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

Integration of MALDI-TOF MS and machine learning to classify enterococci: A comparative analysis of supervised learning algorithms for species prediction

This research focused on distinguishing distinct matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectral signatures of three Enterococcus species. We evaluated and compared the predictive performance of four supervised machine learning algorithms, K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), to accurately classify Enterococcus species. This study involved a comprehensive dataset of 410 strains, generating 1640 individual spectra through on-plate and off-plate protein extraction methods. Although the commercial database correctly identified 76.9% of the strains, machine learning classifiers demonstrated superior performance (accuracy 0.991). In the RF model, top informative peaks played a significant role in the classification. Whole-genome sequencing showed that the most informative peaks are biomarkers connected to proteins, which are essential for understanding bacterial classification and evolution. The integration of MALDI-TOF MS and machine learning provides a rapid and accurate method for identifying Enterococcus species, improving healthcare and food safety.

A Possible Tool for Guiding Therapeutic Approaches to Urinary Infections with Klebsiella pneumoniae: Analyzing a Dataset from a Romanian Tertiary Hospital

Introduction: The emergence of carbapenem-resistant pathogenic bacteria is a growing global public health concern. Carbapenem-resistant uropathogenic strains of Klebsiella pneumoniae can cause uncomplicated or complicated urinary tract infections, leading to a high risk of treatment failure and the spread of resistance determinants. The objectives of this 24-month study were to identify the prognostic characteristics of patients who were infected with carbapenem-resistant Klebsiella pneumoniae (CRKpn) and to create a tool to estimate the probability of a CRKpn infection before having the complete results of a patient's antibiogram. Results: We found that 41.6% of all urinary infections with Kpn were caused by CRKpn. Factors such as male gender, the presence of upper urinary tract infections, invasive urinary maneuvers, recent infection with or carriage of the germ, and the nosocomial occurrence of UTIs with Kpn were predictive for CRKpn infection. Based on these factors, we proposed a model to estimate the presence of CRKpn. Methods: A retrospective case-control study including all hospitalized patients with urinary tract infections (UTIs) caused by Klebsiella pneumoniae was carried out. We reported data as percentages, identified independent predictors of the presence of CRKpn, and proposed a tool to evaluate the probability through multivariate analysis. Conclusions: Through this study, we aim to provide clinicians with a tool to support decision making regarding first-line antibiotic treatment.

Pioneering Klebsiella Pneumoniae Antibiotic Resistance Prediction With Artificial Intelligence-Clinical Decision Support System-Enhanced Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry: Retrospective Study

BACKGROUND

The rising prevalence and swift spread of multidrug-resistant gram-negative bacteria (MDR-GNB), especially Klebsiella pneumoniae (KP), present a critical global health threat highlighted by the World Health Organization, with mortality rates soaring approximately 50% with inappropriate antimicrobial treatment.

OBJECTIVE

This study aims to advance a novel strategy to develop an artificial intelligence-clinical decision support system (AI-CDSS) that combines machine learning (ML) with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), aiming to significantly improve the accuracy and speed of diagnosing antibiotic resistance, directly addressing the grave health risks posed by the widespread dissemination of pan drug-resistant gram-negative bacteria across numerous countries.

METHODS

A comprehensive dataset comprising 165,299 bacterial specimens and 11,996 KP isolates was meticulously analyzed using MALDI-TOF MS technology. Advanced ML algorithms were harnessed to sculpt predictive models that ascertain resistance to quintessential antibiotics, particularly levofloxacin and ciprofloxacin, by using the amassed spectral data.

RESULTS

Our ML models revealed remarkable proficiency in forecasting antibiotic resistance, with the random forest classifier emerging as particularly effective in predicting resistance to both levofloxacin and ciprofloxacin, achieving the highest area under the curve of 0.95. Performance metrics across different models, including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score, were detailed, underlining the potential of these algorithms in aiding the development of precision treatment strategies.

