Higher dose corticosteroids in hospitalised COVID-19 patients with hypoxia but not requiring ventilatory support (RECOVERY): a randomised, controlled, open-label, platform trial (preprint)

Background: Low-dose corticosteroids have been shown to reduce mortality for hypoxic COVID-19 patients requiring oxygen or ventilatory support (non-invasive mechanical ventilation, invasive mechanical ventilation or extra-corporeal membrane oxygenation). We evaluated the use of a higher dose of corticosteroids in this patient group. Methods: This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]) is assessing multiple possible treatments in patients hospitalised for COVID-19. Eligible and consenting adult patients with clinical evidence of hypoxia (i.e. receiving oxygen or with oxygen saturation <92% on room air) were randomly allocated (1:1) to either usual care with higher dose corticosteroids (dexamethasone 20 mg once daily for 5 days followed by 10 mg once daily for 5 days or until discharge if sooner) or usual standard of care alone (which includes dexamethasone 6 mg once daily for 10 days or until discharge if sooner). The primary outcome was 28-day mortality. On 11 May 2022, the independent Data Monitoring Committee recommended stopping recruitment of patients receiving no oxygen or simple oxygen only to this comparison due to safety concerns. We report the results for these participants only. Recruitment of patients receiving ventilatory support continues. The RECOVERY trial is registered with ISRCTN (50189673) and clinicaltrials.gov ( NCT04381936 ). Findings: Between 25 May 2021 and 12 May 2022, 1272 COVID-19 patients with hypoxia and receiving no oxygen (1%) or simple oxygen only (99%) were randomly allocated to receive usual care plus higher dose corticosteroids versus usual care alone (of whom 87% received low dose corticosteroids during the follow-up period). Of those randomised, 745 (59%) were in Asia, 512 (40%) in the UK and 15 (1%) in Africa. 248 (19%) had diabetes mellitus. Overall, 121 (18%) of 659 patients allocated to higher dose corticosteroids versus 75 (12%) of 613 patients allocated to usual care died within 28 days (rate ratio [RR] 1.56; 95% CI 1.18-2.06; p=0.0020). There was also an excess of pneumonia reported to be due to non-COVID infection (10% vs. 6%; absolute difference 3.7%; 95% CI 0.7-6.6) and an increase in hyperglycaemia requiring increased insulin dose (22% vs. 14%; absolute difference 7.4%; 95% CI 3.2-11.5). Interpretation: In patients hospitalised for COVID-19 with clinical hypoxia but requiring either no oxygen or simple oxygen only, higher dose corticosteroids significantly increased the risk of death compared to usual care, which included low dose corticosteroids. The RECOVERY trial continues to assess the effects of higher dose corticosteroids in patients hospitalised with COVID-19 who require non-invasive ventilation, invasive mechanical ventilation or extra-corporeal membrane oxygenation.

RapiD_AI: A framework for Rapidly Deployable AI for novel disease & pandemic preparedness (preprint)

COVID-19 is unlikely to be the last pandemic that we face. According to an analysis of a global dataset of historical pandemics from 1600 to the present, the risk of a COVID-like pandemic has been estimated as 2.63% annually or a 38% lifetime probability. This rate may double over the coming decades. While we may be unable to prevent future pandemics, we can reduce their impact by investing in preparedness. In this study, we propose RapiD_AI: a framework to guide the use of pretrained neural network models as a pandemic preparedness tool to enable healthcare system resilience and effective use of ML during future pandemics. The RapiD_AI framework allows us to build high-performing ML models using data collected in the first weeks of the pandemic and provides an approach to adapt the models to the local populations and healthcare needs. The motivation is to enable healthcare systems to overcome data limitations that prevent the development of effective ML in the context of novel diseases. We digitally recreated the first 20 weeks of the COVID-19 pandemic and experimentally demonstrated the RapiD_AI framework using domain adaptation and inductive transfer. We (i) pretrain two neural network models (Deep Neural Network and TabNet) on a large Electronic Health Records dataset representative of a general in-patient population in Oxford, UK, (ii) fine-tune using data from the first weeks of the pandemic, and (iii) simulate local deployment by testing the performance of the models on a held-out test dataset of COVID-19 patients. Our approach has demonstrated an average relative/absolute gain of 4.92/4.21% AUC compared to an XGBoost benchmark model trained on COVID-19 data only. Moreover, we show our ability to identify the most useful historical pretraining samples through clustering and to expand the task of deployed models through inductive transfer to meet the emerging needs of a healthcare system without access to large historical pretraining datasets.

