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

Introduction: Case definitions are used to guide clinical practice, surveillance and research protocols. However, how they identify COVID-19-hospitalised patients is not fully understood. We analysed the proportion of hospitalised patients with laboratory-confirmed COVID-19, in the ISARIC prospective cohort study database, meeting widely used case definitions. Methods: Patients were assessed using the Centers for Disease Control (CDC), European Centre for Disease Prevention and Control (ECDC), World Health Organization (WHO) and UK Health Security Agency (UKHSA) case definitions by age, region and time. Case fatality ratios (CFRs) and symptoms of those who did and who did not meet the case definitions were evaluated. Patients with incomplete data and non-laboratory-confirmed test result were excluded.Results: A total of 263,218 of the patients (42%) in the ISARIC database were included. Most patients (90.4%) were from Europe and Central Asia. The proportions of patients meeting the case definitions were 56.8% (WHO), 74.4% (UKHSA), 81.6% (ECDC) and 82.3% (CDC). For each case definition, patients at the extremes of age distribution met the criteria less frequently than those aged 30 to 70 years; geographical and time variations were also observed. Estimated CFRs were similar for the patients who met the case definitions. However, when more patients did not meet the case definition, the CFR increased. Conclusions:The performance of case definitions might be different in different regions and may change over time. Similarly concerning is the fact that older patients often did not meet case definitions, risking delayed medical care. While Joaquin Baruch and Amanda Rojek contributed equally.

Background We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. Methods The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). Results Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. Conclusions Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death.

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BackgroundAirway inflammation promotes bronchiectasis and lung injury in cystic fibrosis (CF). Amplification of inflammation underlies pulmonary exacerbations of disease. We asked whether sputum inflammatory biomarkers provide explanatory information on pulmonary exacerbations.Patients and MethodsWe collected sputum from randomly chosen stable adolescents and adults and prospectively observed time to next exacerbation, our primary outcome. We evaluated relationships between potential biomarkers of inflammation, clinical characteristics and outcomes and assessed clinical variables as potential confounders or mediators of explanatory models. We assessed associations between the markers and time to next exacerbation using proportional hazard models adjusting for confounders.ResultsWe enrolled 114 patients, collected data on clinical variables [December 8, 2014 to January 16, 2016; 46% male, mean age 28 years (SD 12), mean percent predicted forced expiratory volume in 1 s (FEV1%) 70 (SD 22)] and measured 24 inflammatory markers. Half of the inflammatory markers were plausibly associated with time to next exacerbation. Age and sex were confounders while we found that FEV1% was a mediator.Three potential biomarkers of RAGE axis inflammation were associated with time to next exacerbation while six potential neutrophil-associated biomarkers indicate associations between protease activity or reactive oxygen species with time to next exacerbation.ConclusionPulmonary exacerbation biomarkers are part of the RAGE proinflammatory axis or reflect neutrophil activity, specifically implicating protease and oxidative stress injury. Further investigations or development of novel anti-inflammatory agents should consider RAGE axis, protease and oxidant stress antagonists.Tweetable abstractSputum from 114 randomly chosen people with CF show RAGE axis inflammation, protease and oxidative stress injury are associated with time to next pulmonary exacerbation and may be targets for bench or factorial design interventional studies. (242 characters)

Background: The clinical sequelae (Long Covid) of acute Covid-19 are recognised globally, yet the risk of developing them is unknown. Methods: A living systematic review (second version). Bibliographical records from the C19 Living Map Long Covid segment (22nd February 2022), Medline, CINAHL, Global Health, WHO Covid-19 database, LitCOVID, and Google Scholar (18th November 2021). We included studies with at least 100 people at 12 weeks or more post-Covid-19 onset and with a control group without confirmed Covid-19. Risk of bias was assessed using the Newcastle-Ottawa scale. Symptoms are aligned with the Post Covid-19 Condition Core Outcome Set. We present descriptive statistics and use meta-analysis to estimate the relative risk of experiencing Long Covid. Results Twenty-eight studies were included: 20 cohort, five case-controls, three cross-sectional. Studies reported on 242,715 people with Covid-19 (55.6% female) and 276,317 controls (55.7% female) in 16 countries. Most were of moderate quality (71%). Only two were set in low-middle-income countries and few included children (18%). The longest mean follow-up time was 419.8 (standard deviation 49.4) days post-diagnosis. The relative risk (RR) of experiencing persistent or new symptoms in cases compared with controls was 1.53 (95% CI: 1.50 to 1.56). The core outcomes with the highest increased risk were cardiovascular (RR 2.53 95% CI: 2.16 to 2.96), cognitive (RR 1.99; 95% CI: 1.82 to 2.17), and physical functioning (RR 1.85; 95% CI: 1.75 to 1.96). Conclusion: SARS-CoV-2 infection is associated with a higher risk of new or persistent symptoms when compared with controls that can last over a year following acute Covid-19. There is still a lack of robust studies set in lower resourced settings and current studies have high heterogeneity and potential misclassifications of cases and controls. Future research should explore the role of vaccination and different variants on the risk of developing Long Covid.

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.

Background Previous meta-analyses have indicated that aspirin could affect breast cancer outcomes, particularly when taken post-diagnostically. However, several recent studies appear to show little to no association between aspirin use and breast cancer mortality, all-cause mortality, or recurrence. Aims This study aims to conduct an updated systematic review and meta-analysis on the associations of pre-diagnostic and post-diagnostic aspirin use with the aforementioned breast cancer outcomes. It also looks, through subgroup analyses and meta-regressions, at a range of variables that could explain the associations between aspirin use and breast cancer outcomes. Results In total, 24 papers and 149 860 patients with breast cancer were included. Pre-diagnostic aspirin use was not associated with breast-cancer-specific mortality (HR 0.98, 95% CI, 0.80-1.20, P = .84) or recurrence (HR 0.94, 95% CI, 0.88-1.02, P = .13). Pre-diagnostic aspirin was associated with non-significantly higher all-cause mortality (HR 1.27, 95% CI, 0.95-1.72, P = .11). Post-diagnostic aspirin was not significantly associated with all-cause mortality (HR 0.87, 95% CI, 0.71-1.07, P = .18) or recurrence (HR 0.89, 95% CI, 0.67-1.16, P = .38). Post-diagnostic aspirin use was significantly associated with lower breast-cancer-specific mortality (HR 0.79, 95% CI, 0.64-0.98, P = .032). Conclusions The only significant association of aspirin with breast cancer outcomes is lower breast-cancer-specific mortality in patients who used aspirin post-diagnostically. However, factors such as selection bias and high inter-study heterogeneity mean that this result should not be treated as conclusive, and more substantial evidence such as that provided by RCTs is needed before any decisions on new clinical uses for aspirin should be made.