CONCLUSIONS

This investigation highlights the synergy between MALDI-TOF MS and ML as a beacon of hope against the escalating threat of antibiotic resistance. The advent of AI-CDSS heralds a new era in clinical diagnostics, promising a future in which rapid and accurate resistance prediction becomes a cornerstone in combating infectious diseases. Through this innovative approach, we answered the challenge posed by KP and other multidrug-resistant pathogens, marking a significant milestone in our journey toward global health security.

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.

Direct Klebsiella pneumoniae Carbapenem Resistance and Carbapenemases Genotype Prediction by Al-MOF/TiO2@Au Cubic Heterostructures-Assisted Intact Bacterial Cells Metabolic Analysis

Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections pose a significant threat to human health. Fast and accurate prediction of K. pneumoniae carbapenem resistance and carbapenemase genotype is critical for guiding antibiotic treatment and reducing mortality rates. In this study, we present a novel method using Al-MOF/TiO2@Au cubic heterostructures for the metabolic analysis of intact bacterial cells, enabling rapid diagnosis of CRKP and its carbapenemases genotype. The Al-MOF/TiO2@Au cubic composites display strong light absorption and high surface area, facilitating the in situ effective extraction of metabolic fingerprints from intact bacterial cells. Utilizing this method, we rapidly and sensitively extracted metabolic fingerprints from 169 clinical isolates of K. pneumoniae obtained from patients. Machine learning analysis of the metabolic fingerprint changes successfully distinguishes CRKP from the sensitive strains, achieving the high area under the curve (AUC) values of 1.00 in both training and testing sets based on the 254 m/z features, respectively. Additionally, this platform enables rapid carbapenemase genotype discrimination of CRKP for precision antibiotic therapy. Our strategy holds great potential for swift diagnosis of CRKP and carbapenemase genotype discrimination, guiding effective management of CRKP bacterial infections in both hospital and community settings.

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.

Classification of Latilactobacillus sakei subspecies based on MALDI-TOF MS protein profiles using machine learning models

Latilactobacillus sakei is an important bacterial species used as a starter culture for fermented foods; however, two subspecies within this species exhibit different properties in the foods. Matrix-assisted laser desorption/ionization-time of flight mass spectrometer (MALDI-TOF MS) is the gold standard for microbial fingerprinting. However, the resolution power is down to the species level. This study was to combine MALDI-TOF mass spectra and machine learning to develop a new method to identify two L. sakei subspecies (L. sakei subsp. sakei and L. sakei subsp. carnosus) and non-L. sakei species. Totally, 227 strains were collected, with 908 spectra obtained via on- and off-plate protein extraction. Only 68.7% of strains were correctly identified at the subspecies level in the Biotyper database; however, a high level of performance was observed from the machine learning models. Partial least squares-discriminant analysis (PLS-DA), principal component analysis-K-nearest neighbor (PCA-KNN), and support vector machine (SVM) demonstrated 0.823, 0.914, and 0.903 accuracies, respectively, whereas the random forest (RF) achieved an accuracy of 0.954, with an area under the receiver operating characteristic (AUROC) curve of 0.99, outperforming the other algorithms in distinguishing the subspecies. The machine learning proved to be a promising technique for the rapid and high-resolution classification of L. sakei subspecies using MALDI-TOF MS.

IMPORTANCE

Latilactobacillus sakei plays a significant role in the realm of food bacteria. One particular subspecies of L. sakei is employed as a protective agent during food fermentation, whereas another strain is responsible for food spoilage. Hence, it is crucial to precisely differentiate between the two subspecies of L. sakei. In this study, machine learning models based on protein mass peaks were developed for the first time to distinguish L. sakei subspecies. Furthermore, the efficacy of three commonly used machine learning algorithms for microbial classification was evaluated. Our results provide the foundation for future research on developing machine learning models for the classification of microbial species or subspecies. In addition, the developed model can be used in the food industry to monitor L. sakei subspecies in fermented foods in a time- and cost-effective method for food quality and safety.

Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for detection of antimicrobial resistance (AMR) remains limited. Here, we used MALDI-TOF MS with artificial intelligence (AI) approaches to successfully predict AMR in Pseudomonas aeruginosa, a priority pathogen with complex AMR mechanisms. The highest performance was achieved for modern β-lactam/β-lactamase inhibitor drugs, namely, ceftazidime/avibactam and ceftolozane/tazobactam. For these drugs, the model demonstrated area under the receiver operating characteristic curve (AUROC) of 0.869 and 0.856, specificity of 0.925 and 0.897, and sensitivity of 0.731 and 0.714, respectively. As part of this work, we developed dynamic binning, a feature engineering technique that effectively reduces the high-dimensional feature set and has wide-ranging applicability to MALDI-TOF MS data. Compared to conventional feature engineering approaches, the dynamic binning method yielded highest performance in 7 of 10 antimicrobials. Moreover, we showcased the efficacy of transfer learning in enhancing the AUROC performance for 8 of 11 antimicrobials. By assessing the contribution of features to the model's prediction, we identified proteins that may contribute to AMR mechanisms. Our findings demonstrate the potential of combining AI with MALDI-TOF MS as a rapid AMR diagnostic tool for Pseudomonas aeruginosa.IMPORTANCEPseudomonas aeruginosa is a key bacterial pathogen that causes significant global morbidity and mortality. Antimicrobial resistance (AMR) emerges rapidly in P. aeruginosa and is driven by complex mechanisms. Drug-resistant P. aeruginosa is a major challenge in clinical settings due to limited treatment options. Early detection of AMR can guide antibiotic choices, improve patient outcomes, and avoid unnecessary antibiotic use. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used for rapid species identification in clinical microbiology. In this study, we repurposed mass spectra generated by MALDI-TOF and used them as inputs for artificial intelligence approaches to successfully predict AMR in P. aeruginosa for multiple key antibiotic classes. This work represents an important advance toward using MALDI-TOF as a rapid AMR diagnostic for P. aeruginosa in clinical settings.

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.

Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation

BACKGROUND

With increasing antibiotic resistance and regulation, the issue of antibiotic combination has been emphasised. However, antibiotic combination prescribing lacks a rapid identification of feasibility, while its risk of drug interactions is unclear.

METHODS

We conducted statistical descriptions on 16 101 antibiotic coprescriptions for inpatients with bacterial infections from 2015 to 2023. By integrating the frequency and effectiveness of prescriptions, we formulated recommendations for the feasibility of antibiotic combinations. Initially, a machine learning algorithm was utilised to optimise grading thresholds and habits for antibiotic combinations. A feedforward neural network (FNN) algorithm was employed to develop antibiotic combination recommendation model (ACRM). To enhance interpretability, we combined sequential methods and DrugBank to explore the correlation between antibiotic combinations and drug interactions.

RESULTS

A total of 55 antibiotics, covering 657 empirical clinical antibiotic combinations were used for ACRM construction. Model performance on the test dataset showed AUROCs of 0.589-0.895 for various antibiotic recommendation classes. The ACRM showed satisfactory clinical relevance with 61.54-73.33% prediction accuracy in a new independent retrospective cohort. Antibiotic interaction detection showed that the risk of drug interactions was 29.2% for strongly recommended and 43.5% for not recommended. A positive correlation was identified between the level of clinical recommendation and the risk of drug interactions.

CONCLUSIONS

Machine learning modelling of retrospective antibiotic prescriptions habits has the potential to predict antibiotic combination recommendations. The ACRM plays a supporting role in reducing the incidence of drug interactions. Clinicians are encouraged to adopt such systems to improve the management of antibiotic usage and medication safety.

Carbapenem-resistant Gram-negative bacteria (CR-GNB) in ICUs: resistance genes, therapeutics, and prevention - a comprehensive review

Intensive care units (ICUs) are specialized environments dedicated to the management of critically ill patients, who are particularly susceptible to drug-resistant bacteria. Among these, carbapenem-resistant Gram-negative bacteria (CR-GNB) pose a significant threat endangering the lives of ICU patients. Carbapenemase production is a key resistance mechanism in CR-GNB, with the transfer of resistance genes contributing to the extensive emergence of antimicrobial resistance (AMR). CR-GNB infections are widespread in ICUs, highlighting an urgent need for prevention and control measures to reduce mortality rates associated with CR-GNB transmission or infection. This review provides an overview of key aspects surrounding CR-GNB within ICUs. We examine the mechanisms of bacterial drug resistance, the resistance genes that frequently occur with CR-GNB infections in ICU, and the therapeutic options against carbapenemase genotypes. Additionally, we highlight crucial preventive measures to impede the transmission and spread of CR-GNB within ICUs, along with reviewing the advances made in the field of clinical predictive modeling research, which hold excellent potential for practical application.

