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BackgroundImmunocompromised 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. MethodsWe 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. FindingsBetween 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 in-hospital 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. ConclusionsImmunocompromised 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. FundingNational Institute for Health Research; Medical Research Council; Chief Scientist Office, Scotland.
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A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The results of a group of studies answering the same, or similar, questions can be combined by meta-analysis to find a consensus or a more reliable answer. Ranking aggregation methods can be used to combine gene lists from various sources in meta-analyses. Evaluating a ranking aggregation method on a specific type of dataset before using it is required to support the reliability of the result since the property of a dataset can influence the performance of an algorithm. Evaluation of aggregation methods is usually based on a simulated database especially for the algorithms designed for gene lists because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. In this study, a group of existing methods and their variations which are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data was used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomics data, with various heterogeneity of quality, noise level, and a mix of unranked and ranked data using 20000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (NSCLC), and bacteria (macrophage apoptosis) was performed. We summarise our evaluation results in terms of a simple flowchart to select a ranking aggregation method for genomics data.
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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 recruits critically-ill cases and compares their genomes 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.
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Structured AbstractO_ST_ABSObjectivesC_ST_ABSThe long-term consequences of severe Covid-19 requiring hospital admission are not well characterised. The objective of this study was to establish the long-term effects of Covid-19 following hospitalisation and the impact these may have on patient reported outcome measures. DesignA multicentre, prospective cohort study with at least 3 months follow-up of participants admitted to hospital between 5th February 2020 and 5th October 2020. Setting31 hospitals in the United Kingdom. Participants327 hospitalised participants discharged alive from hospital with confirmed/high likelihood SARS-CoV-2 infection. Main outcome measures and comparisonsThe primary outcome was self-reported recovery at least ninety days after initial Covid-19 symptom onset. Secondary outcomes included new symptoms, new or increased disability (Washington group short scale), breathlessness (MRC Dyspnoea scale) and quality of life (EQ5D-5L). We compared these outcome measures across age, comorbidity status and in-hospital Covid-19 severity to identify groups at highest risk of developing long-term difficulties. Multilevel logistic and linear regression models were built to adjust for the effects of patient and centre level risk factors on these outcomes. ResultsIn total 53.7% (443/824) contacted participants responded, yielding 73.8% (327/443) responses with follow-up of 90 days or more from symptom onset. The median time between symptom onset of initial illness and completing the participant questionnaire was 222 days (Interquartile range (IQR) 189 to 269 days). In total, 54.7% (179/327) of participants reported they did not feel fully recovered. Persistent symptoms were reported by 93.3% (305/325) of participants, with fatigue the most common (82.8%, 255/308), followed by breathlessness (53.5%, 175/327). 46.8% (153/327) reported an increase in MRC dyspnoea scale of at least one grade. New or worse disability was reported by 24.2% (79/327) of participants. Overall (EQ5D-5L) summary index was significantly worse at the time of follow-up (median difference 0.1 points on a scale of 0 to 1, IQR: -0.2 to 0.0). Females under the age of 50 years were five times less likely to report feeling recovered (adjusted OR 5.09, 95% CI 1.64 to 15.74), were more likely to have greater disability (adjusted OR 4.22, 95% CI 1.12 to 15.94), twice as likely to report worse fatigue (adjusted OR 2.06, 95% CI 0.81 to 3.31) and seven times more likely to become more breathless (adjusted OR 7.15, 95% CI 2.24 to 22.83) than men of the same age. ConclusionsSurvivors of Covid-19 experienced long-term symptoms, new disability, increased breathlessness, and reduced quality of life. These findings were present even in young, previously healthy working age adults, and were most common in younger females. Policymakers should fund further research to identify effective treatments for long-Covid and ensure healthcare, social care and welfare support is available for individuals with long-Covid. Section 1: What is already known on this topicO_LILong-term symptoms after hospitalisation for Covid-19 have been reported, but it is not clear what impact this has on quality of life. C_LIO_LIIt is not known which patient groups are most likely to have long-term persistent symptoms following hospitalisation for Covid-19, or if this differs by disease severity. C_LI Section 2: What this study addsO_LIMore than half of patients reported not being fully recovered 7 months after onset of Covid-19 symptoms. C_LIO_LIPreviously healthy participants and those under the age of 50 had higher odds of worse long-term outcomes compared to older participants and those with comorbidities. C_LIO_LIYounger women and those with more severe acute disease in-hospital had the worst long-term outcomes. C_LIO_LIPolicy makers need to ensure there is long-term support for people experiencing long-Covid and should plan for lasting long-term population morbidity. Funding for research to understand mechanisms underlying long-Covid and identify potential interventions for testing in randomised trials is urgently required. C_LI
