Outcome of COVID-19 in hospitalised immunocompromised patients: an analysis of the WHO ISARIC CCP-UK prospective cohort study. (preprint)

Background Immunocompromised patients may be at higher risk of mortality if hospitalised with COVID-19 compared with immunocompetent patients. However, previous studies have been contradictory. We aimed to determine whether immunocompromised patients were at greater risk of in-hospital death, and how this risk changed over the pandemic. Methods We included patients >=19yrs with symptomatic community-acquired COVID-19 recruited to the ISARIC WHO Clinical Characterisation Protocol UK. We defined immunocompromise as: immunosuppressant medication preadmission, cancer treatment, organ transplant, HIV, or congenital immunodeficiency. We used logistic regression to compare the risk of death in both groups, adjusting for age, sex, deprivation, ethnicity, vaccination and co-morbidities. We used Bayesian logistic regression to explore mortality over time. Findings Between 17/01/2020 and 28/02/2022 we recruited 156,552 eligible patients, of whom 21,954 (14%) were immunocompromised. 29% (n=6,499) of immunocompromised and 21% (n=28,608) of immunocompetent patients died in hospital. The odds of inhospital mortality were elevated for immunocompromised patients (adjOR 1.44, 95% CI 1.39-1.50, p<0.001). As the pandemic progressed, in-hospital mortality reduced more slowly for immunocompromised patients than for immunocompetent patients. This was particularly evident with increasing age: the probability of the reduction in hospital mortality being less for immunocompromised patients aged 50-69yrs was 88% for men and 83% for women, and for those >80yrs was 99% for men, and 98% for women. Conclusions Immunocompromised patients remain at elevated risk of death from COVID-19. Targeted measures such as additional vaccine doses and monoclonal antibodies should be considered for this group.

Systematic review and meta-analysis identifies potential host therapeutic targets in COVID-19. (preprint)

An increasing body of literature describes the role of host factors in COVID-19 pathogenesis. There is a need to combine diverse, multi-omic data in order to evaluate and substantiate the most robust evidence and inform development of future therapies. We conducted a systematic review of experiments identifying host factors involved in human betacoronavirus infection (SARS-CoV-2, SARS-CoV, MERS-CoV, seasonal coronaviruses). Gene lists from these diverse sources were integrated using Meta-Analysis by Information Content (MAIC). This previously described algorithm uses data-driven gene list weightings to produce a comprehensive ranked list of implicated host genes. 5,418 genes implicated in human betacoronavirus infection were identified from 32 datasets. The top ranked gene was *PPIA*, encoding cyclophilin A. Pharmacological inhibition with cyclosporine in vitro exerts antiviral activity against several coronaviruses including SARS-CoV. Other highly-ranked genes included proposed prognostic factors (*CXCL10*, *CD4*, *CD3E*) and investigational therapeutic targets (*IL1A*) for COVID-19, but also previously overlooked genes with potential as therapeutic targets. Gene rankings also inform the interpretation of COVID-19 GWAS results, implicating *FYCO1* over other nearby genes in a disease-associated locus on chromosome 3. Pathways enriched in gene rankings included T-cell receptor signalling, protein processing, and viral infections. We identified limited overlap of our gene list with host genes implicated in ARDS (innate immune and inflammation genes) and Influenza A virus infection (RNA-binding and ribosome-associated genes). We will continue to update this dynamic ranked list of host genes as the field develops, as a resource to inform and prioritise future studies. Updated results are available at https://baillielab.net/maic/covid19.

Robust, reproducible clinical patterns in hospitalised patients with COVID-19 (preprint)

Severe COVID-19 is characterised by fever, cough, and dyspnoea. Symptoms affecting other organ systems have been reported. The clinical associations of different patterns of symptoms can influence diagnostic and therapeutic decision-making: for example, significant differential therapeutic effects in sub-groups of patients with different severities of respiratory failure have already been reported for the only treatment so far shown to reduce mortality in COVID-19, dexamethasone. We obtained structured clinical data on 68914 patients in the UK (the ISARIC Coronavirus Clinical Characterisation Consortium, 4C) and used a principled, unsupervised clustering approach to partition the first 33468 cases according to symptoms reported at recruitment. We validated our findings in a second group of 35446 cases recruited to ISARIC-4C, and in separate cohort of community cases. A core symptom set of fever, cough, and dyspnoea co-occurred with additional symptoms in three patterns: fatigue and confusion, diarrhoea and vomiting, or productive cough. Presentations with a single reported symptom of dyspnoea or confusion were common, and a subgroup of patients reported few or no symptoms. Patients presenting with gastrointestinal symptoms were more commonly female, had a longer duration of symptoms before presentation, and had lower 30-day mortality. Patients presenting with confusion, with or without core symptoms, were older and had a higher unadjusted mortality. Symptom clusters were highly consistent in replication analysis using a further 35446 individuals subsequently recruited to ISARIC-4C. Similar patterns were externally verified in 4445 patients from a study of self-reported symptoms of mild disease. The large scale of ISARIC-4C study enabled robust, granular discovery and replication of patient clusters. Clinical interpretation is necessary to determine which of these observations have practical utility. We propose that four patterns are usefully distinct from the core symptom groups: gastro-intestinal disease, productive cough, confusion, and pauci-symptomatic presentations. Importantly, each is associated with an in-hospital mortality which differs from that of patients with core symptoms. These observations deepen our understanding of COVID-19 and will influence clinical diagnosis, risk prediction, and future mechanistic and clinical studies.