Biofilm antimicrobial susceptibility testing: where are we and where could we be going?

Our knowledge about the fundamental aspects of biofilm biology, including the mechanisms behind the reduced antimicrobial susceptibility of biofilms, has increased drastically over the last decades. However, this knowledge has so far not been translated into major changes in clinical practice. While the biofilm concept is increasingly on the radar of clinical microbiologists, physicians, and healthcare professionals in general, the standardized tools to study biofilms in the clinical microbiology laboratory are still lacking; one area in which this is particularly obvious is that of antimicrobial susceptibility testing (AST). It is generally accepted that the biofilm lifestyle has a tremendous impact on antibiotic susceptibility, yet AST is typically still carried out with planktonic cells. On top of that, the microenvironment at the site of infection is an important driver for microbial physiology and hence susceptibility; but this is poorly reflected in current AST methods. The goal of this review is to provide an overview of the state of the art concerning biofilm AST and highlight the knowledge gaps in this area. Subsequently, potential ways to improve biofilm-based AST will be discussed. Finally, bottlenecks currently preventing the use of biofilm AST in clinical practice, as well as the steps needed to get past these bottlenecks, will be discussed.

Rapid discrimination of Bifidobacterium longum subspecies based on MALDI-TOF MS and machine learning

Although MALDI-TOF mass spectrometry (MS) is widely known as a rapid and cost-effective reference method for identifying microorganisms, its commercial databases face limitations in accurately distinguishing specific subspecies of Bifidobacterium. This study aimed to explore the potential of MALDI-TOF MS protein profiles, coupled with prediction methods, to differentiate between Bifidobacterium longum subsp. infantis (B. infantis) and Bifidobacterium longum subsp. longum (B. longum). The investigation involved the analysis of mass spectra of 59 B. longum strains and 41 B. infantis strains, leading to the identification of five distinct biomarker peaks, specifically at m/z 2,929, 4,408, 5,381, 5,394, and 8,817, using Recurrent Feature Elimination (RFE). To facilate classification between B. longum and B. infantis based on the mass spectra, machine learning models were developed, employing algorithms such as logistic regression (LR), random forest (RF), and support vector machine (SVM). The evaluation of the mass spectrometry data showed that the RF model exhibited the highest performace, boasting an impressive AUC of 0.984. This model outperformed other algorithms in terms of accuracy and sensitivity. Furthermore, when employing a voting mechanism on multi-mass spectrometry data for strain identificaton, the RF model achieved the highest accuracy of 96.67%. The outcomes of this research hold the significant potential for commercial applications, enabling the rapid and precise discrimination of B. longum and B. infantis using MALDI-TOF MS in conjunction with machine learning. Additionally, the approach proposed in this study carries substantial implications across various industries, such as probiotics and pharmaceuticals, where the precise differentiation of specific subspecies is essential for product development and quality control.

