Evidence map›Paper›PMID 33622955›Full record

ArticleBMJ open2021

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

Jiri Jarkovsky, Klara Benesova, Vladimir Cerny, Jarmila Razova, Petr Kala, Jiri Dolina, Ondrej Majek, Silvie Sebestova, Monika Bezdekova, Hana Melicharova and 3 more

Abstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Jiri JarkovskyInstitute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Klara BenesovaInstitute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Vladimir CernyDepartment of Anaesthesiology, Perioperative Medicine and Intensive Care, J.E. Purkinje University and Masaryk Hospital, Usti nad Labem, Czech Republic.
Jarmila RazovaMinistry of Health of the Czech Republic, Praha, Czech Republic.
Petr KalaFaculty of Medicine, Masaryk University, Brno, Czech Republic.
Jiri DolinaFaculty of Medicine, Masaryk University, Brno, Czech Republic.
Ondrej MajekInstitute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Silvie SebestovaInstitute of Health Information and Statistics of the Czech Republic, Praha, Czech Republic.
Monika BezdekovaInstitute of Health Information and Statistics of the Czech Republic, Praha, Czech Republic.
Hana MelicharovaInstitute of Health Information and Statistics of the Czech Republic, Praha, Czech Republic.
Lenka SnajdrovaInstitute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Ladislav DusekInstitute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Jiri ParenicaInstitute of Health Information and Statistics of the Czech Republic, Praha, Czech Republic jiri.parenica@atlas.cz.ORCID 0000-0003-1065-8351

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesCOVID-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.

designA population-based study.

settingCzech Republic.

participantsThe 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.

resultOf 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.

conclusionWe 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.

Indexed as

COVID-19AdultAgedAged, 80 and overCzech RepublicFemaleHumansMaleMiddle AgedReproducibility of ResultsResearchSARS-CoV-2COVID-19gastroduodenal diseaseorganisation of health services

Identifiers

PMID33622955
PMCPMC7907625

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.