BackgroundSARS-CoV-2 nosocomial transmission to patients and healthcare workers (HCWs) has occurred throughout the COVID-19 pandemic. Aerosol generating procedures (AGPs) seemed particularly risky, and policies have restricted their use in all settings. We examined the prevalence of aerosolized SARS-CoV-2 in the rooms of COVID-19 patients requiring AGP or supplemental oxygen compared to those on room air. MethodsSamples were collected prospectively near to adults hospitalised with COVID-19 at two tertiary care hospitals in the UK from November 2020 - October 2021. The Sartorius MD8 AirPort air sampler was used to collect air samples at a minimum distance of 1.5 meters from patients. RT-qPCR was used following overnight incubation of membranes in culture media and extraction. ResultsWe collected 219 samples from patients rooms: individuals on room air (n=67), receiving oxygen (n=65) or AGP (n=67). Of these, 54 (24.6%) samples were positive for SARS-CoV-2 viral RNA. The highest prevalence was identified in the air around patients receiving oxygen (32.3%, n=21, CI95% 22.2 to 44.3%) with AGP and room air recording prevalence of (20.7%, n=18, CI95% 14.1 - 33.7%) and (22.3%, n=15, CI95% 13.5 - 30.4%) respectively. We did not detect a significant difference in the observed frequency of viral RNA between interventions. InterpretationSARS-CoV-2 viral RNA was detected in the air of hospital rooms of COVID-19 patients, and AGPs did not appear to impact the likelihood of viral RNA. Enhanced respiratory protection and appropriate infection prevention and control measures are required to be fully and carefully implemented for all COVID-19 patients to reduce risk of aerosol transmission.
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 real-world 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.
BackgroundWhilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. MethodsHere, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. ResultsOur analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61 - 0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. ConclusionsAlthough clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome.
BackgroundContinuous 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. Methods30 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. ResultsOverall 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. ConclusionsThe 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.
The survival of newer variants of SARS-CoV-2 on a representative surface has been compared to the established UK circulating isolate to determine whether enhanced environmental stability could play a part in their increased transmissibility. Stainless-steel coupons were inoculated with liquid cultures of the three variants, with coupons recovered over seven days and processed for recoverable viable virus using plaque assay. After drying, there was no significant difference in inactivation rates between variants. Indicating there is no increased environmental persistence from the new variants.
The transmission of SARS-CoV-2 is likely to occur through a number of routes, including contact with contaminated surfaces. Many studies have used RT-PCR analysis to detect SARS-CoV-2 RNA on surfaces but seldom has viable virus been detected. This paper investigates the viability over time of SARS-CoV-2 dried onto a range of materials and compares viability of the virus to RNA copies recovered, and whether virus viability is concentration dependant. Viable virus persisted for the longest time on surgical mask material and stainless steel with a 99.9% reduction in viability by 124 and 113 hours respectively. Viability of SARS-CoV-2 reduced the fastest on a polyester shirt, with a 99.9% reduction within 2.5 hours. Viability on cotton was reduced second fastest, with 99.9% reduction in 72 hours. RNA on all the surfaces exhibited a one log reduction in genome copy recovery over 21 days. The findings show that SARS-CoV-2 is most stable on non-porous hydrophobic surfaces. RNA is highly stable when dried on surfaces with only one log reduction in recovery over three weeks. In comparison, SARS-CoV-2 viability reduced more rapidly, but this loss in viability was found to be independent of starting concentration. Expected levels of SARS-CoV-2 viable environmental surface contamination would lead to undetectable levels within two days. Therefore, when RNA is detected on surfaces it does not directly indicate presence of viable virus even at high CT values. ImportanceThis study shows the impact of material type on the viability of SARS-CoV-2 on surfaces. It demonstrates that the decay rate of viable SARS-CoV-2 is independent of starting concentration. However, RNA shows high stability on surfaces over extended periods. This has implications for interpretation of surface sampling results using RT-PCR to determine the possibility of viable virus from a surface. Unless sampled immediately after contamination it is difficult to align RNA copy numbers to quantity of viable virus on a surface.
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.
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.
