While it is now widely accepted that host inflammatory responses contribute to lung injury, the pathways that drive severity and distinguish coronavirus disease 2019 (COVID-19) from other viral lung diseases remain poorly characterized. We analyzed plasma samples from 471 hospitalized patients recruited through the prospective multicenter ISARIC4C study and 39 outpatients with mild disease, enabling extensive characterization of responses across a full spectrum of COVID-19 severity. Progressive elevation of levels of numerous inflammatory cytokines and chemokines (including IL-6, CXCL10, and GM-CSF) were associated with severity and accompanied by elevated markers of endothelial injury and thrombosis. Principal component and network analyses demonstrated central roles for IL-6 and GM-CSF in COVID-19 pathogenesis. Comparing these profiles to archived samples from patients with fatal influenza, IL-6 was equally elevated in both conditions whereas GM-CSF was prominent only in COVID-19. These findings further identify the key inflammatory, thrombotic, and vascular factors that characterize and distinguish severe and fatal COVID-19.

Relationships between viral load, severity of illness, and transmissibility of virus are fundamental to understanding pathogenesis and devising better therapeutic and prevention strategies for COVID-19. Here we present within-host modelling of viral load dynamics observed in the upper respiratory tract (URT), drawing upon 2172 serial measurements from 605 subjects, collected from 17 different studies. We developed a mechanistic model to describe viral load dynamics and host response and contrast this with simpler mixed-effects regression analysis of peak viral load and its subsequent decline. We observed wide variation in URT viral load between individuals, over 5 orders of magnitude, at any given point in time since symptom onset. This variation was not explained by age, sex, or severity of illness, and these variables were not associated with the modelled early or late phases of immune-mediated control of viral load. We explored the application of the mechanistic model to identify measured immune responses associated with the control of the viral load. Neutralising antibodies correlated strongly with modelled immune-mediated control of viral load amongst subjects who produced neutralising antibodies. Our models can be used to identify host and viral factors which control URT viral load dynamics, informing future treatment and transmission blocking interventions.

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Background Most studies of immunity to SARS-CoV-2 focus on circulating antibody, giving limited insights into mucosal defences that prevent viral replication and onward transmission. We studied nasal and plasma antibody responses one year after hospitalisation for COVID-19, including a period when SARS-CoV-2 vaccination was introduced. Methods Plasma and nasosorption samples were prospectively collected from 446 adults hospitalised for COVID-19 between February 2020 and March 2021 via the ISARIC4C and PHOSP-COVID consortia. IgA and IgG responses to NP and S of ancestral SARS-CoV-2, Delta and Omicron (BA.1) variants were measured by electrochemiluminescence and compared with plasma neutralisation data. Findings Strong and consistent nasal anti-NP and anti-S IgA responses were demonstrated, which remained elevated for nine months. Nasal and plasma anti-S IgG remained elevated for at least 12 months with high plasma neutralising titres against all variants. Of 180 with complete data, 160 were vaccinated between 6 and 12 months; coinciding with rises in nasal and plasma IgA and IgG anti-S titres for all SARS-CoV-2 variants, although the change in nasal IgA was minimal. Samples 12 months after admission showed no association between nasal IgA and plasma IgG responses, indicating that nasal IgA responses are distinct from those in plasma and minimally boosted by vaccination. Interpretation The decline in nasal IgA responses 9 months after infection and minimal impact of subsequent vaccination may explain the lack of long-lasting nasal defence against reinfection and the limited effects of vaccination on transmission. These findings highlight the need to develop vaccines that enhance nasal immunity.

All healthcare systems are increasingly reliant on health information technology to support the delivery of high-quality, efficient and safe care. Data on its effectiveness are however limited. We therefore sought to examine the impact of organisational digital maturity on clinical outcomes in secondary care within the English National Health Service. We conducted a retrospective analysis of routinely collected administrative data for 13,105,996 admissions across 136 hospitals in England from 2015 to 2016. Data from the 2016 NHS Clinical Digital Maturity Index were used to characterise organisational digital maturity. A multivariable regression model including 12 institutional covariates was utilised to examine the relationship between one measure of organisational digital maturity and five key clinical outcome measures. There was no significant relationship between organisational digital maturity and risk-adjusted 30-day mortality, 28-day readmission rates or complications of care. In multivariable analysis risk-adjusted long length of stay and harm-free care were significantly related to aspects of organisational digital maturity; digitally mature hospitals may not only deliver more harm-free care episodes but also may have a significantly increased risk of patients experiencing a long length of stay. Organisational digital maturity is to some extent related to selected clinical outcomes in secondary care in England. Digital maturity is, however, also strongly linked to other institutional factors that likely play a greater role in influencing clinical outcomes. There is a need to better understand how health IT impacts care delivery and supports other drivers of hospital quality.