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      Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study

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          Abstract

          Objective

          To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults.

          Design

          Population based cohort study.

          Setting and participants

          QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020.

          Main outcome measures

          The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period.

          Results

          4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R 2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19.

          Conclusion

          The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.

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          Most cited references34

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          Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

          Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
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            OpenSAFELY: factors associated with COVID-19 death in 17 million patients

            COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. 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 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with 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 risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
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              A Proportional Hazards Model for the Subdistribution of a Competing Risk

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                Author and article information

                Contributors
                Role: clinical research fellow
                Role: professor of medical statistics in primary care
                Role: professor of biostatistics and epidemiology
                Role: associate professor of biostatistics
                Role: associate professor of medical statistics
                Role: professor of surgery and data science
                Role: director
                Role: professor of clinical epidemiology
                Role: professor of emerging infectious diseases
                Role: clinical adviser
                Role: acting chief medical officer
                Role: professor of primary careRole: diabetes and vascular medicine
                Role: professor of mathematics
                Role: director
                Role: national clinical director for diabetes and obesity
                Role: clinical professor of public health
                Role: clinical reader in primary care research and development
                Role: professor in child health and outbreak medicine
                Role: professor of public health medicine
                Role: national clinical director for cancer
                Role: professor of diet and population health
                Role: consultant occupational physician
                Role: professor of clinical epidemiology and general practice
                Journal
                BMJ
                BMJ
                BMJ-UK
                bmj
                The BMJ
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2020
                21 October 2020
                : 371
                : m3731
                Affiliations
                [1 ]Nuffield Department of Primary Care Health Sciences, Radcliffe Observatory Quarter, Oxford OX2 6GG, UK
                [2 ]Division of Primary Care, School of Medicine, University of Nottingham, Nottingham, UK
                [3 ]Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
                [4 ]Usher Institute, University of Edinburgh, Edinburgh, UK
                [5 ]UCL Institute of Epidemiology and Health Care, University College London, London, UK
                [6 ]UCL Institute for Health Informatics, University College London, London, UK
                [7 ]Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
                [8 ]Office of the Chief Medical Officer, Department of Health and Social Care, London, UK
                [9 ]NHS Digital, Leeds, UK
                [10 ]Diabetes Research Centre, University of Leicester, Leicester, UK
                [11 ]Winton Centre for Risk and Evidence Communication, Faculty of Mathematics, University of Cambridge, Cambridge, UK
                [12 ]NHS England, London, UK
                [13 ]Swansea University, Swansea, UK
                [14 ]Centre for Primary Care and Public Health, Queen Mary University of London, London, UK
                [15 ]Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
                [16 ]Centre for Public Health, Queen’s University Belfast, Belfast, UK
                [17 ]Association of Local Authority Medical Advisors, London, UK
                [18 ]Imperial College London, London, UK
                Author notes
                Correspondence to: J Hippisley-Cox julia.hippisley-cox@ 123456phc.ox.ac.uk (or @juliahcox on Twitter)
                Author information
                https://orcid.org/0000-0002-2479-7283
                Article
                clia060842
                10.1136/bmj.m3731
                7574532
                33082154
                5356e519-4c37-4639-b999-440f9c7e9990
                © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 September 2020
                Categories
                Research
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                Medicine
                Medicine

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