Understanding how Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is spread within the hospital setting is essential if staff are to be adequately protected, effective infection control measures are to be implemented and nosocomial transmission is to be prevented. The presence of SARS-CoV-2 in the air and on environmental surfaces around hospitalised patients, with and without respiratory symptoms, was investigated. Environmental sampling was carried out within eight hospitals in England during the first wave of the COVID-19 outbreak. Samples were analysed using reverse transcription polymerase chain reaction (RT-PCR) and virus isolation assays. SARS-CoV-2 RNA was detected on 30 (8.9%) of 336 environmental surfaces. Ct values ranged from 28.8 to 39.1 equating to 2.2 x 105 to 59 genomic copies/swab. Concomitant bacterial counts were low, suggesting the cleaning performed by nursing and domestic staff across all eight hospitals was effective. SARS-CoV-2 RNA was detected in four of 55 air samples taken < 1 m from four different patients. In all cases, the concentration of viral RNA was low and ranged from < 10 to 460 genomic copies per m3 of air. Infectious virus was not recovered from any of the PCR positive samples analysed. Effective cleaning can reduce the risk of fomite (contact) transmission but some surface types may facilitate the survival, persistence and/or dispersal of SARS-CoV-2. The presence of low or undetectable concentrations of viral RNA in the air supports current guidance on the use of specific PPE ensembles for aerosol and non-aerosol generating procedures.
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.
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
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 participation 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, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries. The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19. For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%) C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/
ObjectiveTo characterise the clinical features of children and young people admitted to hospital with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the UK, and explore factors associated with admission to critical care, mortality, and development of multisystem inflammatory syndrome in children and adolescents temporarily related to covid-19 (MIS-C). DesignProspective observational cohort study with rapid data gathering and near real time analysis. Setting260 acute care hospitals in England, Wales, and Scotland between 17th January and 5th June 2020, with a minimal follow-up time of two weeks (to 19th June 2020). Participants451 children and young people aged less than 19 years admitted to 116 hospitals and enrolled into the International Severe Acute Respiratory and emergency Infections Consortium (ISARIC) WHO Clinical Characterisation Protocol UK study with laboratory-confirmed SARS-CoV-2. Main Outcome MeasuresAdmission to critical care (high dependency or intensive care), in-hospital mortality, or meeting the WHO preliminary case definition for MIS-C. ResultsMedian age was 3.9 years [interquartile range (IQR) 0.3-12.9 years], 36% (162/451) were under 12 months old, and 57% (256/450) were male. 56% (224/401) were White, 12% (49/401) South Asian and 10% (40/401) Black. 43% (195/451) had at least one recorded comorbidity. A muco-enteric cluster of symptoms was identified, closely mirroring the WHO MIS-C criteria. 17% of children (72/431) were admitted to critical care. On multivariable analysis this was associated with age under one month odds ratio 5.05 (95% confidence interval 1.69 to 15.72, p=0.004), age 10 to 14 years OR 3.11 (1.21 to 8.55, p=0.022) and Black ethnicity OR 3.02 (1.30 to 6.84, p=0.008). Three young people died (0.7 %, 3/451) aged 16 to 19 years, all of whom had profound comorbidity. Twelve percent of children (36/303) met the WHO MIS-C criteria, with the first patient developing symptoms in mid-March. Those meeting MIS-C criteria were older, (median age 10.8 years ([IQR 8.4-14.1] vs 2.0 [0.2-12.6]), p<0.001) and more likely to be of non-White ethnicity (70% (23/33) vs 43% (101/237), p=0.005). Children with MIS-C were four times more likely to be admitted to critical care (61% (22/36) vs 15% (40/267, p<0.001). In addition to the WHO criteria, children with MIS-C were more likely to present with headache (45% (13/29) vs 11% (19/171), p<0.001), myalgia (39% (11/28) vs 7% (12/170), p<0.001), sore throat (37% (10/27) vs (13% (24/183, p = 0.004) and fatigue (57% (17/30) vs 31% (60/192), p =0.012) than children who did not and to have a platelet count of less than 150 x109/L (30% (10/33) vs 10% (24/232), p=0.004). ConclusionsOur data confirms less severe covid-19 in children and young people than in adults and we provide additional evidence for refining the MIS-C case definition. The identification of a muco-enteric symptom cluster also raises the suggestion that MIS-C is the severe end of a spectrum of disease. Study registrationISRCTN66726260