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BackgroundMortality rates of UK patients hospitalised with COVID-19 appeared to fall during the first wave. We quantify potential drivers of this change and identify groups of patients who remain at high risk of dying in hospital. MethodsThe International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) WHO Clinical Characterisation Protocol UK recruited a prospective cohort admitted to 247 acute UK hospitals with COVID-19 in the first wave (March to August 2020). Outcome was hospital mortality within 28 days of admission. We performed a three-way decomposition mediation analysis using natural effects models to explore associations between week of admission and hospital mortality adjusting for confounders (demographics, comorbidity, illness severity) and quantifying potential mediators (respiratory support and steroids). FindingsUnadjusted hospital mortality fell from 32.3% (95%CI 31.8, 32.7) in March/April to 16.4% (95%CI 15.0, 17.8) in June/July 2020. Reductions were seen in all ages, ethnicities, both sexes, and in comorbid and non-comorbid patients. After adjustment, there was a 19% reduction in the odds of mortality per 4 week period (OR 0.81, 95%CI 0.79, 0.83). 15.2% of this reduction was explained by greater disease severity and comorbidity earlier in the epidemic. The use of respiratory support changed with greater use of non-invasive ventilation (NIV). 22.2% (OR 0.94, 95%CI 0.94, 0.96) of the reduction in mortality was mediated by changes in respiratory support. InterpretationThe fall in hospital mortality in COVID-19 patients during the first wave in the UK was partly accounted for by changes in case mix and illness severity. A significant reduction was associated with differences in respiratory support and critical care use, which may partly reflect improved clinical decision making. The remaining improvement in mortality is not explained by these factors, and may relate to community behaviour on inoculum dose and hospital capacity strain. FundingNIHR & MRC Key points / Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSRisk factors for mortality in patients hospitalised with COVID-19 have been established. However there is little literature regarding how mortality is changing over time, and potential explanations for why this might be. Understanding changes in mortality rates over time will help policy makers identify evolving risk, strategies to manage this and broader decisions about public health interventions. Added value of this studyMortality in hospitalised patients at the beginning of the first wave was extremely high. Patients who were admitted to hospital in March and early April were significantly more unwell at presentation than patients who were admitted in later months. Mortality fell in all ages, ethnic groups, both sexes and in patients with and without comorbidity, over and above contributions from falling illness severity. After adjustment for these variables, a fifth of the fall in mortality was explained by changes in the use of respiratory support and steroid treatment, along with associated changes in clinical decision-making relating to supportive interventions. However, mortality was persistently high in patients who required invasive mechanical ventilation, and in those patients who received non-invasive ventilation outside of critical care. Implications of all the available evidenceThe observed reduction in hospital mortality was greater than expected based on the changes seen in both case mix and illness severity. Some of this fall can be explained by changes in respiratory care, including clinical learning. In addition, introduction of community policies including wearing of masks, social distancing, shielding of vulnerable patients and the UK lockdown potentially resulted in people being exposed to less virus. The decrease in mortality varied depending on the level of respiratory support received. Patients receiving invasive mechanical ventilation have persistently high mortality rates, albeit with a changing case-mix, and further research should target this group. Severe COVID-19 disease has primarily affected older people in the UK. Many of these people, but not all have significant frailty. It is essential to ensure that patients and their families remain at the centre of decision-making, and we continue with an individualised approach to their treatment and care.
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Introductory paragraphThe mechanisms that underpin COVID-19 disease severity, and determine the outcome of infection, are only beginning to be unraveled. The host inflammatory response contributes to lung injury, but circulating mediators levels fall below those in classical cytokine storms. We analyzed serial plasma samples from 619 patients hospitalized with COVID-19 recruited through the prospective multicenter ISARIC clinical characterization protocol U.K. study and 39 milder community cases not requiring hospitalization. Elevated levels of numerous mediators including angiopoietin-2, CXCL10, and GM-CSF were seen at recruitment in patients who later died. Markers of endothelial injury (angiopoietin-2 and von-Willebrand factor A2) were detected early in some patients, while inflammatory cytokines and markers of lung injury persisted for several weeks in fatal COVID-19 despite decreasing antiviral cytokine levels. Overall, markers of myeloid or endothelial cell activation were associated with severe, progressive, and fatal disease indicating a central role for innate immune activation and vascular inflammation in COVID-19.