RapiD_AI: A framework for Rapidly Deployable AI for novel disease & pandemic preparedness (preprint)

COVID-19 is unlikely to be the last pandemic that we face. According to an analysis of a global dataset of historical pandemics from 1600 to the present, the risk of a COVID-like pandemic has been estimated as 2.63% annually or a 38% lifetime probability. This rate may double over the coming decades. While we may be unable to prevent future pandemics, we can reduce their impact by investing in preparedness. In this study, we propose RapiD AI : a framework to guide the use of pretrained neural network models as a pandemic preparedness tool to enable healthcare system resilience and effective use of ML during future pandemics. The RapiD AI framework allows us to build highperforming ML models using data collected in the first weeks of the pandemic and provides an approach to adapt the models to the local populations and healthcare needs. The motivation is to enable healthcare systems to overcome data limitations that prevent the development of effective ML in the context of novel diseases. We digitally recreated the first 20 weeks of the COVID-19 pandemic and experimentally demonstrated the RapiD AI framework using domain adaptation and inductive transfer. We (i) pretrain two neural network models (Deep Neural Network and TabNet) on a large Electronic Health Records dataset representative of a general in-patient population in Oxford, UK, (ii) fine-tune using data from the first weeks of the pandemic, and (iii) simulate local deployment by testing the performance of the models on a held-out test dataset of COVID-19 patients. Our approach has demonstrated an average relative/absolute gain of 4.92/4.21% AUC compared to an XGBoost benchmark model trained on COVID-19 data only. Moreover, we show our ability to identify the most useful historical pretraining samples through clustering and to expand the task of deployed models through inductive transfer to meet the emerging needs of a healthcare system without access to large historical pretraining datasets.

The burden and dynamics of hospital-acquired SARS-CoV-2 in England (preprint)

Hospital-based transmission played a dominant role in MERS-CoV and SARS-CoV epidemics but large-scale studies of its role in the SARS-CoV-2 pandemic are lacking. Such transmission risks spreading the virus to the most vulnerable individuals and can have wider-scale impacts through hospital-community interactions. Using data from acute hospitals in England we quantify within-hospital transmission, evaluate likely pathways of spread and factors associated with heightened transmission risk, and explore the wider dynamical consequences. We show that hospital transmission is likely to have been a major contributor to the burden of COVID-19 in England. We estimate that between June 2020 and March 2021 between 95,000 and 167,000 patients acquired SARS-CoV-2 in hospitals with nosocomially-infected patients likely to have been the main sources of transmission to other patients. Increased transmission to patients was associated with hospitals having fewer single rooms and lower heated volume per bed. Moreover, we show that reducing hospital transmission could substantially enhance the efficiency of punctuated lockdown measures in suppressing community transmission. These findings reveal the previously unrecognised scale of hospital transmission, have direct implications for targeting of hospital control measures, and highlight the need to design hospitals better-equipped to limit the transmission of future high consequence pathogens.

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.

Analysis of severe outcomes associated with the SARS-CoV-2 Variant of Concern 202012/01 in England using ICNARC Case Mix Programme and QResearch databases (preprint)