BackgroundSignificant nosocomial transmission of SARS-CoV-2 has been demonstrated. Understanding the prevalence of SARS-CoV-2 carriage amongst HCWs at work is necessary to inform the development of HCW screening programmes to control nosocomial spread. MethodsCross-sectional snapshot survey from April-May 2020; HCWs recruited from six UK hospitals. Participants self-completed a health questionnaire and underwent a combined viral nose and throat swab, tested by Polymerase Chain Reaction (PCR) for SARS-CoV-2 with viral culture on majority of positive samples. FindingsPoint prevalence of SARS-CoV-2 carriage across the sites was 2{middle dot}0% (23/1152 participants), median cycle threshold value 35{middle dot}70 (IQR:32{middle dot}42-37{middle dot}57). 17 were previously symptomatic, two currently symptomatic (isolated anosmia and sore throat); the remainder declared no prior or current symptoms. Symptoms in the past month were associated with threefold increased odds of testing positive (aOR 3{middle dot}46, 95%CI 1{middle dot}38-8{middle dot}67; p=0{middle dot}008). SARS-CoV-2 virus was isolated from only one (5%) of nineteen cultured samples. A large proportion (39%) of participants reported symptoms in the past month. InterpretationThe point-prevalence is similar to previous estimates for HCWs in April 2020, though a magnitude higher than in the general population. Based upon interpretation of symptom history and testing results including viral culture, the majority of those testing positive were unlikely to be infectious at time of sampling. Development of screening programmes must balance the potential to identify additional cases based upon likely prevalence, expanding the symptoms list to encourage HCW testing, with resource implications and risks of excluding those unlikely to be infectious with positive tests. FundingPublic Health England. Word CountO_ST_ABSResearch in contextC_ST_ABSEvidence before this studyA search of PubMed was performed on 29th April 2020 to identify other major works in this field, using the search terms ("novel coronavirus" OR "SARS-CoV-2" OR "COVID-19" OR "coronavirus") AND ("workers" OR "staff") AND ("testing" OR "screening") from 31st December 2019 onwards with no other limits. This search was updated on 10th May 2020, and in addition reference lists were checked and pre-print papers were shared with us through professional networks. We found three papers commenting on prevalence of asymptomatic/pauci-symptomatic SARS-CoV-2 infection in healthcare workers, with prevalence estimates ranging from 1{middle dot}1 to 8%. One of these studies explored previous symptoms in depth, though this was based upon a retrospective questionnaire and thus subject to recall bias. None of these studies explored exposures to the SARS-CoV-2 virus, commented on whether participants had been tested prior to the start of the study, or broke down results by staff role. Only one reported on estimated viral load (as inferred from cycle threshold [Ct] value), and none reported attempting viral culture. Added value of this studyThis is the first published study of which we are aware that has been conducted across multiple sites in England and is therefore potentially more representative of the overall prevalence of SARS-CoV-2 infectivity amongst HCWs in the workplace. We explored symptoms in the preceding month in more depth than previous studies and in addition asked about previous test results and various exposures, also not commented on in other studies. Additionally, we attempted to isolate virus from some PCR-positive samples to look for evidence of infectious virus. Implications of all the available evidenceAuthors of previous studies have proposed that screening asymptomatic HCWs for SARS-CoV-2 RNA may be beneficial, in addition to screening symptomatic HCWs. Our findings suggest that when prevalence of COVID-19 is very low, routine and repeated screening would be unlikely to have significant value, especially given the majority of participants testing positive in this study were unlikely to be infectious. However, in situations where prevalence levels are high in a particular population or setting, for example in a hospital outbreak, widening the case definition, or screening all HCWs irrespective of symptoms, may be of benefit.
Structured abstractO_ST_ABSObjectiveC_ST_ABSTo characterize the clinical features of patients with severe COVID-19 in the UK. DesignProspective observational cohort study with rapid data gathering and near real-time analysis, using a pre-approved questionnaire adopted by the WHO. Setting166 UK hospitals between 6th February and 18th April 2020. Participants16,749 people with COVID-19. InterventionsNo interventions were performed, but with consent samples were taken for research purposes. Many participants were co-enrolled in other interventional studies and clinical trials. ResultsThe median age was 72 years [IQR 57, 82; range 0, 104], the median duration of symptoms before admission was 4 days [IQR 1,8] and the median duration of hospital stay was 7 days [IQR 4,12]. The commonest comorbidities were chronic cardiac disease (29%), uncomplicated diabetes (19%), non-asthmatic chronic pulmonary disease (19%) and asthma (14%); 47% had no documented reported comorbidity. Increased age and comorbidities including obesity were associated with a higher probability of mortality. Distinct clusters of symptoms were found: 1. respiratory (cough, sputum, sore throat, runny nose, ear pain, wheeze, and chest pain); 2. systemic (myalgia, joint pain and fatigue); 3. enteric (abdominal pain, vomiting and diarrhoea). Overall, 49% of patients were discharged alive, 33% have died and 17% continued to receive care at date of reporting. 17% required admission to High Dependency or Intensive Care Units; of these, 31% were discharged alive, 45% died and 24% continued to receive care at the reporting date. Of those receiving mechanical ventilation, 20% were discharged alive, 53% died and 27% remained in hospital. ConclusionsWe present the largest detailed description of COVID-19 in Europe, demonstrating the importance of pandemic preparedness and the need to maintain readiness to launch research studies in response to outbreaks. Trial documentationAvailable at https://isaric4c.net/protocols. Ethical approval in England and Wales (13/SC/0149), and Scotland (20/SS/0028). ISRCTN (pending).
