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

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.

Genetic associations with severe COVID-19 (preprint)

Identification of host genetic factors that predispose individuals to severe COVID-19 is important, not only for understanding the disease and guiding the development of treatments, but also for risk prediction when combined to form a polygenic risk score (PRS). Using population controls, Pairo-Castineira et al. identified 12 SNPs (a panel of 8 SNPs and a panel of 6 SNPs, with two SNPs in both panels) associated with severe COVID-19. Using controls with asymptomatic or mild COVID-19, we were able to replicate the association with severe COVID-19 for only three of their SNPs and found marginal evidence for an association for one other. When combined as an 8-SNP PRS and a 6-SNP PRS, we found no evidence of association with severe COVID-19. The difference in our results and the results of Pairo-Castineira et al. might be the choice of controls: population controls vs controls with asymptomatic or mild COVID-19.

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

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.

An integrated clinical and genetic model for predicting risk of severe COVID-19 (preprint)

BackgroundAge and gender are often the only considerations in determining risk of severe COVID-19. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk management. MethodsClinical risk factors and a panel of 64 single-nucleotide polymorphisms were identified from published data. We used logistic regression to develop a model for severe COVID-19 in 1,582 UK Biobank participants aged 50 years and over who tested positive for the SARS-CoV-2 virus: 1,018 with severe disease and 564 without severe disease. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC). ResultsA model incorporating the SNP score and clinical risk factors (AUC=0.786) had 111% better discrimination of disease severity than a model with just age and gender (AUC=0.635). The effects of age and gender are attenuated by the other risk factors, suggesting that it is those risk factors - not age and gender - that confer risk of severe disease. In the whole UK Biobank, most are at low or only slightly elevated risk, but one- third are at two-fold or more increased risk. ConclusionsWe have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone to determine risk of severe COVID-19 will unnecessarily classify healthy older people as being at high risk and will fail to accurately quantify the increased risk for younger people with comorbidities.