You are here

Article Text

Article menu
XML
Epidemiology
Original research
Covidogram as a simple tool for predicting severe course of COVID-19: population-based study
  1. Jiri Jarkovsky1,2,
  2. Klara Benesova1,2,
  3. Vladimir Cerny3,4,
  4. Jarmila Razova5,
  5. Petr Kala6,7,
  6. Jiri Dolina6,8,
  7. Ondrej Majek1,2,
  8. Silvie Sebestova2,
  9. Monika Bezdekova2,
  10. Hana Melicharova2,
  11. Lenka Snajdrova1,2,
  12. Ladislav Dusek1,2,
  13. Jiri Parenica2,6,7
  1. 1Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic
  2. 2Institute of Health Information and Statistics of the Czech Republic, Praha, Czech Republic
  3. 3Department of Anaesthesiology, Perioperative Medicine and Intensive Care, J.E. Purkinje University and Masaryk Hospital, Usti nad Labem, Czech Republic
  4. 4Department of Anesthesia, Pain Management and Perioperative Medicine, Dalhousie University, Halifax, Nova Scotia, Canada
  5. 5Ministry of Health of the Czech Republic, Praha, Czech Republic
  6. 6Faculty of Medicine, Masaryk University, Brno, Czech Republic
  7. 7Internal and Cardiology Department, University Hospital Brno, Brno, Czech Republic
  8. 8Gastroenterology and Internal Department, University Hospital Brno, Brno, Czech Republic
  1. Correspondence to Professor Jiri Parenica; jiri.parenica{at}atlas.cz

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.

  • COVID-19
  • gastroduodenal disease
  • organisation of health services
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2020-045442

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    View Full Text

    Supplementary materials

    • Supplementary Data

      This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

    Footnotes

    • Contributors JJ, LD, VC and JR designed the study and wrote the research plan. OM, SS, MB and HM extracted the data used for the study from the databases. JJ and KB undertook statistical analysis with feedback from LD. JP, JJ, PK and JD interpreted the results and wrote the first draft of the manuscript with critical comments and revision from VC, LD and LS.

    • Funding This research was supported by grant the Czech Republic Operational Programme eHealth and Rare Disease CZ.03.4.74/0.0/0.0/15_025/.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available on reasonable request. The anonymised data available on reasonable request. The data are deidentified participant data and available from the first author JJ (jarkovsky@uzis.cz). The reuse of the data subset is permitted only for revalidation of the results.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.