Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 May;52(5):e13753.
doi: 10.1111/eci.13753. Epub 2022 Feb 7.

Prognostic value of copeptin and mid-regional proadrenomedullin in COVID-19-hospitalized patients

Rita Indirli  1   2 , Alessandra Bandera  3 , Luca Valenti  4 , Ferruccio Ceriotti  5 , Adriana Di Modugno  5 , Mauro Tettamanti  6 , Roberta Gualtierotti  7 , Flora Peyvandi  7 , Nicola Montano  8 , Francesco Blasi  9   10 , Giorgio Costantino  11 , Veronica Resi  1 , Emanuela Orsi  1 , Maura Arosio  1   2 , Giovanna Mantovani  1   2 , Emanuele Ferrante  1 ; COVID-19 Network Working Group
Collaborators, Affiliations

Prognostic value of copeptin and mid-regional proadrenomedullin in COVID-19-hospitalized patients

Rita Indirli et al. Eur J Clin Invest. 2022 May.

Abstract

Background: Biomarkers are used for diagnosis, risk stratification and medical decisions. Copeptin and mid-regional proadrenomedullin (MR-proADM) are markers of stress and endothelial function, respectively, which have been studied in pneumonia, sepsis and septic shock. This study aimed to assess whether copeptin and MR-proADM could predict coronavirus disease 2019 (COVID-19) in-hospital outcomes, that is multi-system complications, length of stay and mortality.

Methods: Copeptin and MR-proADM were assessed at admission in 116 patients hospitalized with COVID-19. Data were retrospectively extracted from an online database. The primary endpoint was in-hospital mortality. The secondary endpoints were in-hospital complications, the composite outcome 'death, or admission to intensive care unit, or in-hospital complications', and length of stay. The predictive power was expressed as area under the receiver operator characteristic curve (AUROC).

Results: Copeptin was increased in non-survivors (median 29.7 [interquartile range 13.0-106.2] pmol/L) compared to survivors (10.9 [5.9-25.3] pmol/L, p < 0.01). The AUROC for mortality was 0.71, with a hazard ratio of 3.67 (p < 0.01) for copeptin values > 25.3 pmol/L. MR-proADM differentiated survivors (0.8 [0.6-1.1] nmol/L) from non-survivors (1.5 [1.1-2.8] nmol/L, p < 0.001) and yielded a AUROC of 0.79 and a hazard ratio of 7.02 (p < 0.001) for MR-proADM values > 1.0 nmol/L. Copeptin and MR-proADM predicted sepsis (AUROC 0.95 and 0.96 respectively), acute kidney injury (0.87 and 0.90), the composite outcome (0.69 and 0.75) and length of stay (r = 0.42, p < 0.001, and r = 0.46, p < 0.001).

Conclusions: Admission MR-proADM and copeptin may be implemented for early risk stratification in COVID-19-hospitalized patients to help identify those eligible for closer monitoring and care intensification.

Keywords: COVID-19; biomarkers; copeptin; mid-regional proadrenomedullin; mortality; prognosis.

PubMed Disclaimer

Conflict of interest statement

All authors declare that they have no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Receiver operator characteristic (ROC) curves of copeptin and mid‐regional proadrenomedullin (MR‐proADM) for prediction of in‐hospital mortality (A), sepsis (B), acute kidney injury (C) and the composite outcome ‘death or admission to intensive care unit or complications’ (D). AUC, area under the curve
FIGURE 2
FIGURE 2
Survival analysis according to copeptin and mid‐regional proadrenomedullin (MR‐proADM) concentrations at hospital admission. HR, hazard ratio. CI, confidence interval
See this image and copyright information in PMC

References

    1. Prezioso C, Pietropaolo V. COVID‐19: update of the Italian situation. J Neurovirol. 2020;26(6):834‐837. doi:10.1007/S13365-020-00900-W - DOI - PMC - PubMed
    1. Boschi T, Di Iorio J, Testa L, Cremona MA, Chiaromonte F. Functional data analysis characterizes the shapes of the first COVID‐19 epidemic wave in Italy. Sci Rep. 2021;11(1):17054. doi:10.1038/s41598-021-95866-y - DOI - PMC - PubMed
    1. Qiu P, Zhou Y, Wang F, et al. Clinical characteristics, laboratory outcome characteristics, comorbidities, and complications of related COVID‐19 deceased: a systematic review and meta‐analysis. Aging Clin Exp Res. 2020;32(9):1869‐1878. doi:10.1007/s40520-020-01664-3 - DOI - PMC - PubMed
    1. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus Disease 2019 (COVID‐19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA ‐ J Am Med Assoc. 2020;323(13):1239‐1242. doi:10.1001/jama.2020.2648 - DOI - PubMed
    1. Del Sole F, Farcomeni A, Loffredo L, et al. Features of severe COVID‐19: a systematic review and meta‐analysis. Eur J Clin Invest. 2020;50(10): doi:10.1111/eci.13378 - DOI - PMC - PubMed

Grants and funding