An integrated clinical and genetic model for predicting risk of severe COVID-19: A population-based case–control study

Abstract

Up to 30% of people who test positive to SARS-CoV-2 will develop severe COVID-19 and require hospitalisation. Age, gender, and comorbidities are known to be risk factors for severe COVID-19 but are generally considered independently without accurate knowledge of the magnitude of their effect on risk, potentially resulting in incorrect risk estimation. 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. Clinical 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). A model incorporating the SNP score and clinical risk factors (AUC = 0.786; 95% confidence interval = 0.763 to 0.808) had 111% better discrimination of disease severity than a model with just age and gender (AUC = 0.635; 95% confidence interval = 0.607 to 0.662). 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. We have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone (or only clinical factors) 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.

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

“…Epidemiological analyses have recognised that male sex and increasing age are risk factors for severe COVID-19 and that common medical comorbidities contribute to individual risk [ 6 – 8 ]. Our previous analysis showed that the effects of sex and age are attenuated when comorbidities are taken into account [ 9 ]. The effect of human genetic variation on COVID-19 severity has been examined by the COVID-19 Host Genetics Initiative, which has now released several meta-analyses of the available genome-wide association studies of COVID-19 severity [ 10 , 11 ].…”

“…We previously developed a prototype risk model [ 9 ] based upon early data from the UK Biobank [ 16 , 17 ] and SNPs identified from the COVID-19 Host Genetics Initiative Release 2 meta-analysis of hospitalised vs. non-hospitalised COVID-19 cases (which was at that time almost exclusively UK Biobank samples) [ 10 , 18 ]. Our prototype model appeared to perform well but was based on a small sample size from the first wave of the pandemic [ 9 ].…”

“…We previously developed a prototype risk model [ 9 ] based upon early data from the UK Biobank [ 16 , 17 ] and SNPs identified from the COVID-19 Host Genetics Initiative Release 2 meta-analysis of hospitalised vs. non-hospitalised COVID-19 cases (which was at that time almost exclusively UK Biobank samples) [ 10 , 18 ]. Our prototype model appeared to perform well but was based on a small sample size from the first wave of the pandemic [ 9 ]. We decided not to attempt validation in that dataset because of our concern about the representativeness of the data (the SARS-CoV-2 testing data was ascertained early in the pandemic when the limitations on testing availability in the United Kingdom meant that mild and asymptomatic cases were not identified) and because Release 2 results from the COVID-19 Host Genetics Initiative were predominately from UK Biobank samples.…”

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

“…33In the development of the new model, the strongest risk factor was respiratory disease (with an odds per standard deviation of 1.35;Table 3). The older age groups (70-74, 75-79, and 80+ years) and being male all had odds per standard deviations of 1.20 to1.29. The other risk factors (the seven SPNs, ethnicity, body mass index, cancer history (haematological and non-haematological), cerebrovascular disease, diabetes, hypertension, and kidney disease) all had odds per adjusted standard deviations in the range 1.07 to 1.13 (or the equivalent protective effect).…”

“…The COVID-19 pandemic continues to dominate global public health, with countries having varying success with infection control measures and social distancing protocols, 1 Coupled with this are the logistical challenges with the distribution of vaccines 2 and the emergence of SARS-CoV-2 variants of concern. 3,4 Of those who become infected with SARS-CoV-2, 10%-15% will develop severe COVID-19 requiring hospitalisation and 5%…”