ABSTRACT Background A new, more transmissible variant of SARS-CoV-2, variant of concern (VOC) 202012/01 or lineage B.1.1.7, has emerged in the UK. We estimate the risk of critical care admission, mortality in critical ill patients, and overall mortality associated with VOC B.1.1.7 compared with the original variant. We also compare clinical outcomes between these variants ‘ groups. Methods We linked a large primary care (QResearch), the national critical care (ICNARC CMP) and the COVID-19 testing (PHE) database and extracted two cohorts. The first was used to explore the association between VOC B.1.1.7 and critical care admission and 28-day mortality. The second to determine the risk of mortality in critically ill patients with VOC B.1.1.7 compared to those without. We used Royston-Parmar models adjusted for age, sex, region, other socio-demographics and comorbidities (asthma, COPD, type I and II, hypertension). We reported information on types and duration of organ supports for the two variants ‘ groups. Findings The first cohort included 198,420 patients. Of these, 80,494 had VOC B.1.1.7, 712 were critically ill and 630 died by 28 days. The second cohort included 3432 critically ill patients. Of these, 2019 had VOC B.1.1.7 and 822 died at the end of critical care. Using the first cohort, we estimated adjusted hazard ratios for critical care admission and mortality to be 1.99 (95% CI: 1.59, 2.49) and 1.59 (95% CI: 1.25-2.03) for VOC B.1.1.7 compared with the original variant group, respectively. The adjusted hazard ratio for mortality in critical care, estimated using the second cohort, was 0.93 (95% CI 0.76-1.15) for patients with VOC B.1.1.7, compared to those without. Interpretation VOC B.1.1.7 appears to be more severe. Patients with VOC B.1.1.7 are at increased risk of critical care admission and mortality compared with patients without. For patients receiving critical care, mortality appears independent of virus strain. RESEARCH IN CONTEXT Evidence before this study A new variant of the SARS-CoV-2 virus, variant of concern (VOC) 202012/01, or lineage B.1.1.7, was detected in England in September 2020. The characteristics and outcomes of patients infected with VOC B.1.1.7 are not yet known. VOC B.1.1.7 has been associated with increased transmissibility. Early analyses have suggested infection with VOC B.1.1.7 may be associated with a higher risk of mortality compared with infection with other virus variants, but these analyses had either limited ability to adjust for key confounding variables or did not consider critical care admission. The effects of VOC B.1.1.7 on severe COVID-19 outcomes remain unclear. Added value of this study This study found a 60% higher risk of 28-day mortality associated with infection with VOC B.1.1.7 in patients tested in the community in comparison with the original variant, when adjusted for key confounding variables. The risk of critical care admission for those with VOC B.1.1.7 is double the risk associated with the original variant. For patients receiving critical care, the infecting variant is not associated with the risk of mortality at the end of critical care. Implications of all the available evidence The higher mortality and rate of critical care admission associated with VOC B.1.1.7, combined with its known increased transmissibility, are likely to put health care systems under further stress. These effects may be mitigated by the ongoing vaccination programme.

Risk factors for long-term consequences of COVID-19 in hospitalised adults in Moscow using the ISARIC Global follow-up protocol: StopCOVID cohort study (preprint)

