ReviewInfection2026
Near-real-time whole-genome sequencing in hospital infection control: a narrative review.
Review in Infection, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHospital-acquired infections (HAIs) continue to pose a significant challenge to patient safety, partly because traditional microbiological methods slow down the identification of pathogens and outbreak detection. Near-real-time whole-genome sequencing (WGS) and point-of-care (POC) sequencing workflows offer the potential for quicker pathogen characterization and tracking of transmission, which could lead to better infection control and improved use of antibiotics.
objectiveTo review rapid and POC WGS technologies and workflows used in hospital settings, and to summarize evidence on typical turnaround times, impacts on outbreak detection, and implications for clinical decision-making.
methodA narrative review was conducted using literature retrieved from major biomedical databases, including PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar. Relevant studies addressing whole-genome sequencing, rapid/near-real-time sequencing, healthcare-associated infections, outbreak investigation, and hospital infection control were identified and reviewed.
resultsStreamlined WGS workflows cut turnaround times from several days (typical of traditional methods) to between 13.5 and 48 h in many rapid implementations. Some nanopore-based protocols report initial species identification in minutes and complete genomic data within a few hours. Faster sequencing and real-time analytics lead to earlier identification of resistance markers and quicker detection of transmission chains. Several studies indicate outbreak detection is easier than with traditional epidemiological methods. This allows for more timely targeted therapy and can enhance the responsible use of antibiotics when combined with clinical decision processes.
conclusionNear-real-time and point-of-care whole-genome sequencing significantly reduces the time needed to identify pathogens and speeds up outbreak detection. However, it requires consistent reporting of turnaround metrics, and careful evaluation of how faster genomic data affects clinical decisions and patient outcomes.
Indexed as
Identifiers
42622934What Socratic holds
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 Socratic graph.