Evidence map›Paper›PMID 29490932›Full record

ReviewClinical microbiology reviews2018

Emerging Technologies for Molecular Diagnosis of Sepsis.

Mridu Sinha, Julietta Jupe, Hannah Mack, Todd P Coleman, Shelley M Lawrence, Stephanie I Fraley

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Clinical microbiology reviews, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 162 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
162citing papers in PubMed, 1 pooled it
–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

162 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
  2. Review
  3. Article
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  8. Review
  9. Review
  10. Article
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  12. Observational
  13. Article
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  15. Review
  16. Article
  17. Review
  18. Article
  19. Review
  20. Early Prediction of Septic Shock in Emergency Department Using Serum Metabolites.Journal of the American Society for Mass Spectrometry · 2025
    Article

102 more citing papers are in PubMed but not listed here.

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

6 authors.

Mridu Sinha *Bioengineering Department, University of California, San Diego, San Diego, California, USA.
Julietta Jupe *Donald Danforth Plant Science Center, Saint Louis, Missouri, USA.
Hannah MackBioengineering Department, University of California, San Diego, San Diego, California, USA.
Todd P ColemanBioengineering Department, University of California, San Diego, San Diego, California, USA.
Shelley M LawrenceDepartment of Pediatrics, Division of Neonatal-Perinatal Medicine, University of California, San Diego, San Diego, California, USA slawrence@ucsd.edu sifraley@ucsd.edu.
Stephanie I FraleyBioengineering Department, University of California, San Diego, San Diego, California, USA slawrence@ucsd.edu sifraley@ucsd.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid and accurate profiling of infection-causing pathogens remains a significant challenge in modern health care. Despite advances in molecular diagnostic techniques, blood culture analysis remains the gold standard for diagnosing sepsis. However, this method is too slow and cumbersome to significantly influence the initial management of patients. The swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 h. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilize small sample volumes and detect polymicrobial infections and contaminants. All of this must be accomplished with a platform that is easily integrated into the clinical workflow. In this review, we outline the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. We include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, we discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making.

Indexed as

Bacteriological TechniquesHumansMolecular Diagnostic TechniquesSepsisbiomedical engineeringdiagnosticDNA sequencinginfectious diseasemicrobiology techniques

Identifiers

PMID29490932
PMCPMC5967692

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.