BackgroundThe long-term sequalae of COVID-19 remain poorly characterised. In this study, we aimed to assess long-standing symptoms (LS) (symptoms lasting from the time of discharge) in previously hospitalised patients with COVID-19 and assess associated risk factors. MethodsThis is a longitudinal cohort study of adults ([≥]18 years of age) with clinically diagnosed or laboratory-confirmed COVID-19 admitted to Sechenov University Hospital Network in Moscow, Russia. Data were collected from patients discharged between April 8 and July 10, 2020. Participants were interviewed via telephone using Tier 1 ISARIC Long-term Follow-up Study CRF and the WHO CRF for Post COVID conditions. Reported symptoms were further categorised based on the system(s) involved. Additional information on dyspnoea, quality of life and fatigue was collected using validated instruments. Multivariable logistic regressions were performed to investigate risk factors for development of LS categories. FindingsOverall, 2,649 of 4,755 patients discharged from the hospitals were available for the follow-up and included in the study. The median age of the patients was 56 years (IQR, 46-66) and 1,353 (51.1%) were women. The median follow-up time since hospital discharge was 217.5 (200.4-235.5) days. At the time of the follow-up interview 1247 (47.1%) participants reported LS. Fatigue (21.2%, 551/2599), shortness of breath (14.5%, 378/2614) and forgetfulness (9.1%, 237/2597) were the most common LS reported. Chronic fatigue (25%, 658/2593) and respiratory (17.2% 451/2616) were the most common LS categories. with reporting of multi-system involvement (MSI) less common (11.3%; 299). Female sex was associated with LS categories of chronic fatigue with an odds ratio of 1.67 (95% confidence interval 1.39 to 2.02), neurological (2.03, 1.60 to 2.58), mood and behaviour (1.83, 1.41 to 2.40), dermatological (3.26, 2.36 to 4.57), gastrointestinal (2.50, 1.64 to 3.89), sensory (1.73, 2.06 to 2.89) and respiratory (1.31, 1.06 to 1.62). Pre-existing asthma was associated with neurological (1.95, 1.25 to 2.98) and mood and behavioural changes (2.02, 1.24 to 3.18) and chronic pulmonary disease was associated with chronic fatigue (1.68, 1.21 to 2.32). Interpretation6 to 8 months after acute infection episode almost a half of patients experience symptoms lasting since hospital discharge. One in ten individuals experiences MSI. Female sex is the main risk factor for majority of the LS categories. chronic pulmonary disease is associated with a higher risk of chronic fatigue development, and asthma with neurological and mood and behaviour changes. Individuals with LS and MSI should be the main target for future research and intervention strategies. FundingThis study is supported by Russian Fund for Basic Research and UK Embassy in Moscow. The ISARIC work is supported by grants from: the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford [award 200907], Wellcome Trust and Department for International Development [215091/Z/18/Z], and the Bill and Melinda Gates Foundation [OPP1209135], EU Platform for European Preparedness Against (Re-) emerging Epidemics (PREPARE) [FP7 project 602525] This research was funded in part, by the Wellcome Trust. The views expressed are those of the authors and not necessarily those of the DID, NIHR, Wellcome Trust or PHE. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSEvidence suggests that COVID-19 may result in short- and long-term consequences to health. Most studies do not provide definitive answers due to a combination of short follow-up (2-3 months), small sample size, and use of non-standardised tools. There is a need to study the longer-term health consequences of previously hospitalised patients with COVID-19 infection and to identify risk factors for sequalae. Added value of this studyTo our knowledge, this is the largest cohort study (n=2,649) with the longest follow-up since hospital discharge (6-8 months) of previously hospitalised adult patients. We found that 6-8 months after discharge from the hospital, around a half (47.1%) of patients reported at least one long-standing symptom since discharge. Once categories of symptoms were assessed, chronic fatigue and respiratory problems were the most frequent clusters of long-standing symptoms in our patients. Of those patients having long-term symptoms, a smaller proportion (11.3%) had multisystem involvement, with three or more categories of long-standing symptoms present. Although most patients developed symptoms since discharge, a smaller number of individuals experienced symptom beginning symptom appearing weeks or months after the acute phase. Female sex was a predictor for most of the symptom categories at the time of the follow-up interview, with chronic pulmonary disease associated with chronic fatigue-related symptoms, and asthma with a higher risk of neurological symptoms, mood and behaviour problems. Implications of all the available evidenceThe majority of patients experienced long-lasting symptoms 6 to 8 months after hospital discharge and almost half reported at least one long-standing symptom, with chronic fatigue and respiratory problems being the most frequent. A smaller number reported multisystem impacts with three or more long-standing categories present at follow-up. A higher risk was found for women, for chronic pulmonary disease with chronic fatigue, and neurological symptoms and mood and behaviour problems with asthma. Patterns of the symptom development following COVID-19 should be further investigated in future research.

From SARS and MERS to COVID-19: a review of the quality and responsiveness of clinical management guidelines in outbreak settings (preprint)

ObjectiveTo assess the responsiveness and quality of clinical management guidelines (CMGs) in SARS, MERS and COVID-19 and determine whether this has improved over time. DesignRapid literature review, quality assessment and focus group consultation. Data Sources- Google and Google Scholar were systematically searched from inception to 6th June 2020.This was supplemented with hand searches of national and international public health agency and infectious disease society websites as well as directly approaching clinical networks in regions where few CMGs had been identified via the primary search. Eligibility CriteriaCMGs for the treatment of COVID-19/SARS/MERS providing recommendations on supportive care and/or specific treatment. MethodsData extraction was performed using a standardised form. The Appraisal of Guidelines for Research and Evaluation (AGREE-II) tool was used to evaluate the quality of the CMGs. Six COVID-19 treatments were selected to assess the responsiveness of a subset of guidelines and their updates to 20th November 2020. We ran two sessions of focus groups with patient advocates to elicit their views on guideline development. ResultsWe included 37 COVID-19, six SARS, and four MERS CMGs. Evidence appraisals in CMGs generally focused on novel drugs rather than basic supportive care; where evidence for the latter was provided it was generally of a low quality. Most CMGs had major methodological flaws (only two MERS-CoV and four COVID-19 CMGs were recommended for use by both reviewers without modification) and there was no evidence of improvement in quality over time. CMGs scored lowest in the following AGREE-II domains: scope and purpose, editorial independence, stakeholder engagement, and rigour of development. Of the COVID-19 CMGs, only eight included specific guidance for the management of elderly patients and only ten for high-risk groups; a further eight did not specify the target patient group at all. Early in the pandemic, multiple guidelines recommended unproven treatments and whilst in general findings of major clinical trials were eventually adopted, this was not universally the case. Eight guidelines recommended that use of unproven agents should be considered on a case-by-case basis. Patient representatives expressed concern about the lack of engagement with them in CMG development and that these documents are not accessible to non-experts. ConclusionThe quality of most CMGs produced in coronaviridae outbreaks is poor and we have found no evidence of improvement over time, highlighting that current development frameworks must be improved. There is an need to strengthen the evidence base surrounding basic supportive care and develop methods to engage patients in CMG development from the beginning in outbreak settings. Systematic review registrationPROSPERO CRD42020167361

