ReviewFrontiers in antibiotics2023
Drug-resistant bacteria in the critically ill: patterns and mechanisms of resistance and potential remedies.
Review in Frontiers in antibiotics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Development and Prospective Validation of a Machine Learning Tool for Predicting Multidrug-resistant Organism Infections in an Indian Critical Care Unit.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- Epidemiology and predictors of multidrug-resistant Enterobacteriaceae in intensive care units of a tertiary hospital in Somalia: a five-year retrospective study (2019-2024).Frontiers in antibiotics · 2026Article
- Cell-to-Cell and Patient-to-Patient Variability in Antimicrobial Resistance.Microorganisms · 2025Review
- Resistance Dynamics in a Romanian Critical Care Unit: Four Years of ESKAPE Pathogen Surveillance.Medicina (Kaunas, Lithuania) · 2025Observational
- Impact of COVID-19 isolation measures on ICU microbial resistance dynamics: simulation-based statistical modeling analysis.Antimicrobial resistance and infection control · 2025Article
- The impact of carbapenem-resistant infections in intensive care units: focus on non-fermenting gram-negative bacilli and survival analysis.Antimicrobial resistance and infection control · 2025Article
- Deciphering vancomycin resistance inFrontiers in microbiology · 2025Article
- Sepsis Stewardship: The Puzzle of Antibiotic Therapy in the Context of Individualization of Decision Making.Journal of personalized medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance in the intensive care unit is an ongoing global healthcare concern associated with high mortality and morbidity rates and high healthcare costs. Select groups of bacterial pathogens express different mechanisms of antimicrobial resistance. Clinicians face challenges in managing patients with multidrug-resistant bacteria in the form of a limited pool of available antibiotics, slow and potentially inaccurate conventional diagnostic microbial modalities, mimicry of non-infective conditions with infective syndromes, and the confounding of the clinical picture of organ dysfunction associated with sepsis with postoperative surgical complications such as hemorrhage and fluid shifts. Potential remedies for antimicrobial resistance include specific surveillance, adequate and systematic antibiotic stewardship, use of pharmacokinetic and pharmacodynamic techniques of therapy, and antimicrobial monitoring and adequate employment of infection control policies. Novel techniques of combating antimicrobial resistance include the use of aerosolized antibiotics for lung infections, the restoration of gut microflora using fecal transplantation, and orally administered probiotics. Newer antibiotics are urgently needed as part of the armamentarium against multidrug-resistant bacteria. In this review we discuss mechanisms and patterns of microbial resistance in a select group of drug-resistant bacteria, and preventive and remedial measures for combating antibiotic resistance in the critically ill.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.