Evidence map›Paper›PMID 40828447›Full record

ArticleInfection2025

Endothelin-1 in combination with CRB-65 enhance risk stratification in COVID-19 patients.

Imrana Farhat, Maciej Rosolowski, Katharina Ahrens, Jasmin Lienau, Peter Ahnert, Mathias Pletz, Gernot Rohde, Jan Rupp, Martin Witzenrath, Markus Scholz and 2 more

Abstract read
In one paragraph

Article in Infection, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Trial
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.

Imrana FarhatInstitute for Medical Informatics, Statistics, and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany.
Maciej RosolowskiInstitute for Medical Informatics, Statistics, and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany.
Katharina AhrensDepartment of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Jasmin LienauDepartment of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Peter AhnertInstitute for Medical Informatics, Statistics, and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany.
Mathias PletzInstitute of Infectious Diseases and Infection Control and Center for Sepsis Care and Control, Jena University Hospital/ Friedrich Schiller University Jena, Jena, Germany.
Gernot RohdeCAPNETZ STIFTUNG, Hannover, Germany.
Jan RuppCAPNETZ STIFTUNG, Hannover, Germany.
Martin Witzenrath *Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Markus Scholz *Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany. Markus.Scholz@imise.uni-leipzig.de.
CAPNETZ Study Group
PROGRESS Study Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 continuously causes severe disease conditions and significant mortality. We evaluate whether easily accessible biomarkers can improve risk prediction of severe disease outcomes.

methodsOur study analysed 426 COVID-19 patients collected by German CAPNETZ and PROGRESS study groups between 2020 and 2021. Troponin T high-sensitive (TnT-hs), procalcitonin (PCT), N-terminal pro brain natriuretic peptide, angiopoietin-2, copeptin, endothelin-1 (ET-1) and lipocalin-2 were measured at enrolment and related to 28d mortality/ICU admission endpoint. Logistic and relaxed LASSO regression were used to evaluate the added value of biomarkers compared to the CRB-65 score and to develop a combined risk prediction model for our endpoint.

resultsOf the 426 COVID-19 patients, 64 (15%) reached the endpoint. Among individual biomarkers, ET-1 showed the highest predictive performance (AUC = 0.76, 95% CI: 0.70-0.82). CRB-65 alone had an AUC of 0.63 (95% CI: 0.56-0.70). Our machine learning method identified CRB-65 + ET-1 to be optimal for prediction performance and model sparsity (AUC = 0.77, 95% CI: 0.71-0.83). Decision curve analysis demonstrated its greater net benefit over CRB-65 across large range of risk thresholds. The generalizability of our non-COVID CAP model (CRB-65 + TnT-hs + PCT) to COVID-19 patients was also assessed, yielding an AUC of 0.67 (95% CI: 0.60-0.74) for our primary endpoint. For 28d mortality alone as endpoint, it performed remarkably well (AUC = 0.90, 95% CI: 0.85-0.95).

conclusionCombining the already established clinical CRB-65 score with ET-1 significantly improves risk prediction of intensive care requirement or death within 28 days in hospitalized COVID-19 patients.

Indexed as

COVID-19Endothelin-1AgedAged, 80 and overBiomarkersFemaleGermanyHumansMaleMiddle AgedRisk AssessmentSARS-CoV-2Severity of Illness IndexBiomarkersEndothelin-1BiomarkersCOVID-19CRB-65Risk prediction

Identifiers

PMID40828447
PMCPMC12675736

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.