Development and validation of the 4C Deterioration model for adults hospitalised with COVID-19 (preprint)

Prognostic models to predict the risk of clinical deterioration in acute COVID-19 are required to inform clinical management decisions. Among 75,016 consecutive adults across England, Scotland and Wales prospectively recruited to the ISARIC Coronavirus Clinical Characterisation Consortium (ISARIC4C) study, we developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) using 11 routinely measured variables. We used internal-external cross-validation to show consistent measures of discrimination, calibration and clinical utility across eight geographical regions. We further validated the final model in held-out data from 8,252 individuals in London, with similarly consistent performance (C-statistic 0.77 (95% CI 0.75 to 0.78); calibration-in-the-large 0.01 (-0.04 to 0.06); calibration slope 0.96 (0.90 to 1.02)). Importantly, this model demonstrated higher net benefit than using other candidate scores to inform decision-making. Our 4C Deterioration model thus demonstrates unprecedented clinical utility and generalisability to predict clinical deterioration among adults hospitalised with COVID-19.

Genetic mechanisms of critical illness in Covid-19 (preprint)

The subset of patients who develop critical illness in Covid-19 have extensive inflammation affecting the lungs and are strikingly different from other patients: immunosuppressive therapy benefits critically-ill patients, but may harm some non-critical cases. Since susceptibility to life-threatening infections and immune-mediated diseases are both strongly heritable traits, we reasoned that host genetic variation may identify mechanistic targets for therapeutic development in Covid-19. GenOMICC (Genetics Of Mortality In Critical Care, genomicc.org) is a global collaborative study to understand the genetic basis of critical illness. Here we report the results of a genome-wide association study (GWAS) in 2790 critically-ill Covid-19 patients from 208 UK intensive care units (ICUs), representing >95% of all ICU beds. Random controls were drawn from three distinct UK population studies. We identify and replicate several novel genome-wide significant associations including variants chr19p13.3 (rs2109069, P = 3.98 x 10-12), within the gene encoding dipeptidyl peptidase 9 (DPP9), and at chr21q22.1 (rs2236757, P = 4.99 x 10-8) in the interferon receptor IFNAR2. Consistent with our focus on extreme disease in younger patients with less comorbidity, we detect a stronger signal at the known 3p21.31 locus than previous studies (rs73064425, P = 1.2 x 10-27). We identify potential targets for repurposing of existing licensed medications. Using Mendelian randomisation we found evidence in support of a causal link from low expression of IFNAR2, and high expression of TYK2, to life-threatening disease. Transcriptome-wide association in lung tissue revealed that high expression of the monocyte/macrophage chemotactic receptor CCR2 is associated with severe Covid-19. We detected genome-wide significant gene-level associations for genes with central roles in viral restriction (OAS1, OAS2, OAS3). These results identify specific loci associated with life-threatening disease, and potential targets for host-directed therapies. Randomised clinical trials will be necessary before any change to clinical practice.

The potential health and economic impact of dexamethasone treatment for patients with COVID-19 (preprint)

Dexamethasone has been shown to reduce mortality in hospitalised COVID-19 patients needing oxygen and ventilation by 18% and 36%, respectively. Here, we estimate the potential number of lives saved and life years gained if this treatment would be rolled out in the UK and globally, as well as its cost-effectiveness of implementing this intervention. We estimate that, for the UK, approximately 12,000 [4,250 - 27,000] lives could be saved by January 2021. Assuming that dexamethasone has a similar effect size in settings where access to oxygen therapies is limited, this would translate into approximately 650,000 [240,000 - 1,400,000] lives saved globally. If dexamethasone acts differently in these settings, the impact could be less than half of this value. To estimate the full potential of dexamethasone in the global fight against COVID-19, it is essential to perform clinical research in settings with limited access to oxygen and/or ventilators, e.g. in low and middle-income countries.

