Validation of a clinical and genetic model for predicting severe COVID-19

Lifelines Corona Research initiative, Gillian S. Dite*, Nicholas M. Murphy, Erika Spaeth, Richard Allman

*Corresponding author for this work

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Abstract

Using nested case-control data from the Lifelines COVID-19 cohort, we undertook a validation study of a clinical and genetic model to predict the risk of severe COVID-19 in people with confirmed COVID-19 and in people with confirmed or self-reported COVID-19. The model performed well in terms of discrimination of cases and controls for all ages (area under the receiver operating characteristic curve (AUC) = 0.680 for confirmed COVID-19 and AUC = 0.689 for confirmed and self-reported COVID-19) and in the age group in which the model was developed (50 years and older; AUC = 0.658 for confirmed COVID-19 and AUC = 0.651 for confirmed and self-reported COVID-19). There was no evidence of over- or under-dispersion of risk scores but there was evidence of overall over-estimation of risk in all analyses (all P < 0.0001). In the light of large numbers of people worldwide remaining unvaccinated and continuing uncertainty regarding vaccine efficacy over time and against variants of concern, identification of people at high risk of severe COVID-19 may encourage the uptake of vaccinations (including boosters) and the use of non-pharmaceutical inventions.

Original languageEnglish
Article numbere91
Number of pages4
JournalEpidemiology And Infection
Volume150
DOIs
Publication statusPublished - 25-Apr-2022

Keywords

  • risk factors
  • risk prediction
  • severe COVID-19
  • single-nucleotide polymorphism
  • validation

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