS

This work presented an AMR prediction tool specifically designed for P. aeruginosa, which offers a hopeful pathway for clinical decision-making.

Ampicillin-susceptible Enterococcus faecium infections: clinical features, causal clades, and contribution of MALDI-TOF to early detection

Enterococcus faecium, a common resident of the human gastrointestinal tract, is also a major pathogen. Prompt initiation of appropriate treatment is essential to improve patient outcome in disseminated E. faecium infections. However, ampicillin resistance is frequent in this species, rendering treatment difficult. We used a comprehensive approach, including clinical data review, whole-genome sequencing, and mass spectrometry, to characterize ampicillin-susceptible (EFM-S) and ampicillin-resistant (EFM-R) isolates. We included all patients with culture-confirmed E. faecium infection attending our hospital over a 16-month period. A comparison of 32 patients infected with EFM-S strains and 251 patients infected with EFM-R strains revealed that EFM-R isolates were strongly associated with a longer hospital stay, history of prior hospitalization, and the carriage of multidrug-resistant organisms. An analysis of the genomes of 26 EFM-S and 26 EFM-R isolates from paired patients revealed a population structure almost perfectly matching ampicillin susceptibility, with resistant isolates in clade A1, and susceptible isolates in clades A2 and B. The clade B and A2 isolates mostly came from digestive or biliary tract samples, whereas clade A1 isolates were mostly obtained from urine and blood. Finally, we built a custom database for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), which differentiated between clade B and clade A1/A2 strains with high-positive and high-negative predictive values (95.6% and 100%, respectively). This study provides important new insight into the clinical features and clades associated with EFM-S and EFM-R isolates. In combination with MALDI-TOF MS, these data could facilitate the rapid initiation of the most appropriate treatment.IMPORTANCEEnterococcus faecium is an important human pathogen in which the prevalence of ampicillin resistance is high. However, little is known about the clinical characteristics of patients infected with ampicillin-resistant and ampicillin-susceptible strains. Indeed, current knowledge is based on genus-wide studies of Enterococcus or studies of very small numbers of susceptible isolates, precluding robust conclusions. Our data highlight specific clinical features related to the epidemiology of EFM-S and EFM-R strains, such as length of hospital stay, history of prior hospitalization, carriage of multidrug-resistant organisms, and type of sample from which the isolate was obtained. The use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry with a custom-built database may make it possible to distinguish clade B isolates, which are typically susceptible to ampicillin, from clade A1/A2 isolates (A1 being typically resistant), thereby facilitating the management of these infections.

Data-Driven Two-Stage Framework for Identification and Characterization of Different Antibiotic-Resistant Escherichia coli Isolates Based on Mass Spectrometry Data

In clinical microbiology, matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is frequently employed for rapid microbial identification. However, rapid identification of antimicrobial resistance (AMR) in Escherichia coli based on a large amount of MALDI-TOF MS data has not yet been reported. This may be because building a prediction model to cover all E. coli isolates would be challenging given the high diversity of the E. coli population. This study aimed to develop a MALDI-TOF MS-based, data-driven, two-stage framework for characterizing different AMRs in E. coli. Specifically, amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM) were used. In the first stage, we split the data into two groups based on informative peaks according to the importance of the random forest. In the second stage, prediction models were constructed using four different machine learning algorithms-logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The findings demonstrate that XGBoost outperformed the other four machine learning models. The values of the area under the receiver operating characteristic curve were 0.62, 0.72, 0.87, 0.72, and 0.72 for AMC, CAZ, CIP, CRO, and CXM, respectively. This implies that a data-driven, two-stage framework could improve accuracy by approximately 2.8%. As a result, we developed AMR prediction models for E. coli using a data-driven two-stage framework, which is promising for assisting physicians in making decisions. Further, the analysis of informative peaks in future studies could potentially reveal new insights. IMPORTANCE Based on a large amount of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) clinical data, comprising 37,918 Escherichia coli isolates, a data-driven two-stage framework was established to evaluate the antimicrobial resistance of E. coli. Five antibiotics, including amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM), were considered for the two-stage model training, and the values of the area under the receiver operating characteristic curve (AUC) were 0.62 for AMC, 0.72 for CAZ, 0.87 for CIP, 0.72 for CRO, and 0.72 for CXM. Further investigations revealed that the informative peak m/z 9714 appeared with some important peaks at m/z 6809, m/z 7650, m/z 10534, and m/z 11783 for CIP and at m/z 6809, m/z 10475, and m/z 8447 for CAZ, CRO, and CXM. This framework has the potential to improve the accuracy by approximately 2.8%, indicating a promising potential for further research.

