Endothelin-1 in combination with CRB-65 enhance risk stratification in COVID-19 patients.
Background: COVID-19 continuously causes severe disease conditions and significant mortality. We evaluate whether easily accessible biomarkers can improve risk prediction of severe disease outcomes. Methods: Our study analysed 426 COVID-19 patients collected by German CAPNETZ and PROGRESS study grou...
| Published in: | Infection Vol. 53; no. 6; pp. 2769 - 2779 |
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| Main Authors: | , , , , , , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Dec2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189750530&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189750530 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03008126 NXO jtl: Infection issn: 03008126 maglogo: N pubinfo: dt: Dec2025 vid: 53 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189750530 187426338 189750530 189750530 10.1007/s15010-025-02627-4 189750530 ppf: 2769 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Endothelin-1 in combination with CRB-65 enhance risk stratification in COVID-19 patients. aug: au: Farhat, Imrana Rosolowski, Maciej Ahrens, Katharina Lienau, Jasmin Ahnert, Peter Pletz, Mathias Rohde, Gernot Rupp, Jan Witzenrath, Martin Scholz, Markus affil: https://ror.org/03s7gtk40 Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany sug: subj: COVID-19 Mortality Endothelins Blood Biological Markers Blood Clinical Prediction Rules Intensive Care Units Patient Admission Risk Assessment Human Funding Source Male Female Middle Age Prospective Studies Vaccination Status Nonexperimental Studies Enzyme-Linked Immunosorbent Assay Comparative Studies Mann-Whitney U Test ROC Curve Logistic Regression Data Analysis Software Descriptive Statistics Prediction Models Troponin Blood Calcitonin Blood Natriuretic Peptide, Brain Blood Proteins Blood Carrier Proteins Blood Germany Inpatients Confidence Intervals Machine Learning Middle Aged: 45-64 years Male Female ab: Background: COVID-19 continuously causes severe disease conditions and significant mortality. We evaluate whether easily accessible biomarkers can improve risk prediction of severe disease outcomes. Methods: Our 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. Results: Of 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). Conclusion: Combining 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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