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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.
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The subset of patients who develop critical illness in Covid-19 have extensive inflammation affecting the lungs1 and are strikingly different from other patients: immunosuppressive therapy benefits critically-ill patients, but may harm some non-critical cases.2 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-19.3 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 2244 critically-ill Covid-19 patients from 208 UK intensive care units (ICUs), representing >95% of all ICU beds. Ancestry-matched controls were drawn from the UK Biobank population study and results were confirmed in GWAS comparisons with two other population control groups: the 100,000 genomes project and Generation Scotland. We identify and replicate three novel genome-wide significant associations, at chr19p13.3 (rs2109069, p = 3.98 x 10-12), within the gene encoding dipeptidyl peptidase 9 (DPP9), at chr12q24.13 (rs10735079, p =1.65 x 10-8) in a gene cluster encoding antiviral restriction enzyme activators (OAS1, OAS2, OAS3), and at chr21q22.1 (rs2236757, p = 4.99 x 10-8) in the interferon receptor gene 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 = 4.77 x 10-30). We identify potential targets for repurposing of 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. Our results identify robust genetic signals relating to key host antiviral defence mechanisms, and mediators of inflammatory organ damage in Covid-19. Both mechanisms may be amenable to targeted treatment with existing drugs. Large-scale randomised clinical trials will be essential before any change to clinical practice.
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IntroductionVery little is known about possible clinical sequelae that may persist after resolution of the acute Coronavirus Disease 2019 (COVID-19). A recent longitudinal cohort from Italy including 143 patients recovered after hospitalisation with COVID-19 reported that 87% had at least one ongoing symptom at 60 day follow-up. Early indications suggest that patients with COVID-19 may need even more psychological support than typical ICU patients. The assessment of risk factors for longer term consequences requires a longitudinal study linked to data on pre-existing conditions and care received during the acute phase of illness. Methods and analysisThis is an international open-access prospective, observational multi-site study. It will enrol patients following a diagnosis of COVID-19. Tier 1 is developed for following up patients day 28 post-discharge, additionally at 3 to 6 months intervals. This module can be used to identify sub-sets of patients experiencing specific symptomatology or syndromes for further follow up. A Tier 2 module will be developed for in-clinic, in-depth follow up. The primary aim is to characterise physical consequences in patients post-COVID-19. Secondary aim includes estimating the frequency of and risk factors for post-COVID-19 medical sequalae, psychosocial consequences and post-COVID-19 mortality. A subset of patients will have sampling to characterize longer term antibody, innate and cell-mediated immune responses to SARS-CoV-2. Ethics and disseminationThis collaborative, open-access study aims to characterize the frequency of and risk factors for long-term consequences and characterise the immune response over time in patients following a diagnosis of COVID-19 and facilitate standardized and longitudinal data collection globally. The outcomes of this study will inform strategies to prevent long term consequences; inform clinical management, direct rehabilitation, and inform public health management to reduce overall morbidity and improve outcomes of COVID-19. Article summaryO_ST_ABSStrengths and limitations of this studyC_ST_ABSO_LIAs an international prospective, observational study we provide open-access standardised tools that can be adapted by any site interested in following up patients with COVID-19, for independent or combined analysis, to forward knowledge into short and long term consequences of COVID-19. C_LIO_LIThis study aims to inform strategies to prevent longer term sequalae; inform clinical management, rehabilitation, and public health management strategies to reduce morbidity and improve outcomes. C_LIO_LIThe protocol will be used for a sub-set of patients, already included in the existing cohort of more than 85,973 individuals hospitalized with confirmed COVID-19 infection across 42 countries (as of 20 July 2020), using the ISARIC/WHO standardized Core- or RAPID Case Report Forms (CRFs). C_LIO_LIThe data will be linked with data on pre-existing comorbidities, presentation, clinical care and treatments documented in the existing cohort already documented using the ISARIC/WHO standardized Core- or RAPID CRFs. C_LIO_LIThe data collection tool is developed to facilitate wide dissemination and uptake, by enabling patient self-assessment, however, follow up of patients requires consent and resources, which might limit the uptake and bias the data towards countries /sites with capacity to follow up patients over time. C_LI
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BackgroundSevere COVID-19 is characterised by fever, cough, and dyspnoea. Symptoms affecting other organ systems have been reported. However, it is the clinical associations of different patterns of symptoms which influence diagnostic and therapeutic decision-making. In this study, we applied simple machine learning techniques to a large prospective cohort of hospitalised patients with COVID-19 identify clinically meaningful sub-groups. MethodsWe obtained structured clinical data on 59 011 patients in the UK (the ISARIC Coronavirus Clinical Characterisation Consortium, 4C) and used a principled, unsupervised clustering approach to partition the first 25 477 cases according to symptoms reported at recruitment. We validated our findings in a second group of 33 534 cases recruited to ISARIC-4C, and in 4 445 cases recruited to a separate study of community cases. FindingsUnsupervised clustering identified distinct sub-groups. First, a core symptom set of fever, cough, and dyspnoea, which co-occurred with additional symptoms in three further 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. InterpretationThe large scale of the 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. FundingMedical Research Council; National Institute Health Research; Well-come Trust; Department for International Development; Bill and Melinda Gates Foundation; Liverpool Experimental Cancer Medicine Centre.
