Evidence map›Paper›PMID 41944864›Full record

ArticleIntensive care medicine2026

Measuring signatures of host resistance, disease tolerance, and damage in human sepsis: a prospective cohort study.

Arnab Chowdhury, Rachel E Powell, Jason N Kennedy, Kelly L Urbanek, Derek C Angus, Chung-Chou H Chang, Lu Tang, Sebastian Weis, Michael Bauer, Manu Shankar-Hari and 1 more

Abstract read
In one paragraph

Article in Intensive care medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

11 authors.

Arnab ChowdhuryDepartment of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, USA. chowdhua@pitt.edu.ORCID http://orcid.org/0000-0001-9836-1098
Rachel E PowellThe CRISMA Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, USA.
Jason N KennedyDepartment of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, USA.
Kelly L UrbanekDepartment of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, USA.
Derek C AngusDepartment of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, USA.
Chung-Chou H ChangDepartment of Biostatistics, University of Pittsburgh, Pittsburgh, USA.
Lu TangDepartment of Biostatistics, University of Pittsburgh, Pittsburgh, USA.
Sebastian WeisDepartment of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Friedrich-Schiller University, Jena, Germany.
Michael BauerDepartment of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Friedrich-Schiller University, Jena, Germany.
Manu Shankar-HariCentre for Inflammation Research, University of Edinburgh, Edinburgh, UK.
Christopher W SeymourDepartment of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, USA.

Funding

Multidisciplinary Training in Critical Care Outcomes ResearchT32HL007820 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Timothy D Girard, Jeremy M Kahn · 1996 to 2026
$6.4M
Sepsis subtypes: biology, treatment, and bedside applicationR35GM119519 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Christopher W. Seymour · 2016 to 2026
$4.9M
REMISE study: REMnant biospecimen Investigation in SEpsisR21GM144851 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SEYMOUR, CHRISTOPHER WARREN · 2022 to 2023
$438k
NHLBI NIH HHS T32HL007820NIGMS NIH HHS R21GM144851NIGMS NIH HHS R35GM119519
6 · The paper itself

Abstract

purposeTo understand how protein biomarkers in blood and urine that are aligned with host resistance to infection, disease tolerance, and damage are associated with clinical outcomes and sepsis subtypes in community-onset sepsis.

methodsAdults meeting Sepsis-3 criteria were prospectively enrolled within 6 h of emergency department arrival and assigned clinical subtypes (α, β, γ, δ), using the Sepsis ENdotyping in Emergency CAre (SENECA) approach. Using structured expert ranking with consensus adjudication, 16 plasma and urinary biomarkers obtained from remnant biospecimens were grouped into three mechanistic axes contributing to sepsis pathophysiology: host resistance to infection, disease tolerance, and damage to the host. Biomarkers for each concept were analyzed with principal component analysis as a signature, and multivariable logistic regression tested associations of each signature with 90-day mortality and sepsis subtype membership.

resultsAmong 444 adults, the mean age was 60 years [SD: 16], the mean SOFA score was 4.3 [SD: 2.3], and 90-day mortality was 17%. After adjustment for age, sex, and race, greater damage to the host was associated with increased 90-day mortality (adjusted odds ratio (aOR) = 1.70; 95% CI 1.38-2.11;  p < 0.001), while greater host resistance (aOR = 0.83; 95% CI 0.54-1.10; p = 0.4) and greater disease tolerance (aOR = 0.83; 95% CI 0.68-1.01; p = 0.06) were not. Differences across sepsis subtypes were most pronounced for disease tolerance and damage signatures, where the δ‑type patients exhibited higher damage and lower disease tolerance and the α‑type patients had lower damage and higher disease tolerance.

conclusionBiomarker signatures aligned with host resistance to infection, disease tolerance, and damage to the host, informed by expert consensus, were associated with clinical outcomes and sepsis subtype membership.

Indexed as

Disease ResistanceSepsisAgedBiomarkersFemaleHumansMaleMiddle AgedOrgan Dysfunction ScoresProspective StudiesBiomarkersBiomarkersDamageDisease toleranceHost resistanceSepsis

Identifiers

PMID41944864
PMCPMC13221389

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Registered trials

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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.