Infection. 2025 Aug 19. doi: 10.1007/s15010-025-02627-4. Online ahead of print.
ABSTRACT
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.
PMID:40828447 | DOI:10.1007/s15010-025-02627-4
