Prediction of methicillin-resistant Staphylococcus aureus and carbapenem-resistant Klebsiella pneumoniae from flagged blood cultures by combining rapid Sepsityper MALDI-TOF mass spectrometry with machine learning

Nanomaterials for bacterial enrichment and detection in healthcare

Bacterial infections in the blood (sepsis) have been recognized as a leading cause of mortality in the clinical field due to limitations in the detection of bacteria at low concentration and their resistance to antibiotics by excessive misuse. Some of the common symptoms are fever, chills, rapid heartbeat, difficulty breathing, confusion, and changes in mental status with occasionally pale, clammy, and mottled skin. Early diagnosis and identification are the keys to a successful treatment for sepsis patients. Researchers have developed nanoparticles to enrich bacterial populations followed by detection and applied them to conventional methods such as phenotypic and molecular diagnostics to enhance different detectors' responses toward pathogens. This short review systematically overviews steps that are followed in clinical labs for bacterial detection, identification, and their drawbacks. In this context, we discuss the role that nanoparticles can play in overcoming the limits of traditional microbiology methods in terms of turnaround times (TATs) and accuracy. We believe that this short review will provide up-to-date information about the applications of nanoparticles in the enrichment, separation, and identification of bacterial infection in the clinical field and, therefore, a way of rapid treatment.

Diagnostic methods and protocols for rapid determination of methicillin resistance in Staphylococcus aureus bloodstream infections: a comparative analysis

PURPOSE

To evaluate diagnostic performance of four diagnostic methods for rapid determination of methicillin resistance in S. aureus positive blood cultures (BCs).

METHODS

Clinical and spiked BCs were subjected to the evaluation of the following methods and protocols: a. Eazyplex® MRSA Plus loop-mediated isothermal amplification (LAMP) assay directly from BC fluid; b. MALDI-TOF MS subtyping on BC pellet extracted with Rapid Sepsityper® protocol and on 4-h short-term subculture; c. Clearview™ Culture Colony PBP2a SA immunochromatography assay on BC pellet and on 4-h short-term subculture; d. EUCAST RAST cefoxitin screen test performed directly from BC and including reading times at 4-h, 6-h and 16-20-h.

RESULTS

Eazyplex® MRSA plus exhibited the best performance, showing 100% sensitivity, specificity, positive predictive value, and negative predictive value, followed by PBP2a SA Culture Colony Clearview assay and EUCAST RAST cefoxitin screen. MALDI-TOF MS subtyping showed the lowest diagnostic accuracy (59.8 and 65.7% directly from BC and from 4-h subculture, respectively). In detail, sensitivity and specificity ranged from 24.3% to 20.4% and from 88.9% to 98.3% for protocols performed from BC pellet and 4-h subculture, respectively.

CONCLUSIONS

The Eazyplex® MRSA Plus and the immunochromatographic Clearview™ PBP2a SA Culture Colony methods can provide reliable results within 1 h from the start of positive BC processing. MALDI TOF MS subtyping showed unacceptable specificity by performing analysis from BC pellets, while its sensitivity depends on the prevalence of PSM-positive MRSA strains. The EUCAST RAST, based on disc diffusion, showed excellent performance with a time-to-result of at least 4 h.

Artificial intelligence in antimicrobial stewardship: a systematic review and meta-analysis of predictive performance and diagnostic accuracy

The increasing threat of antimicrobial resistance has prompted a need for more effective antimicrobial stewardship programs (AMS). Artificial intelligence (AI) and machine learning (ML) tools have emerged as potential solutions to enhance decision-making and improve patient outcomes in AMS. This systematic review and meta-analysis aims to evaluate the impact of AI in AMS and to assess its predictive performance and diagnostic accuracy. We conducted a comprehensive literature search across PubMed/MEDLINE, Scopus, EMBASE, and Web of Science to identify studies published up to July 2024. Studies included were observational, cohort, or retrospective, focusing on the application of AI/ML in AMS. The outcomes assessed were the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We calculated the mean pooled effect size (ES) and its 95% confidence interval (CI) using a random-effects model. The risk of bias was assessed using the QUADAS-AI tool, and the protocol was registered in PROSPERO. Out of 3,458 retrieved articles, 80 studies met the inclusion criteria. Our meta-analysis demonstrated that ML models exhibited strong predictive performance and diagnostic accuracy, with the following results: AUC [ES: 72.28 (70.42-74.14)], accuracy [ES: 74.97 (73.35-76.58)], sensitivity [ES: 76.89; (71.90-81.89)], specificity [ES: 73.77; (67.87-79.67)], NPV [ES:79.92 (76.54-83.31)], and PPV [ES: 69.41 (60.19-78.63)] across various AMS settings. AI and ML tools offer promising enhancements due to their strong predictive performance. The integration of AI into AMS could lead to more precise antimicrobial prescribing, reduced antimicrobial resistance, and better resource utilization.

Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning

BACKGROUND

Rapid and accurate identification of bacteria is required in order to develop effective treatment strategies. Traditional culture-based methods are time-consuming, while MALDI-TOF MS is expensive. The Raman spectroscopy, due to its relatively cost-effectiveness, offers a promising alternative for bacterial identification. However, its clinical utility still requires further validation.

METHODS

In this study, the artificial intelligent Raman detection and identification system (AIRDIS) was implemented to identify bacterial species, including Staphylococcus aureus (n = 1290), Enterococcus faecium (n = 1020), Klebsiella pneumoniae (n = 1366), Pseudomonas aeruginosa (n = 1067), and Acinetobacter baumannii (n = 811). Raman spectra were collected, preprocessed, and analyzed by machine learning (ML).

RESULTS

After training on 24,420 Raman spectra from 1221 isolates and testing on 4333 isolates, the AIRDIS demonstrated an area under the curve (AUC) of 0.99 for Gram classification, with accuracies of 97.64 % for Gram-positive bacteria and 98.86 % for Gram-negative bacteria. Spectral differences between Gram-positive and Gram-negative bacteria were linked to structural variations in their cell walls, such as peptidoglycan and lipopolysaccharides. At the species level, S. aureus, E. faecium, K. pneumoniae, P. aeruginosa, and A. baumannii were identified with high accuracy, ranging from 94.76 % to 96.88 %, with all species achieving an AUC of 0.99.

CONCLUSIONS

Validation with a large number of clinical isolates demonstrated Raman spectroscopy combined with ML excels in identification of five bacterial species associated with multidrug resistance. This finding confirms the clinical utility of the system while laying a solid foundation for the future development of antimicrobial resistance prediction models.

Clinical outcomes of carbapenem-resistant gram-negative bacterial bloodstream infection in patients with end-stage renal disease in intensive care units: a multicenter retrospective observational study

BACKGROUND

Carbapenem-resistant gram-negative bacteria (CRGNB) present a considerable global threat due to their challenging treatment and increased mortality rates, with bloodstream infection (BSI) having the highest mortality rate. Patients with end-stage renal disease (ESRD) undergoing renal replacement therapy (RRT) face an increased risk of BSI. Limited data are available regarding the prognosis and treatment outcomes of CRGNB-BSI in patients with ESRD in intensive care units (ICUs).

METHODS

This multi-center retrospective observational study included a total of 149 ICU patients with ESRD and CRGNB-BSI in Taiwan from January 2015 to December 2019. Clinical and microbiological outcomes were assessed, and multivariable regression analysis was used to evaluate the independent risk factors for day-28 mortality and the impact of antimicrobial therapy regimen on treatment outcomes.

RESULTS

Among the 149 patients, a total of 127 patients (85.2%) acquired BSI in the ICU, with catheter-related infections (47.7%) and pneumonia (32.2%) being the most common etiologies. Acinetobacter baumannii (49.0%) and Klebsiella pneumoniae (31.5%) were the most frequently isolated pathogens. The day-28 mortality rate from BSI onset was 52.3%, and in-hospital mortality was 73.2%, with survivors experiencing prolonged hospital stays. A higher Sequential Organ Failure Assessment (SOFA) score (adjusted hazards ratio [aHR], 1.25; 95% confidence interval [CI] 1.17-1.35) and shock status (aHR, 2.12; 95% CI 1.14-3.94) independently predicted day-28 mortality. Colistin-based therapy reduced day-28 mortality in patients with shock, a SOFA score of ≥ 13, and Acinetobacter baumannii-related BSI.

