Comorbidity patterns associated with severe COVID-19 outcomes: A cohort study based on the UK Biobank

Abstract

Background Pre-existing comorbidities are linked to increased risk of severe COVID-19, but comprehensive assessments of comorbidity patterns remain limited. Methods We used network analysis to identify pre-existing comorbidity modules (i.e., groups of diseases more densely interconnected with each other than with other diseases in the comorbidity network) in a cohort of 420,920 individuals from the UK Biobank who were in England. We defined cases requiring hospitalization or who died of COVID-19 as “severe COVID-19”. Logistic regression was used to examine associations between comorbidity modules and severe COVID-19, and a module-based comorbidity index was developed to predict severe COVID-19, compared with existing indices. Results Comorbidity network analysis identified 190 disease pairs with confirmed comorbidity associations, which were further divided into seven comorbidity modules. Among the 30,914 individuals diagnosed with COVID-19, 3,970 were identified as severe cases (median age of 73.6 years, 58.77% being male). Six of seven identified modules showed statistically significant associations with severe COVID-19, especially modules related to circulatory and respiratory diseases (odds ratio = 1.67 [95% confidence interval 1.54–1.81]) and age-related eye diseases (1.39 [1.27–1.52]). Associations did not differ by sex, age or vaccination status but were generally stronger during the first wave of COVID-19 pandemic (i.e., 31st January-1st October, 2020). Our newly developed module-based comorbidity index showed better performance in predicting severe COVID-19 (AUC = 0.779) compared to the existing Charlson Comorbidity Index (0.714) and the 16-comorbidity index (0.714). Conclusions Our study demonstrated that pre-existing comorbidity modules, particularly modules related to circulatory and respiratory diseases and age-related eye diseases, were associated with severe COVID-19. Moreover, the module-based comorbidity index provides better prediction of severe COVID-19 than existing prediction indices.