Evidence map›Paper›PMID 42200756›Full record

ReviewCritical care explorations2026

Rapid Microbiological Diagnostics for Sepsis: Narrative Review of Current and Prospective Approaches.

Luke B Harrison, Zahra N Sohani, David Lasry, Mathew P Cheng, Todd C Lee, Ahmed Babiker, Sameer S Kadri, Alexander Lawandi

Abstract readReview
In one paragraph

Review in Critical care explorations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Luke B HarrisonDivision of Infectious Diseases, Department of Medicine, McGill University Health Center, Montréal, QC, Canada.
Zahra N SohaniDivision of Infectious Diseases, Department of Medicine, McGill University Health Center, Montréal, QC, Canada.
David LasryDivision of Infectious Diseases, Department of Medicine, McGill University Health Center, Montréal, QC, Canada.
Mathew P ChengDivision of Infectious Diseases, Department of Medicine, McGill University Health Center, Montréal, QC, Canada.
Todd C LeeDivision of Infectious Diseases, Department of Medicine, McGill University Health Center, Montréal, QC, Canada.
Ahmed BabikerDivision of Infectious Diseases, Department of Medicine, Rush University Medical Center, Chicago, IL.
Sameer S KadriCritical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD.
Alexander LawandiDivision of Infectious Diseases, Department of Medicine, McGill University Health Center, Montréal, QC, Canada.ORCID 0000-0001-7948-3161

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesIn this review, we aim to provide critical care clinicians with a concise introduction to the current and prospective tools that exist for rapid diagnostics in sepsis employed in the microbiology laboratory. Our objective is to provide a primer for clinicians to engage with their colleagues in the microbiology laboratory for the selection and implementation of new and emerging tools. DATA SOURCES: The primary literature, restricted to peer-reviewed sources, was queried using relevant search terms (e.g., sepsis, rapid diagnostics, microbiology, etc) using PubMed and Google Scholar (until February 2025), as well as review of citations of relevant articles. STUDY SELECTION: After initial searches, literature was screened by each author responsible for the sections of this review: blood culture-based methods (L.B.H.), nonblood culture-based molecular diagnostics (D.L.), and antigen-based methods (Z.N.S.). Titles and abstracts of individual articles were reviewed by the respective section authors and articles describing microbiological diagnostic techniques that decrease the turnaround time for the identification of microorganisms and/or antimicrobial susceptibility testing with relevance to the diagnosis of sepsis were retained. DATA EXTRACTION: Data from individual studies was extracted by each respective section author using Zotero reference management software and synthesized narratively. DATA SYNTHESIS: Rapid diagnostics for sepsis can be broadly divided into three categories: those applied to incubated positive blood culture specimens, and culture-independent approaches applied directly to clinical specimens, which can be further divided into those based on the direct detection of the nucleic acids of microorganisms, and those based on the detection of antigens. Blood culture-based approaches rely on biological amplification of microorganisms present but aim to measure this amplified signal directly to speed identification of microorganisms or antimicrobial resistance relative to traditional plate-culture-based workflows. Nucleic acid and antigen detection methods can be performed directly on clinical specimens, and so promise more rapid diagnostics in sepsis, but with method-specific tradeoffs in sensitivity, specificity, and interpretation.

conclusionsEvolutionary refinements of blood culture-based diagnostic approaches have decreased time to actionable information significantly while emerging and established culture-independent approaches can reduce time to actionable information to a few hours. In aggregate these interventions may have important clinical benefits, yet significant heterogeneity exists in the applicability and availability of technologies.

Indexed as

Microbiological TechniquesSepsisBlood CultureHumansMolecular Diagnostic TechniquesRapid Diagnostic Testsantimicrobial susceptibility testingblood culturemetagenomic sequencingmolecular diagnosticsrapid diagnosticssepsis

Identifiers

PMID42200756
PMCPMC13215682

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.