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      Factors associated with COVID-19-related death using OpenSAFELY.

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          Abstract

          Coronavirus disease 2019 (COVID-19) has rapidly affected mortality worldwide1. There is unprecedented urgency to understand who is most at risk of severe outcomes, and this requires new approaches for the timely analysis of large datasets. Working on behalf of NHS England, we created OpenSAFELY-a secure health analytics platform that covers 40% of all patients in England and holds patient data within the existing data centre of a major vendor of primary care electronic health records. Here we used OpenSAFELY to examine factors associated with COVID-19-related death. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19-related deaths. COVID-19-related death was associated with: being male (hazard ratio (HR) 1.59 (95% confidence interval 1.53-1.65)); greater age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared with people of white ethnicity, Black and South Asian people were at higher risk, even after adjustment for other factors (HR 1.48 (1.29-1.69) and 1.45 (1.32-1.58), respectively). We have quantified a range of clinical factors associated with COVID-19-related death in one of the largest cohort studies on this topic so far. More patient records are rapidly being added to OpenSAFELY, we will update and extend our results regularly.

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          Clinical Characteristics of Coronavirus Disease 2019 in China

          Wei-jie Guan, Zheng-yi Ni, Yu Hu … (2020)
          Abstract Background Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. Methods We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. Results The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. Conclusions During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.)
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            A new equation to estimate glomerular filtration rate.

            Andrew Levey, Lesley Stevens, Christopher H Schmid … (2009)
            Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
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              High prevalence of obesity in severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) requiring invasive mechanical ventilation

              Arthur Simonnet, Mikael Chetboun, Julien Poissy … (2020)
              Abstract Objective The Covid‐19 pandemic is rapidly spreading worldwide, notably in Europe and North America, where obesity is highly prevalent. The relation between obesity and severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) has not been fully documented. Methods In this retrospective cohort study we analyzed the relationship between clinical characteristics, including body mass index (BMI), and the requirement for invasive mechanical ventilation (IMV) in 124 consecutive patients admitted in intensive care for SARS‐CoV‐2, in a single French center. Results Obesity (BMI >30 kg/m2) and severe obesity (BMI >35 kg/m2) were present in 47.6% and 28.2% of cases, respectively. Overall, 85 patients (68.6%) required IMV. The proportion of patients who required IMV increased with BMI categories (p 35 kg/m2 (85.7%). In multivariate logistic regression, the need for IMV was significantly associated with male sex (p 35 kg/m2 vs patients with BMI <25 kg/m2 was 7.36 (1.63‐33.14; p=0.02) Conclusion The present study showed a high frequency of obesity among patients admitted in intensive care for SARS‐CoV‐2. Disease severity increased with BMI. Obesity is a risk factor for SARS‐CoV‐2 severity requiring increased attention to preventive measures in susceptible individuals.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): Nature
                Title: Nature
                Publisher: Springer Science and Business Media LLC
                ISSN (Electronic): 1476-4687
                ISSN (Print): 0028-0836
                Publication date (Electronic): August 2020
                Volume: 584
                Issue: 7821
                Affiliations
                [1 ] London School of Hygiene and Tropical Medicine, Faculty of Epidemiology and Population Health, London, UK.
                [2 ] The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
                [3 ] TPP, Horsforth, UK.
                [4 ] NIHR Health Protection Research Unit in Immunisation, London, UK.
                [5 ] Intensive Care National Audit and Research Centre (ICNARC), London, UK.
                [6 ] The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK. ben.goldacre@phc.ox.ac.uk.
                Article
                Publisher Item ID: 10.1038/s41586-020-2521-4
                DOI: 10.1038/s41586-020-2521-4
                PubMed ID: 32640463
                SO-VID: fa16f22c-40da-49b5-8095-9ff26db5d222
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