Combining viral genomics and clinical data to assess risk factors for severe COVID-19 (mortality, ICU admission, or intubation) amongst hospital patients in a large acute UK NHS hospital Trust.

Throughout the COVID-19 pandemic, valuable datasets have been collected on the effects of the virus SARS-CoV-2. In this study, we combined whole genome sequencing data with clinical data (including clinical outcomes, demographics, comorbidity, treatment information) for 929 patient cases seen at a large UK hospital Trust between March 2020 and May 2021. We identified associations between acute physiological status and three measures of disease severity; admission to the intensive care unit (ICU), requirement for intubation, and mortality. Whilst the maximum National Early Warning Score (NEWS2) was moderately associated with severe COVID-19 (A = 0.48), the admission NEWS2 was only weakly associated (A = 0.17), suggesting it is ineffective as an early predictor of severity. Patient outcome was weakly associated with myriad factors linked to acute physiological status and human genetics, including age, sex and pre-existing conditions. Overall, we found no significant links between viral genomics and severe outcomes, but saw evidence that variant subtype may impact relative risk for certain sub-populations. Specific mutations of SARS-CoV-2 appear to have little impact on overall severity risk in these data, suggesting that emerging SARS-CoV-2 variants do not result in more severe patient outcomes. However, our results show that determining a causal relationship between mutations and severe COVID-19 in the viral genome is challenging. Whilst improved understanding of the evolution of SARS-CoV-2 has been achieved through genomics, few studies on how these evolutionary changes impact on clinical outcomes have been seen due to complexities associated with data linkage. By combining viral genomics with patient records in a large acute UK hospital, this study represents a significant resource for understanding risk factors associated with COVID-19 severity. However, further understanding will likely arise from studies of the role of host genetics on disease progression.

Comparison of breath-guards and face-masks on droplet spread in eye clinics.

INTRODUCTION: COVID-19 has impacted ophthalmic care delivery, with many units closed and several ophthalmologists catching COVID-19. Understanding droplet spread in clinical and training settings is paramount in maintaining productivity, while keeping patients and practitioners safe. OBJECTIVES: We aimed to assess the effectiveness of a breath-guard and a face mask in reducing droplet spread within an eye clinic. METHODS: We performed a randomised trial of droplet spread using a fluorescein-based cough model to assess the efficacy of a 'breath-guard' and 'face-mask' to prevent the spread of droplets. The 'cough' spray was collected on calibrated paper targets. The sheets were photographed under blue light, with an orange filter on the camera; the position and size of the spots was measured with software originally developed for astronomy. We performed 44 randomised coughs; 22 controls with no breath-guard or face-mask, 11 using breath-guard only and 11 with combined breath-guard and face-mask. We compared both the number of droplets detected and the area of drops on paper targets. RESULTS: The average number of droplets in the controls was 19,430 (SE 2691), the breath-guard group 80 (SE 19) droplets (P < 0.001); in the combined In the group the count was 5 (SE 2), a significant drop from shield only (P = 0.008). The mean areas of each target covered by spots for each group were 5.7 ± 0.857% (95% CI), 0.004 ± 0.000104% (95% CI) and 0.001 ± 0.0000627% (95% CI) respectively. CONCLUSION: These results show that the breath-guard alone reduced the droplet count by 99.93%. Combining the breath-guard with a face-mask reduced the droplet count by over 99.98%. Breath-guards are widely used in clinics and this trial demonstrates that breath-guards with face-masks effectively block droplet spray.