There is broad interest in improved methods to generate robust evidence regarding best practice, especially in settings where patient conditions are heterogenous and require multiple concomitant therapies. Here, we present the rationale and design of a large, international trial that combines features of adaptive platform trials with pragmatic point-of-care trials to determine best treatment strategies for patients admitted to an intensive care unit with severe community-acquired pneumonia. The trial uses a novel design, entitled “a randomized embedded multifactorial adaptive platform.” The design has five key features: 1) randomization, allowing robust causal inference; 2) embedding of study procedures into routine care processes, facilitating enrollment, trial efficiency, and generalizability; 3) a multifactorial statistical model comparing multiple interventions across multiple patient subgroups; 4) response-adaptive randomization with preferential assignment to those interventions that appear most favorable; and 5) a platform structured to permit continuous, potentially perpetual enrollment beyond the evaluation of the initial treatments. The trial randomizes patients to multiple interventions within four treatment domains: antibiotics, antiviral therapy for influenza, host immunomodulation with extended macrolide therapy, and alternative corticosteroid regimens, representing 240 treatment regimens. The trial generates estimates of superiority, inferiority, and equivalence between regimens on the primary outcome of 90-day mortality, stratified by presence or absence of concomitant shock and proven or suspected influenza infection. The trial will also compare ventilatory and oxygenation strategies, and has capacity to address additional questions rapidly during pandemic respiratory infections. As of January 2020, REMAP-CAP (Randomized Embedded Multifactorial Adaptive Platform for Community-acquired Pneumonia) was approved and enrolling patients in 52 intensive care units in 13 countries on 3 continents. In February, it transitioned into pandemic mode with several design adaptations for coronavirus disease 2019. Lessons learned from the design and conduct of this trial should aid in dissemination of similar platform initiatives in other disease areas. Clinical trial registered with www.clinicaltrials.gov (NCT02735707).

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care¹ or hospitalization^2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease.

Introduction: Higher lactate concentrations within the normal reference range (relative hyperlactatemia) are not considered clinically significant. We tested the hypothesis that relative hyperlactatemia is independently associated with an increased risk of hospital death. Methods: This observational study examined a prospectively obtained intensive care database of 7,155 consecutive critically ill patients admitted to the Intensive Care Units (ICUs) of four Australian university hospitals. We assessed the relationship between ICU admission lactate, maximal lactate and time-weighted lactate levels and hospital outcome in all patients and also in those patients whose lactate concentrations (admission n = 3,964, maximal n = 2,511, and time-weighted n = 4,584) were under 2 mmol.L^-1 (i.e. relative hyperlactatemia). Results: We obtained 172,723 lactate measurements. Higher admission and time-weightedlactate concentration within the reference range was independently associated with increased hospital mortality (admission odds ratio (OR) 2.1, 95% confidence interval (CI) 1.3 to 3.5, P = 0.01; time-weighted OR 3.7, 95% CI 1.9 to 7.00, P < 0.0001). This significant association was first detectable at lactate concentrations > 0.75 mmol.L^-1. Furthermore, in patients whose lactate ever exceeded 2 mmol.L^-1, higher time-weighted lactate remained strongly associated with higher hospital mortality (OR 4.8, 95% CI 1.8 to 12.4, P < 0.001). Conclusions: In critically ill patients, relative hyperlactataemia is independently associated with increased hospital mortality. Blood lactate concentrations > 0.75 mmol.L^-1 can be used by clinicians to identify patients at higher risk of death. The current reference range for lactate in the critically ill may need to be re-assessed.

Severe traumatic brain injury is a leading cause of neurologic disability, and approximately 50% of patients have long-term outcomes of death or severe disability.^1-3 The economic and social costs of severe traumatic brain injury are high.⁴

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown¹ to be highly efficient for discovery of genetic associations². Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group³. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).