Antimicrobial Resistance In Nosocomial Bacterial Infections: A Systematic Review Of Global Trends And Laboratory Detection Methods
Bibliographic record
Abstract
Background: Nosocomial bacterial infections due to antimicrobial-resistant (AMR) bacteria are among the most critical public health concerns in the world in the 21st century. Healthcare-associated infections (HAI) cause millions of patients to fall ill every year. These pose a significant public health burden in terms of mortality rates, morbidity rates, increased hospitalization times, and financial costs. The rapid transmission of multidrug-resistant (MDR), extensively drug-resistant (XDR), and pan-drug-resistant (PDR) bacteria in hospital environments has seriously undermined the therapeutic options available to clinicians. Laboratory detection of resistance mechanisms is critical to appropriate therapy and effective infection control. Objectives: The objectives of this systematic review were to synthesize evidence on global epidemiological trends of AMR in nosocomial bacterial infections, identify the nature of the most common bacteria that are resistant to antimicrobials in healthcare environments, and critically evaluate existing laboratory techniques for the detection of clinically relevant resistance mechanisms. Methods: An extensive literature search was conducted on several databases, including PubMed/MEDLINE, Scopus, Web of Science, CINAHL, and Cochrane Library, for literature published from January 2020 to March 2026. Relevant literature was included in this study if it discussed AMR rates in nosocomial infections, resistance mechanisms, and detection methods. Authors of this study strictly followed the guidelines of PRISMA 2020 for this study. For assessing the quality of included literature, Newcastle-Ottawa Scale was used for observational studies, and for assessing diagnostic accuracy, QUADAS-2 was used. Results: For this study, a total of 10 high-quality literature was included in the final data extraction. The global prevalence of MDR nosocomial infections varied from 38.2% to 76.4%. ESKAPE organisms, including Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumonia, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter, were responsible for a large number of resistant nosocomial infections. The prevalence of MRSA in ICU settings varied from 18.3% to 54.7%.superior diagnostic accuracy for resistance detection in comparison with conventional phenotypic approaches. Conclusion: The burden of AMR in nosocomial infections is rising globally with alarming trends in some areas of the world. It is imperative that the use of molecular diagnostic tools, ASPs, and effective infection prevention and control strategies be implemented globally to curb the rising burden of AMR. It is also vital that there be effective international collaborations in the sharing of data and information on the rising burden of resistance in the world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".