Evidence map›Paper›PMID 34210368›Full record

ArticleEpidemiology and infection2021

Development and validation of a clinical and genetic model for predicting risk of severe COVID-19.

Gillian S Dite, Nicholas M Murphy, Richard Allman

Abstract read
In one paragraph

Article in Epidemiology and infection, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Transmembrane protease serine 2 (Life science alliance · 2022
    Article
  10. Host genetic loci LZTFL1 and CCL2 associated with SARS-CoV-2 infection and severity of COVID-19.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2022
    Article
  11. The human genetic epidemiology of COVID-19.Nature reviews. Genetics · 2022
    Review
  12. Article
  13. Article
  14. Review
  15. Article
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

3 authors.

Gillian S DiteGenetic Technologies Ltd, Fitzroy, Victoria, Australia.ORCID 0000-0002-2448-2548
Nicholas M MurphyGenetic Technologies Ltd, Fitzroy, Victoria, Australia.ORCID 0000-0001-7881-1952
Richard AllmanGenetic Technologies Ltd, Fitzroy, Victoria, Australia.ORCID 0000-0002-8635-5237

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical and genetic risk factors for severe coronavirus disease 2019 (COVID-19) are often considered independently and without knowledge of the magnitudes of their effects on risk. Using severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) positive participants from the UK Biobank, we developed and validated a clinical and genetic model to predict risk of severe COVID-19. We used multivariable logistic regression on a 70% training dataset and used the remaining 30% for validation. We also validated a previously published prototype model. In the validation dataset, our new model was associated with severe COVID-19 (odds ratio per quintile of risk = 1.77, 95% confidence interval (CI) 1.64-1.90) and had acceptable discrimination (area under the receiver operating characteristic curve = 0.732, 95% CI 0.708-0.756). We assessed calibration using logistic regression of the log odds of the risk score, and the new model showed no evidence of over- or under-estimation of risk (α = -0.08; 95% CI -0.21-0.05) and no evidence or over-or under-dispersion of risk (β = 0.90, 95% CI 0.80-1.00). Accurate prediction of individual risk is possible and will be important in regions where vaccines are not widely available or where people refuse or are disqualified from vaccination, especially given uncertainty about the extent of infection transmission among vaccinated people and the emergence of SARS-CoV-2 variants of concern.

Indexed as

COVID-19Models, GeneticAgedAged, 80 and overComorbidityFemaleHumansMaleModels, StatisticalPolymorphism, Single NucleotideReproducibility of ResultsRisk AssessmentROC CurveSARS-CoV-2Severity of Illness IndexRisk factorsrisk predictionsevere COVID-19single-nucleotide polymorphism

Identifiers

PMID34210368
PMCPMC8292840

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.