Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and 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. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Differences in access to Emergency Paediatric Intensive Care and care during Transport (DEPICT): study protocol for a mixed methods study

Introduction Following centralisation of UK paediatric intensive care, specialist retrieval teams were established who travel to general hospitals to stabilise and transport sick children to regional paediatric intensive care units (PICUs). There is national variation among these PICU retrieval teams (PICRTs) in terms of how quickly they reach the patient’s bedside and in the care provided during transport. The impact of these variations on clinical outcomes and the experience of stakeholders (patients, families and healthcare staff) is however unknown. The primary objective of this study is to address this evidence gap. Methods and analysis This mixed-methods project involves the following: (1) retrospective analysis of linked data from routine clinical audits (2014–2016) to assess the impact of service variations on 30-day mortality and other secondary clinical outcomes; (2) a prospective questionnaire study conducted at 24 PICUs and 9 associated PICRTs in England and Wales over a 12-month period in 2018 to collect experience data from parents of transported children as well as qualitative analysis of in-depth interviews with a purposive sample of patients, parents and staff to assess the impact of service variations on patient/family experience; (3) health economic evaluation analysing transport service costs (and other associated costs) against lives saved and longer term measurements of quality of life at 12 months in transported children and (4) mathematical modelling evaluating the costs and potential impact of different service configurations. A final work stream involves a series of stakeholder workshops to synthesise study findings and generate recommendations. Ethics and dissemination The study has been reviewed and approved by the Health Research Authority, ref: 2 18 569. Study results will be actively disseminated through peer-reviewed journals, conference presentations, social media, print and broadcast media, the internet and stakeholder workshops

TEX-CT Vs PET-CT as a prognostic tool in Non-Small Cell Lung Carcinoma

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Paediatric acute respiratory distress syndrome incidence and epidemiology (PARDIE): an international, observational study

BackgroundPaediatric acute respiratory distress syndrome (PARDS) is associated with high mortality in children, but until recently no paediatric-specific diagnostic criteria existed. The Pediatric Acute Lung Injury Consensus Conference (PALICC) definition was developed to overcome limitations of the Berlin definition, which was designed and validated for adults. We aimed to determine the incidence and outcomes of children who meet the PALICC definition of PARDS.MethodsIn this international, prospective, cross-sectional, observational study, 145 paediatric intensive care units (PICUs) from 27 countries were recruited, and over a continuous 5 day period across 10 weeks all patients were screened for enrolment. Patients were included if they had a new diagnosis of PARDS that met PALICC criteria during the study week. Exclusion criteria included meeting PARDS criteria more than 24 h before screening, cyanotic heart disease, active perinatal lung disease, and preparation or recovery from a cardiac intervention. Data were collected on the PICU characteristics, patient demographics, and elements of PARDS (ie, PARDS risk factors, hypoxaemia severity metrics, type of ventilation), comorbidities, chest imaging, arterial blood gas measurements, and pulse oximetry. The primary outcome was PICU mortality. Secondary outcomes included 90 day mortality, duration of invasive mechanical and non-invasive ventilation, and cause of death.FindingsBetween May 9, 2016, and June 16, 2017, during the 10 study weeks, 23 280 patients were admitted to participating PICUs, of whom 744 (3·2%) were identified as having PARDS. 95% (708 of 744) of patients had complete data for analysis, with 17% (121 of 708; 95% CI 14-20) mortality, whereas only 32% (230 of 708) of patients met Berlin criteria with 27% (61 of 230) mortality. Based on hypoxaemia severity at PARDS diagnosis, mortality was similar among those who were non-invasively ventilated and with mild or moderate PARDS (10-15%), but higher for those with severe PARDS (33% [54 of 165; 95% CI 26-41]). 50% (80 of 160) of non-invasively ventilated patients with PARDS were subsequently intubated, with 25% (20 of 80; 95% CI 16-36) mortality. By use of PALICC PARDS definition, severity of PARDS at 6 h after initial diagnosis (area under the curve [AUC] 0·69, 95% CI 0·62-0·76) discriminates PICU mortality better than severity at PARDS diagnosis (AUC 0·64, 0·58-0·71), and outperforms Berlin severity groups at 6 h (0·64, 0·58-0·70; p=0·01).InterpretationThe PALICC definition identified more children as having PARDS than the Berlin definition, and PALICC PARDS severity groupings improved the stratification of mortality risk, particularly when applied 6 h after PARDS diagnosis. The PALICC PARDS framework should be considered for use in future epidemiological and therapeutic research among children with PARDS.FundingUniversity of Southern California Clinical Translational Science Institute, Sainte Justine Children's Hospital, University of Montreal, Canada, Réseau en Santé Respiratoire du Fonds de Recherche Quebec-Santé, and Children's Hospital Los Angeles, Department of Anesthesiology and Critical Care Medicine

Azithromycin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Background: Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatory actions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19. Methods: In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospital with COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients were randomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once per day by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatment groups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment and were twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants and local study staff were not masked to the allocated treatment, but all others involved in the trial were masked to the outcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treat population. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936. Findings: Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) were eligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was 65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomly allocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall, 561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days (rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median 10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days (rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, no significant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilation or death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24). Interpretation: In patients admitted to hospital with COVID-19, azithromycin did not improve survival or other prespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restricted to patients in whom there is a clear antimicrobial indication. Funding: UK Research and Innovation (Medical Research Council) and National Institute of Health Research.</p

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management