CONCLUSIONS

CRGNB-BSI led to high mortality in critically ill patients with ESRD. Day-28 mortality was independently predicted by a higher SOFA score and shock status. In patients with higher disease severity and Acinetobacter baumannii-related BSI, colistin-based therapy improved treatment outcomes.

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

BACKGROUND

Bloodstream infection (BSI) is a systemic infection that predisposes individuals to sepsis and multiple organ dysfunction syndrome. Early identification of infectious agents and determination of drug-resistant phenotypes can help patients with BSI receive timely, effective, and targeted treatment and improve their survival. This study was based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), and Extremely Randomized Trees (ERT) models were constructed to classify carbapenem-resistant Escherichia coli (CREC) and carbapenem-resistant Klebsiella pneumoniae (CRKP). Bacterial species were identified by MALDI-TOF MS in positive blood cultures isolated via the serum isolation gel method, and E. coli and K. pneumoniae in positive blood cultures were collected and placed into machine learning models to predict susceptibility to carbapenems. The aim of this study was to provide rapid detection of CREC and CRKP in blood cultures, to shorten the turnaround time for laboratory reporting, and to provide a basis for early clinical intervention and rational use of antibiotics.

RESULTS

The collected MALDI-TOF MS data of 640 E. coli and 444 K. pneumoniae were analysed by machine learning algorithms. The area under the receiver operating characteristic curve (AUROC) for the diagnosis of E. coli susceptibility to carbapenems by the DT, RF, GBM, XGBoost, and ERT models were 0.95, 1.00, 0.99, 0.99, and 1.00, respectively, and the accuracy in predicting 149 E. coli-positive blood cultures were 0.89, 0.92, 0.90, 0.92, and 0.86, respectively. The AUROC for the diagnosis of K. pneumoniae susceptibility to carbapenems by the DT, RF, GBM, XGBoost, and ERT models were 0.78, 0.95, 0.93, 0.90, and 0.95, respectively, and the accuracy in predicting 127 K. pneumoniae-positive blood cultures were 0.76, 0.86, 0.81, 0.80, and 0.76, respectively.

CONCLUSIONS

Machine learning models constructed by MALDI-TOF MS were able to directly predict the susceptibility of E. coli and K. pneumoniae in positive blood cultures to carbapenems. This rapid identification of CREC and CRKP reduces detection time and contributes to early warning and response to potential antibiotic resistance problems in the clinic.

CLINICAL TRIAL NUMBER

Not applicable.

Hypervirulent and carbapenem-resistant Klebsiella pneumoniae: A global public health threat

The evolution of hypervirulent and carbapenem-resistant Klebsiella pneumoniae can be categorized into three main patterns: the evolution of KL1/KL2-hvKp strains into CR-hvKp, the evolution of carbapenem-resistant K. pneumoniae (CRKp) strains into hv-CRKp, and the acquisition of hybrid plasmids carrying carbapenem resistance and virulence genes by classical K. pneumoniae (cKp). These strains are characterized by multi-drug resistance, high virulence, and high infectivity. Currently, there are no effective methods for treating and surveillance this pathogen. In addition, the continuous horizontal transfer and clonal spread of these bacteria under the pressure of hospital antibiotics have led to the emergence of more drug-resistant strains. This review discusses the evolution and distribution characteristics of hypervirulent and carbapenem-resistant K. pneumoniae, the mechanisms of carbapenem resistance and hypervirulence, risk factors for susceptibility, infection syndromes, treatment regimens, real-time surveillance and preventive control measures. It also outlines the resistance mechanisms of antimicrobial drugs used to treat this pathogen, providing insights for developing new drugs, combination therapies, and a "One Health" approach. Narrowing the scope of surveillance but intensifying implementation efforts is a viable solution. Monitoring of strains can be focused primarily on hospitals and urban wastewater treatment plants.

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.