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ObjectivesTo develop and validate a pragmatic risk score to predict mortality for patients admitted to hospital with covid-19. DesignProspective observational cohort study: ISARIC WHO CCP-UK study (ISARIC Coronavirus Clinical Characterisation Consortium [4C]). Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited between 21 May and 29 June 2020. Setting260 hospitals across England, Scotland, and Wales. ParticipantsAdult patients ([≥]18 years) admitted to hospital with covid-19 admitted at least four weeks before final data extraction. Main outcome measuresIn-hospital mortality. ResultsThere were 34 692 patients included in the derivation dataset (mortality rate 31.7%) and 22 454 in the validation dataset (mortality 31.5%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea, and C-reactive protein (score range 0-21 points). The 4C risk stratification score demonstrated high discrimination for mortality (derivation cohort: AUROC 0.79; 95% CI 0.78 - 0.79; validation cohort 0.78, 0.77-0.79) with excellent calibration (slope = 1.0). Patients with a score [≥]15 (n = 2310, 17.4%) had a 67% mortality (i.e., positive predictive value 67%) compared with 1.0% mortality for those with a score [≤]3 (n = 918, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (AUROC range 0.60-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). ConclusionsWe have developed and validated an easy-to-use risk stratification score based on commonly available parameters at hospital presentation. This outperformed existing scores, demonstrated utility to directly inform clinical decision making, and can be used to stratify inpatients with covid-19 into different management groups. The 4C Mortality Score may help clinicians identify patients with covid-19 at high risk of dying during current and subsequent waves of the pandemic. Study registrationISRCTN66726260
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BackgroundTissue inflammation is associated with organ dysfunction and death in Covid-19. The efficacy of dexamethasone in preventing mortality in critical Covid-19 suggests that inflammation has a causal role in death. Whether this deleterious inflammation is a direct response to the presence of SARS-CoV-2, or an independent immuno-pathologic process, is unknown. MethodsTissue was acquired from detailed post-mortem examinations conducted on 11 well characterised hospitalised patients with fatal Covid-19. SARS-CoV-2 organotropism was mapped at an organ level by multiplex PCR and sequencing, with cellular resolution achieved by in situ viral spike (S) protein detection. Histological evidence of inflammation and organ injury was systematically examined, and the pulmonary immune response characterized with multiplex immunofluorescence. FindingsSARS-CoV-2 was detected across a wide variety of organs, most frequently in the respiratory tract but also in numerous extra-pulmonary sites. Minimal histological evidence of inflammation was identified in non-pulmonary organs despite frequent detection of viral RNA and protein. At a cellular level, viral protein was identified without adjacent inflammation in the intestine, liver and kidney. Severe inflammatory change was restricted to the lung and reticulo-endothelial system. Diffuse alveolar damage, pulmonary thrombi and a monocyte/myeloid-predominant vasculitis were the predominant pulmonary findings, though there was not a consistent association between viral presence and either the presence or nature of the inflammatory response within the lung. Immunophenotyping revealed an influx of macrophages, monocytes and T cells into pulmonary parenchyma. Bone marrow examination revealed plasmacytosis, erythroid dysplasia and iron-laden macrophages. Plasma cell excess was also present in lymph node, spleen and lung. These stereotyped reticulo-endothelial responses occurred largely independently of the presence of virus in lymphoid tissues. ConclusionsTissue inflammation and organ dysfunction in fatal Covid-19 do not map to the tissue and cellular distribution of SARS-CoV-2, demonstrating tissue-specific tolerance. We conclude that death in Covid-19 is primarily a consequence of immune-mediated, rather than pathogen-mediated, organ inflammation and injury. FundingThe Chief Scientist Office, LifeArc, Medical Research Scotland, UKRI (MRC).
