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.

Whole genome sequencing identifies multiple loci for critical illness caused by COVID-19 (preprint)

Critical illness in COVID-19 is caused by inflammatory lung injury, mediated by the host immune system. We and others have shown that host genetic variation influences the development of illness requiring critical care1 or hospitalisation2;3;4 following SARS-Co-V2 infection. The GenOMICC (Genetics of Mortality in Critical Care) study is designed to compare genetic variants in critically-ill cases with population controls in order to find underlying disease mechanisms. Here, we use whole genome sequencing and statistical fine mapping in 7,491 critically-ill cases compared with 48,400 population controls to discover and replicate 22 independent variants that significantly predispose to life-threatening COVID-19. We identify 15 new independent associations with critical COVID-19, including variants within genes involved in interferon signalling (IL10RB, PLSCR1), leucocyte differentiation (BCL11A), and blood type antigen secretor status (FUT2). Using transcriptome-wide association and colocalisation to infer the effect of gene expression on disease severity, we find evidence implicating expression of multiple genes, including reduced expression of a membrane flippase (ATP11A), and increased mucin expression (MUC1), in critical disease. We show that comparison between critically-ill cases and population controls is highly efficient for genetic association analysis and enables detection of therapeutically-relevant mechanisms of disease. Therapeutic predictions arising from these findings require testing in clinical trials.