Antibiotic Resistance Diagnosis in ESKAPE Pathogens-A Review on Proteomic Perspective

Antibiotic resistance has emerged as an imminent pandemic. Rapid diagnostic assays distinguish bacterial infections from other diseases and aid antimicrobial stewardship, therapy optimization, and epidemiological surveillance. Traditional methods typically have longer turn-around times for definitive results. On the other hand, proteomic studies have progressed constantly and improved both in qualitative and quantitative analysis. With a wide range of data sets made available in the public domain, the ability to interpret the data has considerably reduced the error rates. This review gives an insight on state-of-the-art proteomic techniques in diagnosing antibiotic resistance in ESKAPE pathogens with a future outlook for evading the "imminent pandemic".

Direct prediction of carbapenem-resistant, carbapenemase-producing, and colistin-resistant Klebsiella pneumoniae isolates from routine MALDI-TOF mass spectra using machine learning and outcome evaluation

The objective of this study was to develop a rapid prediction method for carbapenem-resistant Klebsiella pneumoniae (CRKP) and colistin-resistant K. pneumoniae (ColRKP) based on routine MALDI-TOF mass spectrometry (MS) results in order to formulate a suitable and rapid treatment strategy. In total, 830 CRKP and 1,462 carbapenem-susceptible K. pneumoniae (CSKP) isolates were collected; 54 ColRKP isolates and 1,592 colistin-intermediate K. pneumoniae (ColIKP) isolates were also included. Routine MALDI-TOF MS, antimicrobial susceptibility testing, NG-Test CARBA 5, and resistance gene detection were followed by machine learning (ML). Using the ML model, the accuracy and area under the curve for differentiating CRKP and CSKP were 0.8869 and 0.9551, and those for ColRKP and ColIKP were 0.8361 and 0.8447, respectively. The most important MS features of CRKP and ColRKP were m/z 4520-4529 and m/z 4170-4179, respectively. Of the CRKP isolates, MS m/z 4520-4529 was a potential biomarker for distinguishing KPC from OXA, NDM, IMP, and VIM. Of the 34 patients who received preliminary CRKP ML prediction results (by texting), 24 (70.6%) were confirmed to have CRKP infection. The mortality rate was lower in patients who received antibiotic regimen adjustment based on the preliminary ML prediction (4/14, 28.6%). In conclusion, the proposed model can provide rapid results for differentiating CRKP and CSKP, as well as ColRKP and ColIKP. The combination of ML-based CRKP with preliminary reporting of results can help physicians alter the regimen approximately 24 h earlier, resulting in improved survival of patients with timely antibiotic intervention.

Towards Accurate Identification of Antibiotic-Resistant Pathogens through the Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical information from complicated MS spectral data. Different preprocessing methods yield different data, and the optimal approach is unclear. In this study, we adopted an ensemble of multiple preprocessing methods--FlexAnalysis, MALDIquant, and continuous wavelet transform-based methods--to detect peaks and build machine learning classifiers, including logistic regressions, naïve Bayes classifiers, random forests, and a support vector machine. The aim was to identify antibiotic resistance in Acinetobacter baumannii, Acinetobacter nosocomialis, Enterococcus faecium, and Group B Streptococci (GBS) based on MALDI-TOF MS spectra collected from two branches of a referral tertiary medical center. The ensemble method was compared with the individual methods. Random forest models built with the data preprocessed by the ensemble method outperformed individual preprocessing methods and achieved the highest accuracy, with values of 84.37% (A. baumannii), 90.96% (A. nosocomialis), 78.54% (E. faecium), and 70.12% (GBS) on independent testing datasets. Through feature selection, important peaks related to antibiotic resistance could be detected from integrated information. The prediction model can provide an opinion for clinicians. The discriminative peaks enabling better prediction performance can provide a reference for further investigation of the resistance mechanism.

Efficiently Predicting Vancomycin Resistance of Enterococcus Faecium From MALDI-TOF MS Spectra Using a Deep Learning-Based Approach

Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has recently become a useful analytical approach for microbial identification. The presence and absence of specific peaks on MS spectra are commonly used to identify the bacterial species and predict antibiotic-resistant strains. However, the conventional approach using few single peaks would result in insufficient prediction power without using complete information of whole MS spectra. In the past few years, machine learning algorithms have been successfully applied to analyze the MALDI-TOF MS peaks pattern for rapid strain typing. In this study, we developed a convolutional neural network (CNN) method to deal with the complete information of MALDI-TOF MS spectra for detecting Enterococcus faecium, which is one of the leading pathogens in the world. We developed a CNN model to rapidly and accurately predict vancomycin-resistant Enterococcus faecium (VREfm) samples from the whole mass spectra profiles of clinical samples. The CNN models demonstrated good classification performances with the average area under the receiver operating characteristic curve (AUROC) of 0.887 when using external validation data independently. Additionally, we employed the score-class activation mapping (CAM) method to identify the important features of our CNN models and found some discriminative signals that can substantially contribute to detecting the ion of resistance. This study not only utilized the complete information of MALTI-TOF MS data directly but also provided a practical means for rapid detection of VREfm using a deep learning algorithm.