ISARIC COVID-19 Clinical Data Report: 8 June 2020 (preprint)

ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global uptake of this resource has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report is a part of a series and includes the results of data analysis on 8 June 2020. We thank all of the data contributors for their ongoing support. As of 8JUN20, data have been entered for 67,130 patients from 488 sites across 37 countries. For this report, we show data for 42,656 patients with confirmed disease who were enrolled >14 days prior. This update includes about 2,400 new cases from France, and we thank these collaborators for this significant addition to the dataset. Some highlights from this report The median time from onset of symptoms to hospital admission is 5 days, but a proportion of patients take longer to get to the hospital (average 14.6 days, standard deviation 8.1). COVID-19 patients tend to require prolonged hospitalisation; of the 88% with a known outcome, the median length of admission to death or discharge is 8 days and the mean 11.5. 17% of patients were admitted to ICU/HDU, about 40% of these on the very day of hospital admission. Antibiotics were given to 83% of patients, antivirals to 9%, steroids to 15%, which becomes 93%, 50% and 27%, respectively for those admitted to ICU/HDU. Attention has been called on overuse of antibiotics and need to adhere to antibiotic stewardship principles. 67% of patients received some degree of oxygen supplementation: of these 23.4% received NIV and 15% IMV. This relatively high proportion of oxygen use will have implications for oxygen surge planning in healthcare facilities. Some centres may need to plan to boost capacity to deliver oxygen therapy if this is not readily available. WHO provides operational advice on surge strategy here https://apps.who.int/iris/bitstream/handle/10665/331746/WHO-2019-nCoV-Oxygen_sources-2020.1-eng.pdf

Effect of Hydroxychloroquine in Hospitalized Patients with COVID-19: Preliminary results from a multi-centre, randomized, controlled trial. (preprint)

Background: Hydroxychloroquine and chloroquine have been proposed as treatments for coronavirus disease 2019 (COVID-19) on the basis of in vitro activity, uncontrolled data, and small randomized studies. Methods: The Randomised Evaluation of COVID-19 therapy (RECOVERY) trial is a randomized, controlled, open-label, platform trial comparing a range of possible treatments with usual care in patients hospitalized with COVID-19. We report the preliminary results for the comparison of hydroxychloroquine vs. usual care alone. The primary outcome was 28-day mortality. Results: 1561 patients randomly allocated to receive hydroxychloroquine were compared with 3155 patients concurrently allocated to usual care. Overall, 418 (26.8%) patients allocated hydroxychloroquine and 788 (25.0%) patients allocated usual care died within 28 days (rate ratio 1.09; 95% confidence interval [CI] 0.96 to 1.23; P=0.18). Consistent results were seen in all pre-specified subgroups of patients. Patients allocated to hydroxychloroquine were less likely to be discharged from hospital alive within 28 days (60.3% vs. 62.8%; rate ratio 0.92; 95% CI 0.85-0.99) and those not on invasive mechanical ventilation at baseline were more likely to reach the composite endpoint of invasive mechanical ventilation or death (29.8% vs. 26.5%; risk ratio 1.12; 95% CI 1.01-1.25). There was no excess of new major cardiac arrhythmia. Conclusions: In patients hospitalized with COVID-19, hydroxychloroquine was not associated with reductions in 28-day mortality but was associated with an increased length of hospital stay and increased risk of progressing to invasive mechanical ventilation or death.

Protocol for the development and evaluation of a tool for predicting risk of short-term adverse outcomes due to COVID-19 in the general UK population (preprint)

Introduction: Novel coronavirus 2019 (COVID-19) has propagated a global pandemic with significant health, economic and social costs. Emerging emergence has suggested that several factors may be associated with increased risk from severe outcomes or death from COVID-19. Clinical risk prediction tools have significant potential to generate individualised assessment of risk and may be useful for population stratification and other use cases. Methods and analysis: We will use a prospective open cohort study of routinely collected data from 1205 general practices in England in the QResearch database. The primary outcome is COVID-19 mortality (in or out-of-hospital) defined as confirmed or suspected COVID-19 mentioned on the death certificate, or death occurring in a person with SARS-CoV-2 infection between 24th January and 30th April 2020. Our primary outcome in adults is COVID-19 mortality (including out of hospital and in hospital deaths). We will also examine COVID-19 hospitalisation in children. Time-to-event models will be developed in the training data to derive separate risk equations in adults (19-100 years) for males and females for evaluation of risk of each outcome within the 3-month follow-up period (24th January to 30th April 2020), accounting for competing risks. Predictors considered will include age, sex, ethnicity, deprivation, smoking status, alcohol intake, body mass index, pre-existing medical co-morbidities, and concurrent medication. Measures of performance (prediction errors, calibration and discrimination) will be determined in the test data for men and women separately and by ten-year age group. For children, descriptive statistics will be undertaken if there are currently too few serious events to allow development of a risk model. The final model will be externally evaluated in (a) geographically separate practices and (b) other relevant datasets as they become available. Ethics and dissemination: The project has ethical approval and the results will be submitted for publication in a peer-reviewed journal.