Clinical glycoproteomics: methods and diseases

Glycoproteins, representing a significant proportion of posttranslational products, play pivotal roles in various biological processes, such as signal transduction and immune response. Abnormal glycosylation may lead to structural and functional changes of glycoprotein, which is closely related to the occurrence and development of various diseases. Consequently, exploring protein glycosylation can shed light on the mechanisms behind disease manifestation and pave the way for innovative diagnostic and therapeutic strategies. Nonetheless, the study of clinical glycoproteomics is fraught with challenges due to the low abundance and intricate structures of glycosylation. Recent advancements in mass spectrometry-based clinical glycoproteomics have improved our ability to identify abnormal glycoproteins in clinical samples. In this review, we aim to provide a comprehensive overview of the foundational principles and recent advancements in clinical glycoproteomic methodologies and applications. Furthermore, we discussed the typical characteristics, underlying functions, and mechanisms of glycoproteins in various diseases, such as brain diseases, cardiovascular diseases, cancers, kidney diseases, and metabolic diseases. Additionally, we highlighted potential avenues for future development in clinical glycoproteomics. These insights provided in this review will enhance the comprehension of clinical glycoproteomic methods and diseases and promote the elucidation of pathogenesis and the discovery of novel diagnostic biomarkers and therapeutic targets.

Modelling the rapid detection of Carbapenemase-resistant Klebsiella pneumoniae based on machine learning and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

In this study, 80 carbapenem-resistant Klebsiella pneumoniae (CR-KP) and 160 carbapenem-susceptible Klebsiella pneumoniae (CS-KP) strains detected in the clinic were selected and their matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) peaks were collected. K-means clustering was performed on the MS peak data to obtain the best "feature peaks", and four different machine learning models were built to compare the area under the ROC curve, specificity, sensitivity, test set score, and ten-fold cross-validation score of the models. By adjusting the model parameters, the test efficacy of the model is increased on the basis of reducing model overfitting. The area under the ROC curve of the Random Forest, Support Vector Machine, Logistic Regression, and Xgboost models used in this study are 0.99, 0.97, 0.96, and 0.97, respectively; the model scores on the test set are 0.94, 0.91, 0.90, and 0.93, respectively; and the results of the ten-fold cross-validation are 0.84, 0.81, 0.81, and 0.85, respectively. Based on the machine learning algorithms and MALDI-TOF MS assay data can realize rapid detection of CR-KP, shorten the in-laboratory reporting time, and provide fast and reliable identification results of CR-KP and CS-KP.

Artificial intelligence applications in the diagnosis and treatment of bacterial infections

The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.

Impact of the implementation of the Intelligent Antimicrobial System (iAMS) on clinical outcomes among patients with bacteraemia caused by methicillin-resistant Staphylococcus aureus

OBJECTIVES

This study aimed to investigate the clinical impact of the Intelligent Antimicrobial System (iAMS) on patients with bacteraemia due to methicillin-resistant (MRSA) and methicillin-susceptible Staphylococcus aureus (MSSA).

METHODS

A total of 1008 patients with suspected SA infection were enrolled before and after the implementation of iAMS. Among them, 252 with bacteraemia caused by SA, including 118 in the iAMS and 134 in the non-iAMS groups, were evaluated.

RESULTS

The iAMS group exhibited a 5.2% (from 55.2% to 50.0%; P = 0.96) increase in the 1-year survival rate. For patients with MRSA and MSSA compared to the non-iAMS group, the 1-year survival rate increased by 17.6% (from 70.9% to 53.3%; P = 0.41) and 7.0% (from 52.3% to 45.3%; P = 0.57), respectively, both surpassing the rate of the non-iAMS group. The iAMS intervention resulted in a higher long-term survival rate (from 70.9% to 52.3%; P = 0.984) for MRSA patients than for MSSA patients. MRSA patients experienced a reduced length of hospital stay (from 23.3% to 35.6%; P = 0.038), and the 45-day discharge rate increased by 20.4% (P = 0.064). Furthermore, the intervention resulted in a significant 97.3% relative decrease in near miss medication incidents reported by pharmacists (P = 0.013).

CONCLUSIONS

Implementation of iAMS platform improved long-term survival rates, discharge rates, hospitalization days, and medical cost (although no significant differences were observed) among patients with MRSA bacteraemia. Additionally, it demonstrated significant benefits in ensuring drug safety.