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We evaluate whether easily accessible biomarkers can improve risk prediction of severe disease outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the 426 COVID-19 patients, 64 (15%) reached the endpoint. Among individual biomarkers, ET-1 showed the highest predictive performance (AUC\u2009=\u20090.76, 95% CI: 0.70\u20130.82). CRB-65 alone had an AUC of 0.63 (95% CI: 0.56\u20130.70). Our machine learning method identified CRB-65\u2009+\u2009ET-1 to be optimal for prediction performance and model sparsity (AUC\u2009=\u20090.77, 95% CI: 0.71\u20130.83). 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\u2009+\u2009TnT-hs\u2009+\u2009PCT) to COVID-19 patients was also assessed, yielding an AUC of 0.67 (95% CI: 0.60\u20130.74) for our primary endpoint. For 28d mortality alone as endpoint, it performed remarkably well (AUC\u2009=\u20090.90, 95% CI: 0.85\u20130.95).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s15010-025-02627-4","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T10:45:15Z","timestamp":1755600315000},"page":"2769-2778","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Endothelin-1 in combination with CRB-65 enhance risk stratification in COVID-19 patients"],"prefix":"10.1007","volume":"53","author":[{"given":"Imrana","family":"Farhat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maciej","family":"Rosolowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katharina","family":"Ahrens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasmin","family":"Lienau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Ahnert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathias","family":"Pletz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gernot","family":"Rohde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Rupp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Witzenrath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Scholz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"CAPNETZ Study Group","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"PROGRESS Study Group","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"key":"2627_CR1","unstructured":"World Health Organization. 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S. received funding from Pfizer Inc. for epidemiological modelling of pneumococcal serotypes and vaccinations, and from Owkin for a project not related to this research. M. W. received funding from Aptarion, Biotest and Pantherna for research outside the current study. The remaining authors have nothing to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}