Effect of Dexamethasone in Hospitalized Patients with COVID-19: Preliminary Report (preprint)

Background: Coronavirus disease 2019 (COVID-19) is associated with diffuse lung damage. Corticosteroids may modulate immune-mediated lung injury and reducing progression to respiratory failure and death. Methods: The Randomised Evaluation of COVID-19 therapy (RECOVERY) trial is a randomized, controlled, open-label, adaptive, platform trial comparing a range of possible treatments with usual care in patients hospitalized with COVID-19. We report the preliminary results for the comparison of dexamethasone 6 mg given once daily for up to ten days vs. usual care alone. The primary outcome was 28-day mortality. Results: 2104 patients randomly allocated to receive dexamethasone were compared with 4321 patients concurrently allocated to usual care. Overall, 454 (21.6%) patients allocated dexamethasone and 1065 (24.6%) patients allocated usual care died within 28 days (age-adjusted rate ratio [RR] 0.83; 95% confidence interval [CI] 0.74 to 0.92; P<0.001). The proportional and absolute mortality rate reductions varied significantly depending on level of respiratory support at randomization (test for trend p<0.001): Dexamethasone reduced deaths by one-third in patients receiving invasive mechanical ventilation (29.0% vs. 40.7%, RR 0.65 [95% CI 0.51 to 0.82]; p<0.001), by one-fifth in patients receiving oxygen without invasive mechanical ventilation (21.5% vs. 25.0%, RR 0.80 [95% CI 0.70 to 0.92]; p=0.002), but did not reduce mortality in patients not receiving respiratory support at randomization (17.0% vs. 13.2%, RR 1.22 [95% CI 0.93 to 1.61]; p=0.14). Conclusions: In patients hospitalized with COVID-19, dexamethasone reduced 28-day mortality among those receiving invasive mechanical ventilation or oxygen at randomization, but not among patients not receiving respiratory support.

Ethnicity and Outcomes from COVID-19: The ISARIC CCP-UK Prospective Observational Cohort Study of Hospitalised Patients (preprint)

