Scholarlink

Background <p>COVID-19 continuously causes severe disease conditions and significant mortality. We evaluate whether easily accessible biomarkers can improve risk prediction of severe disease outcomes.</p> Methods <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.</p> Results <p>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).</p> Conclusion <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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月19日

查看全文
  • Imrana Farhat,
  • Maciej Rosolowski,
  • Katharina Ahrens,
  • Jasmin Lienau,
  • Peter Ahnert,
  • Mathias Pletz,
  • Gernot Rohde,
  • Jan Rupp,
  • Martin Witzenrath,
  • Markus Scholz

摘要

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.