Evaluation of S/F94 as a proxy for COVID-19 severity (preprint)

Optimising statistical power in early-stage trials and observational studies accelerates discovery and improves the reliability of results. Ideally, intermediate outcomes should be continuously distributed and lie on the causal pathway between an intervention and a definitive outcome such as mortality. In order to optimise power for an intermediate outcome in the RECOVERY trial, we devised and evaluated a modification to a simple, pragmatic measure of oxygenation function - the SaO2/FIO2 (S/F) ratio. We demonstrate that, because of the ceiling effect in oxyhaemoglobin saturation, S/F ceases to reflect pulmonary oxygenation function at high values of SaO2. Using synthetic and real data, we found that the correlation of S/F with a gold standard (PaO2/FIO2, P/F ratio) improved substantially when measurements with SaO2 > 0.94 are excluded(Spearman r, synthetic data: S/F: 0.31; S/F94: 0.85). We refer to this measure as S/F94. In order to test the underlying assumptions and validity of S/F94 as a predictor of a definitive outcome (mortality), we collected an observational dataset including over 39,000 hospitalised patients with COVID-19 in the ISARIC4C study. We first demonstrated that S/F94 is predictive of mortality in COVID-19. We then compared the sample sizes required for trials using different outcome measures (S/F94, the WHO ordinal scale, sustained improvement at day 28 and mortality at day 28) ensuring comparable effect sizes. The smallest sample size was needed when S/F94 on day 5 was used as an outcome measure. To facilitate future study design, we provide an online user interface to quantify realworld power for a range of outcomes and inclusion criteria, using a synthetic dataset retaining the population-level clinical associations in real data accrued in ISARIC4C https://isaric4c.net/endpoints. We demonstrated that S/F94 is superior to S/F as a measure of pulmonary oxygenation function and is an effective intermediate outcome measure in COVID-19. It is a simple and non-invasive measurement, representative of disease severity and provides greater statistical power to detect treatment differences than other intermediate endpoints.

SARS-CoV-2 environmental contamination from hospitalised COVID-19 patients receiving aerosol generating procedures (preprint)

ABSTRACT Background. Continuous positive airways pressure (CPAP) and high-flow nasal oxygen (HFNO) are considered "aerosol-generating procedures" (AGPs) in the treatment of COVID-19. We aimed to measure air and surface environmental contamination of SARS-CoV-2 virus when CPAP and HFNO were used, compared with supplemental oxygen, to investigate the potential risks of viral transmission to healthcare workers and patients. Methods. 30 hospitalised patients with COVID-19 requiring supplemental oxygen, with a fraction of inspired oxygen [≥]0.4 to maintain oxygen saturations [≥]94%, were prospectively enrolled into an observational environmental sampling study. Participants received either supplemental oxygen, CPAP or HFNO (n=10 in each group). A nasopharyngeal swab, three air and three surface samples were collected from each participant and the clinical environment. RT qPCR analyses were performed for viral and human RNA, and positive/suspected-positive samples were cultured for the presence of biologically viable virus. Results. Overall 21/30 (70%) of participants tested positive for SARS-CoV-2 RNA in the nasopharynx. In contrast, only 4/90 (4%) and 6/90 (7%) of all air and surface samples tested positive (positive for E and ORF1a) for viral RNA respectively, although there were an additional 10 suspected-positive samples in both air and surfaces samples (positive for E or ORF1a). CPAP/HFNO use or coughing was not associated with significantly more environmental contamination. Only one nasopharyngeal sample was culture positive. Conclusions. The use of CPAP and HFNO to treat moderate/severe COVID-19 was not associated with significantly higher levels of air or surface viral contamination in the immediate care environment.

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.

The COVID-19 Critical Care Consortium observational study: Design and rationale of a prospective, international, multicenter, observational study (preprint)

ImportanceThere is a paucity of data that can be used to guide the management of critically ill patients with coronavirus disease 2019 (COVID-19). Global collaboration offers the best chance of obtaining these data, at scale and in time. In the absence of effective therapies, insights derived from real-time observational data will be a crucial means of improving outcomes. ObjectiveIn response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, a research and data-sharing collaborative has been assembled to harness the cumulative experience of intensive care units (ICUs) worldwide. The resulting observational study provides a platform to rapidly disseminate detailed data and insights. DesignThe COVID-19 Critical Care Consortium observational study is an international, multicenter, prospective, observational study of patients with confirmed or suspected SARSCoV-2 infection admitted to ICUs. SettingThis is an evolving, open-ended study that commenced on January 1st, 2020 and currently includes more than 350 sites in over 48 countries. The study enrolls patients at the time of ICU admission and follows them to the time of death, hospital discharge, or 28 days post-ICU admission, whichever occurs last. ParticipantsAll subjects, without age limit, requiring admission to an ICU for SARS-CoV-2 infection, confirmed by real-time polymerase chain reaction (PCR) and/or next-generation sequencing or with high clinical suspicion of the infection. Patients admitted to an ICU for any other reason are excluded. Main outcomes and measuresKey data, collected via an electronic case report form devised in collaboration with the ISARIC/SPRINT-SARI networks, include: patient demographic data and risk factors, clinical features, severity of illness and respiratory failure, need for non-invasive and/or mechanical ventilation and/or extracorporeal membrane COVID-19 CCC observational study protocol oxygenation (ECMO), and associated complications, as well as data on adjunctive therapies. Final outcomes of in-hospital death, discharge or continuing admissions at 28 days. DiscussionThis large-scale, observational study of COVID-19 in the critically ill will provide rapid international characterization. Open-ended accrual will increase the power to answer hypothesis-led questions over time. Several sub-studies have already been initiated, examining hemostasis, neurological, cardiac, and long-term outcomes.