Midregional proatrial naturetic peptide (MRproANP) and copeptin (COPAVP) as predictors of all-cause mortality in recently diagnosed mild to moderate COPD—results from COSYCONET

Autor: S. Fähndrich, C. Herr, S. Teuteberg, P. Alter, S. Söhler, D. Soriano, J. Classen, J. Adams, V. Weinhold, H. Watz, B. Waschki, T. Zeller, M. Eichenlaub, F. C. Trudzinski, J. D. Michels, A. Omlor, F. Seiler, I. Moneke, F. Biertz, D. Stolz, T. Welte, H. U. Kauczor, K. Kahnert, R. A. Jörres, C. F. Vogelmeier, R. Bals, the German COSYCONET Cohort
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Respiratory Research, Vol 25, Iss 1, Pp 1-9 (2024)
Druh dokumentu: article
ISSN: 1465-993X
DOI: 10.1186/s12931-024-02690-9
Popis: Abstract Background MRproANP and COPAVP are prognostic markers for mortality in chronic obstructive pulmonary disease (COPD). Furthermore, these biomarkers predict mortality due to cardiovascular diseases, which are important prognostically determining comorbidities in patients with COPD. However, less is known about these biomarkers in recently diagnosed mild to moderate COPD. Therefore, we analyzed these biomarkers as potential predictors of mortality in recently diagnosed mild to moderate COPD. Methods The blood biomarkers considered were copeptin (COPAVP), midregional adrenomedullin (MRproADM), midregional proatrial naturetic peptide (MRproANP), and fibrinogen. Analyses were performed in patients with stable “recently diagnosed mild to moderate COPD” defined by GOLD grades 0–2 and diagnosis of COPD ≤ 5 years prior to inclusion into the COSYCONET cohort (COPD and Systemic Consequences—Comorbidities Network), using Cox regression analysis with stepwise adjustment for multiple COPD characteristics, comorbidities, troponin and NT-proBNP. Results 655 patients with recently diagnosed mild to moderate COPD were included. In the initial regression model, 43 of 655 patients died during the 6-year follow-up, in the final model 27 of 487. Regression analyses with adjustment for confounders identified COPAVP and MRproANP as statistically robust biomarkers (p
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