Dataset Information

Covidogram as a simple tool for predicting severe course of COVID-19: population-based study.

ABSTRACT:

Objectives

COVID-19 might either be entirely asymptomatic or manifest itself with a large variability of disease severity. It is beneficial to identify early patients with a high risk of severe course. The aim of the analysis was to develop a prognostic model for the prediction of the severe course of acute respiratory infection.

Design

A population-based study.

Setting

Czech Republic.

Participants

The first 7455 consecutive patients with COVID-19 who were identified by reverse transcription-PCR testing from 1 March 2020 to 17 May 2020.

Primary outcome

Severe course of COVID-19.

Result

Of a total 6.2% of patients developed a severe course of COVID-19. Age, male sex, chronic kidney disease, chronic obstructive pulmonary disease, recent history of cancer, chronic heart failure, acid-related disorders treated with proton-pump inhibitors and diabetes mellitus were found to be independent negative prognostic factors (Area under the ROC Curve (AUC) was 0.893). The results were visualised by risk heat maps, and we called this diagram a 'covidogram'. Acid-related disorders treated with proton-pump inhibitors might represent a negative prognostic factor.

Conclusion

We developed a very simple prediction model called 'covidogram', which is based on elementary independent variables (age, male sex and the presence of several chronic diseases) and represents a tool that makes it possible to identify-with a high reliability-patients who are at risk of a severe course of COVID-19. Obtained results open clinically relevant question about the role of acid-related disorders treated by proton-pump inhibitors as predictor for severe course of COVID-19.

SUBMITTER: Jarkovsky J

PROVIDER: S-EPMC7907625 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Covidogram as a simple tool for predicting severe course of COVID-19: population-based study.

Jarkovsky Jiri J Benesova Klara K Cerny Vladimir V Razova Jarmila J Kala Petr P Dolina Jiri J Majek Ondrej O Sebestova Silvie S Bezdekova Monika M Melicharova Hana H Snajdrova Lenka L Dusek Ladislav L Parenica Jiri J

BMJ open 20210223 2

<h4>Objectives</h4>COVID-19 might either be entirely asymptomatic or manifest itself with a large variability of disease severity. It is beneficial to identify early patients with a high risk of severe course. The aim of the analysis was to develop a prognostic model for the prediction of the severe course of acute respiratory infection.<h4>Design</h4>A population-based study.<h4>Setting</h4>Czech Republic.<h4>Participants</h4>The first 7455 consecutive patients with COVID-19 who were identified ...[more]

PMID: 33622955

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Project description:BackgroundThe severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes a respiratory illness named coronavirus disease 2019 (COVID-19), which is one of the main global health problems since 2019. Glycans attached to the Fc portion of immunoglobulin G (IgG) are important modulators of IgG effector functions. Fc region binds to different receptors on the surface of various immune cells, dictating the type of immune response. Here, we performed a large longitudinal study to determine whether the severity and duration of COVID-19 are associated with altered IgG glycosylation.MethodsUsing ultra-high-performance liquid chromatography analysis of released glycans, we analysed the composition of the total IgG N-glycome longitudinally during COVID-19 from four independent cohorts. We analysed 77 severe COVID-19 cases from the HR1 cohort (74% males, median age 72, age IQR 25-80); 31 severe cases in the HR2 cohort (77% males, median age 64, age IQR 41-86), 18 mild COVID-19 cases from the UK cohort (17% males, median age 50, age IQR 26-71) and 28 mild cases from the BiH cohort (71% males, median age 60, age IQR 12-78).FindingsMultiple statistically significant changes in IgG glycome composition were observed during severe COVID-19. The most statistically significant changes included increased agalactosylation of IgG (meta-analysis 95% CI [0.03, 0.07], adjusted meta-analysis P= <0.0001), which regulates proinflammatory actions of IgG via complement system activation and indirectly as a lack of sialylation and decreased presence of bisecting N-acetylglucosamine on IgG (meta-analysis 95% CI [-0.11, -0.08], adjusted meta-analysis P= <0.0001), which indirectly affects antibody-dependent cell-mediated cytotoxicity. On the contrary, no statistically significant changes in IgG glycome composition were observed in patients with mild COVID-19.InterpretationThe IgG glycome in severe COVID-19 patients is statistically significantly altered in a way that it indicates decreased immunosuppressive action of circulating immunoglobulins. The magnitude of observed changes is associated with the severity of the disease, indicating that aberrant IgG glycome composition or changes in IgG glycosylation may be an important molecular mechanism in COVID-19.FundingThis work has been supported in part by Croatian Science Foundation under the project IP-CORONA-2020-04-2052 and Croatian National Centre of Competence in Molecular Diagnostics (The European Structural and Investment Funds grant #KK.01.2.2.03.0006), by the UKRI/MRC (Cov-0331 - MR/V027883/1) and by the National Institutes for Health Research Nottingham Biomedical Research Centre and by Ministry Of Science, Higher Education and Youth Of Canton Sarajevo, grant number 27-02-11-4375-10/21.

Dataset Information

Covidogram as a simple tool for predicting severe course of COVID-19: population-based study.

Objectives

Design

Setting

Participants

Primary outcome

Result

Conclusion

Publications

Covidogram as a simple tool for predicting severe course of COVID-19: population-based study.

Similar Datasets

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