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

This study investigated combination of the Rapid Sepsityper Kit and a machine learning (ML)-based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) approach for rapid prediction of methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem-resistant Klebsiella pneumoniae (CRKP) from positive blood culture bottles. The study involved 461 patients with monomicrobial bloodstream infections. Species identification was performed using the conventional MALDI-TOF MS Biotyper system and the Rapid Sepsityper protocol. The data underwent preprocessing steps, and ML models were trained using preprocessed MALDI-TOF data and corresponding labels. The interpretability of the model was enhanced using SHapely Additive exPlanations values to identify significant features. In total, 44 S. aureus isolates comprising 406 MALDI-TOF MS files and 126 K. pneumoniae isolates comprising 1249 MALDI-TOF MS files were evaluated. This study demonstrated the feasibility of predicting MRSA among S. aureus and CRKP among K. pneumoniae isolates using MALDI-TOF MS and Sepsityper. Accuracy, area under the receiver operating characteristic curve, and F1 score for MRSA/methicillin-susceptible S. aureus were 0.875, 0.898 and 0.904, respectively; for CRKP/carbapenem-susceptible K. pneumoniae, these values were 0.766, 0.828 and 0.795, respectively. In conclusion, the novel ML-based MALDI-TOF MS approach enables rapid identification of MRSA and CRKP from flagged blood cultures within 1 h. This enables earlier initiation of targeted antimicrobial therapy, reducing deaths due to sepsis. The favourable performance and reduced turnaround time of this method suggest its potential as a rapid detection strategy in clinical microbiology laboratories, ultimately improving patient outcomes.

MALDI-TOF MS-Based KPC Direct Detection from Patients' Positive Blood Culture Bottles, Short-Term Cultures, and Colonies at the Hospital

Carbapenemase resistance in Enterobacterales is a global public health problem and rapid and effective methods for detecting these resistance mechanisms are needed urgently. Our aim was to evaluate the performance of a MALDI-TOF MS-based "Klebsiella pneumoniae carbapenemase" (KPC) detection protocol from patients' positive blood cultures, short-term cultures, and colonies in healthcare settings. Bacterial identification and KPC detection were achieved after protein extraction with organic solvents and target spot loading with suitable organic matrices. The confirmation of KPC production was performed using susceptibility tests and bla_KPC amplification using PCR and sequencing. The KPC direct detection (KPC peak at approximately 28.681 Da) from patients' positive blood cultures, short-term cultures, and colonies, once bacterial identification was achieved, showed an overall sensibility and specificity of 100% (CI95: [95%, 100%] and CI95: [99%, 100%], respectively). The concordance between hospital routine bacterial identification protocol and identification using this new methodology from the same extract used for KPC detection was ≥92%. This study represents the pioneering effort to directly detect KPC using MALDI-TOF MS technology, conducted on patient-derived samples obtained from hospitals for validation purposes, in a multi-resistance global context that requires concrete actions to preserve the available therapeutic options and reduce the spread of antibiotic resistance markers.

A Review of Carbapenem Resistance in Enterobacterales and Its Detection Techniques

Infectious disease outbreaks have caused thousands of deaths and hospitalizations, along with severe negative global economic impacts. Among these, infections caused by antimicrobial-resistant microorganisms are a major growing concern. The misuse and overuse of antimicrobials have resulted in the emergence of antimicrobial resistance (AMR) worldwide. Carbapenem-resistant Enterobacterales (CRE) are among the bacteria that need urgent attention globally. The emergence and spread of carbapenem-resistant bacteria are mainly due to the rapid dissemination of genes that encode carbapenemases through horizontal gene transfer (HGT). The rapid dissemination enables the development of host colonization and infection cases in humans who do not use the antibiotic (carbapenem) or those who are hospitalized but interacting with environments and hosts colonized with carbapenemase-producing (CP) bacteria. There are continuing efforts to characterize and differentiate carbapenem-resistant bacteria from susceptible bacteria to allow for the appropriate diagnosis, treatment, prevention, and control of infections. This review presents an overview of the factors that cause the emergence of AMR, particularly CRE, where they have been reported, and then, it outlines carbapenemases and how they are disseminated through humans, the environment, and food systems. Then, current and emerging techniques for the detection and surveillance of AMR, primarily CRE, and gaps in detection technologies are presented. This review can assist in developing prevention and control measures to minimize the spread of carbapenem resistance in the human ecosystem, including hospitals, food supply chains, and water treatment facilities. Furthermore, the development of rapid and affordable detection techniques is helpful in controlling the negative impact of infections caused by AMR/CRE. Since delays in diagnostics and appropriate antibiotic treatment for such infections lead to increased mortality rates and hospital costs, it is, therefore, imperative that rapid tests be a priority.