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<jats:title>Abstract</jats:title><jats:sec><jats:title>Introduction</jats:title><jats:p>Novel coronavirus 2019 (COVID-19) has propagated a global pandemic with significant health, economic and social costs. Emerging emergence has suggested that several factors may be associated with increased risk from severe outcomes or death from COVID-19. Clinical risk prediction tools have significant potential to generate individualised assessment of risk and may be useful for population stratification and other use cases.</jats:p></jats:sec><jats:sec><jats:title>Methods and analysis</jats:title><jats:p>We will use a prospective open cohort study of routinely collected data from 1205 general practices in England in the QResearch database. The primary outcome is COVID-19 mortality (in or out-of-hospital) defined as confirmed or suspected COVID-19 mentioned on the death certificate, or death occurring in a person with SARS-CoV-2 infection between 24<jats:sup>th</jats:sup> January and 30<jats:sup>th</jats:sup> April 2020. Our primary outcome in adults is COVID-19 mortality (including out of hospital and in hospital deaths). We will also examine COVID-19 hospitalisation in children. Time-to-event models will be developed in the training data to derive separate risk equations in adults (19-100 years) for males and females for evaluation of risk of each outcome within the 3-month follow-up period (24<jats:sup>th</jats:sup> January to 30<jats:sup>th</jats:sup> April 2020), accounting for competing risks. Predictors considered will include age, sex, ethnicity, deprivation, smoking status, alcohol intake, body mass index, pre-existing medical co-morbidities, and concurrent medication. Measures of performance (prediction errors, calibration and discrimination) will be determined in the test data for men and women separately and by ten-year age group. For children, descriptive statistics will be undertaken if there are currently too few serious events to allow development of a risk model. The final model will be externally evaluated in (a) geographically separate practices and (b) other relevant datasets as they become available.</jats:p></jats:sec><jats:sec><jats:title>Ethics and dissemination</jats:title><jats:p>The project has ethical approval and the results will be submitted for publication in a peer-reviewed journal.</jats:p></jats:sec><jats:sec><jats:title>Strengths and limitations of the study</jats:title><jats:list list-type="bullet"><jats:list-item><jats:p>The individual-level linkage of general practice, Public Health England testing, Hospital Episode Statistics and Office of National Statistics death register datasets enable a robust and accurate ascertainment of outcomes</jats:p></jats:list-item><jats:list-item><jats:p>The models will be trained and evaluated in population-representative datasets of millions of individuals</jats:p></jats:list-item><jats:list-item><jats:p>Shielding for clinically extremely vulnerable was advised and in place during the study period, therefore risk predictions influenced by the presence of some ‘shielding’ conditions may require careful consideration</jats:p></jats:list-item></jats:list></jats:sec>

Original publication

DOI

10.1101/2020.06.28.20141986

Type

Journal article

Publisher

Cold Spring Harbor Laboratory

Publication Date

29/06/2020