Evidence map›Paper›PMID 41157190›Full record

ReviewLife (Basel, Switzerland)2025

The 15th Anniversary of

Jean-Louis Vincent

Abstract readReview
In one paragraph

Review in Life (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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

1 author.

Jean-Louis VincentDepartment of Intensive Care, Erasme Hospital, Hôpital Universitaire de Bruxelles (HUB), Universite Libre de Bruxelles (ULB), 1070 Brussels, Belgium.ORCID 0000-0001-6011-6951

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical trials of drugs specifically targeting the sepsis response have frequently produced negative or inconclusive results. This has largely been due to the broad heterogeneity of enrolled patient populations, particularly when inclusion was based on the presence of the non-specific systemic inflammatory response syndrome (SIRS) criteria. The heterogeneity may have diluted any possible treatment effect: while some patients may have benefited from the intervention under investigation, others will have been harmed, resulting in an overall null-effect. Furthermore, an underlying infection is not always required for immune-modulating interventions to be effective; for example, patients with severe acute pancreatitis, but no infection, may still benefit from such therapies. There is therefore a need for better patient stratification or subphenotyping to identify those most likely to benefit from a particular therapy. Several trials have already adopted this approach using prognostic and/or predictive enrichment strategies. For example, measurements of triggering receptor expressed on myeloid cells (TREM) could be used to identify candidates most likely to respond to anti-TREM therapies, or patients with coagulopathy or specific inflammatory patterns could be selected for treatments like thrombomodulin or activated protein C. However, challenges remain, including the need for more rapid tools that can be used at the bedside to inform real-time treatment decisions given the rapidly evolving nature of the sepsis response. Nevertheless, the era of broad-spectrum "sepsis drugs" is now giving way to more selective, personalized interventions tailored to individual biological profiles, offering a more promising pathway for future therapeutic development-even in the absence of infection.

Indexed as

biomarkersheterogeneitypersonalizationsubphenotypes

Identifiers

PMID41157190
PMCPMC12565336

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.