Development and validation of a clinical and genetic model for predicting risk of severe COVID-19 (preprint)

ABSTRACT

Age, sex, and comorbidities are known risk factors for severe COVID-19 but are frequently considered independently and without accurate knowledge of the magnitude of their effects on risk. Single-nucleotide polymorphisms (SNPs) associated with risk of severe COVID-19 have appeared in the literature, but their application in predictive risk testing has not been validated. Reliance on age and sex alone to determine risk of severe COVID-19 will fail to accurately quantify risk. Here, we report the development and validation of a clinical and genetic model to predict risk of severe COVID-19 using confirmed SARS-CoV-2 positive participants from the UK Biobank. Our new model out-performed an age and sex model and had excellent discrimination and was well calibrated in the validation dataset. We also report validation studies of our prototype model and polygenic risk scores based on 8-SNP and 6-SNP panels identified in the literature. Accurate prediction of individual risk 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.