Development and validation of a clinical and genetic model for predicting risk of severe COVID-19.
Epidemiol Infect
; 149: e162, 2021 07 02.
Article
in English
| MEDLINE | ID: covidwho-1294409
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
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 (ß = 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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Risk Assessment
/
COVID-19
/
Models, Genetic
Type of study:
Observational study
/
Prognostic study
Topics:
Vaccines
/
Variants
Limits:
Aged
/
Female
/
Humans
/
Male
Language:
English
Journal:
Epidemiol Infect
Journal subject:
Communicable Diseases
/
Epidemiology
Year:
2021
Document Type:
Article
Affiliation country:
S095026882100145X
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