Background: Reports of ethnic inequalities in COVID-19 outcomes are conflicting and the reasons for any differences in outcomes are unclear. We investigated ethnic inequalities in critical care admission patterns, the need for invasive mechanical ventilation (IMV), and in-hospital mortality, among hospitalised patients with COVID-19. Methods: We undertook a prospective cohort study in which dedicated research staff recruited hospitalised patients with suspected/confirmed COVID-19 from 260 hospitals across England, Scotland and Wales, collecting data directly and from records between 6th February and 8th May 2020 with follow-up until 22nd May 2020. Analysis used hierarchical regression models accounting for confounding, competing risks, and clustering of patients in hospitals. Potential mediators for death were explored with a three-way decomposition mediation analysis. Findings: Of 34,986 patients enrolled, 30,693 (88%) had ethnicity recorded: South Asian (1,388, 5%), East Asian (266, 1%), Black (1,094, 4%), Other Ethnic Minority (2,398, 8%) (collectively Ethnic Minorities), and White groups (25,547, 83%). Ethnic Minorities were younger and more likely to have diabetes (type 1/type 2) but had fewer other comorbidities such as chronic heart disease or dementia than the White group. No difference was seen between ethnic groups in the time from symptom onset to hospital admission, nor in illness severity at admission. Critical care admission was more common in South Asian (odds ratio 1.28, 95% confidence interval 1.09 to 1.52), Black (1.36, 1.14 to 1.62), and Other Ethnic Minority (1.29, 1.13 to 1.47) groups compared to the White group, after adjusting for age, sex and location. This was broadly unchanged after adjustment for deprivation and comorbidities. Patterns were similar for IMV. Higher adjusted mortality was seen in the South Asian (hazard ratio 1.19, 1.05 to 1.36), but not East Asian (1.00, 0.74 to 1.35), Black (1.05, 0.91 to 1.26) or Other Ethnic Minority (0.99, 0.89 to 1.10) groups, compared to the White group. 18% (95% CI, 9% to 56%) of the excess mortality in South Asians was mediated by pre-existing diabetes. Interpretation: Ethnic Minorities in hospital with COVID-19 were more likely to be admitted to critical care and receive IMV than Whites, despite similar disease severity on admission, similar duration of symptoms, and being younger with fewer comorbidities. South Asians are at greater risk of dying, due at least in part to a higher prevalence of pre-existing diabetes. Trial Registration: The study was registered at https://www.isrctn.com/ISRCTN66726260. Funding Statement: This work is supported by grants from: the National Institute for Health Research [award CO-CIN-01], the Medical Research Council [grant MC_PC_19059] and by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford [NIHR award 200907], Wellcome Trust and Department for International Development [215091/Z/18/Z], and the Bill and Melinda Gates Foundation [OPP1209135], and Liverpool Experimental Cancer Medicine Centre for providing infrastructure support for this research (Grant Reference: C18616/A25153). JSN-V-T is seconded to the Department of Health and Social Care, England (DHSC).Declaration of Interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: AB Docherty reports grants from Department of Health and Social Care, during the conduct of the study; grants from Wellcome Trust, outside the submitted work; CA Green reports grants from DHSC National Institute of Health Research UK, during the conduct of the study; PW Horby reports grants from Wellcome Trust / Department for International Development / Bill and Melinda Gates Foundation, grants from NIHR , during the conduct of the study; JS Nguyen-Van-Tam reports grants from Department of Health and Social Care, England, during the conduct of the study; and is seconded to the Department of Health and Social Care, England (DHSC); PJM Openshaw reports personal fees from consultancies and from European Respiratory Society; grants from MRC, MRC Global Challenge Research Fund, EU, NIHR Biomedical Research Centre, MRC/GSK, Wellcome Trust, NIHR (HPRU in Respiratory Infection), and NIHR Senior Investigator outside the submitted work. His role as President of the British Society for Immunology was unpaid but travel and accommodation at some meetings was provided by the Society. JK Baillie reports grants from Medical Research Council UK; MG Semple reports grants from DHSC National Institute of Health Research UK, grants from Medical Research Council UK, grants from Health Protection Research Unit in Emerging & Zoonotic Infections, University of Liverpool, during the conduct of the study; other from Integrum Scientific LLC, Greensboro, NC, USA, outside the submitted work. EM Harrison, H Ardwick, J Dunning, R Pius, L Norman, KA Holden, JM Read, G Carson, L Merson, J Lee, D Plotkin, L Sigfrid, S Halpin, C Jackson, and C Gamble, all declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; and no other relationships or activities that could appear to have influenced the submitted work.Ethics Approval Statement: Ethical approval was given by the South Central – Oxford C Research Ethics Committee in England (Ref: 13/SC/0149), and by the Scotland A Research Ethics Committee (Ref: 20/SS/0028).

Compassionate drug (mis)use during pandemics: lessons for COVID-19 from 2009. (preprint)

Background: New emerging infections have no known treatment. Assessing potential drugs for safety and efficacy enables clinicians to make evidence-based treatment decisions, and contributes to overall outbreak control. However, it is difficult to launch clinical trials in the unpredictable environment of an outbreak. We conducted a bibliometric systematic review for the 2009 influenza pandemic to determine the speed, and quality of evidence generation for treatments. This informs approaches to high-quality evidence generation in this and future pandemics. Methods: We searched PubMed for all clinical data (including clinical trial, observational and case series) describing treatment for patients with influenza A(H1N1)pdm09 and ClinicalTrials.gov for research that aimed to enrol patients with the disease. Results: 33869 treatment courses for patients hospitalised with A(H1N1)pdm09 were detailed in 160 publications. Most were retrospective observational studies or case series. 592 patients received treatment (or placebo) as participants in a registered interventional clinical trial with results publicly available. None of these registered trial results were available during the timeframe of the pandemic, and the median date of publication was 213 days after the Public Health Emergency of International Concern ended. Conclusion: Patients were frequently treated for pandemic influenza with drugs not registered for this indication, but rarely under circumstances of high-quality data capture. The result was a reliance on use under compassionate circumstances, resulting in continued uncertainty regarding the potential benefits and harms of anti-viral treatment. Rapid scaling of clinical trials is critical for generating a quality evidence base during pandemics.