Evidence map›Paper›PMID 42005923›Full record

ReviewEClinicalMedicine2026

Challenges in early detection and prognostication of sepsis: new approaches from the emergency department and intensive care unit.

Thomas Lafon, Melanie Weingart, Julien Vaidie, Carolyn S Calfee, Shevin T Jacob, Yonathan Freund, Nathan I Shapiro, Olivier Barraud, Guillaume Monneret, Tom van der Poll and 2 more

Abstract readReview
In one paragraph

Review in EClinicalMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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

  1. Pooled it
  2. Review
  3. Article
  4. Observational
  5. Review
  6. Article
  7. Review
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

12 authors.

Thomas LafonEmergency Department, CHU Limoges, Limoges, France.
Melanie WeingartDivision of Pulmonary, Critical Care, Allergy and Sleep Medicine, University of California, San Francisco, San Francisco, CA, USA.
Julien VaidieInserm CIC 1435, CHU Limoges, Limoges, France.
Carolyn S CalfeeDivision of Pulmonary, Critical Care, Allergy and Sleep Medicine, University of California, San Francisco, San Francisco, CA, USA.
Shevin T JacobDepartment of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
Yonathan FreundSorbonne Université, IMProving Emergency Care FHU, Paris, France.
Nathan I ShapiroDepartment of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Olivier BarraudInserm CIC 1435, CHU Limoges, Limoges, France.
Guillaume MonneretImmunology Laboratory, Edouard Herriot Hospital - Hospices Civils de Lyon, Lyon, France.
Tom van der PollCenter of Infection and Molecular Medicine, Division of Infectious Diseases, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
Yeleen FromageInserm CIC 1435, CHU Limoges, Limoges, France.
Bruno FrançoisInserm CIC 1435, CHU Limoges, Limoges, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this narrative review, we aimed to provide a comprehensive overview of emerging diagnostic strategies and precision medicine approaches in sepsis, while explicitly acknowledging the heterogeneity of clinical contexts. In the Emergency Department (ED), timely recognition of infection and sepsis represents one of the most frequent and challenging tasks, which may delay management directly increasing morbidity and mortality. Even if very popular and widely used, traditional scores and routine biomarkers remain of limited interest to confirm diagnosis and predict deterioration. Nevertheless, emerging point-of-care tools hold promise such as "real-time microbiology", bedside immune profiling, and echocardiography for on-time hemodynamic phenotyping. More advanced strategies, such as omics technologies and transcriptomic signatures, offer deeper biological precision, while machine learning and artificial intelligence can integrate high-dimensional ED data to anticipate deterioration and capture the dynamic evolution of sepsis subphenotypes. Many of these tools are already feasible at the bedside and only await integration into routine ED workflows. Embedding them within dedicated sepsis pathways and multidisciplinary teams could optimize global patient care and accelerate the transition toward precision medicine in acute sepsis. Sustainable improvements in sepsis outcomes will most likely not come from isolated devices but from their integration into coordinated and sepsis-specific pathways.

Indexed as

Artificial intelligenceDiagnosisEmergency departmentPhenotypesSepsis

Identifiers

PMID42005923
PMCPMC13090662

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.