Title: COVID-19-Risk Prediction Tool
Funder: Department of Health and Social Care

Abstract: Objective To derive and evaluate a risk prediction tool to estimate short term risks of adverse outcomes from COVID-19 in adults and children which can be used to risk-stratify the general population. Design Prospective population based open cohort study using routinely collected data from 1205 GP practices in England in the QResearch database (derived from EMIS electronic medical records) between 24th January and May 2020. We will use the QResearch database to develop the risk equations. Models will be developed on data from a subset of 904 practices (referred to as the training data) and evaluated in the remaining 301 practices (referred to as the test data), before the final model is fitted using the full database. The final model will be externally evaluated in (a) geographically separate practices and (b) other relevant datasets as they become available. Subjects We will study 6.4 million adults aged 19-100 years in the training data and 2 million patients in the test data. We will study 1.7m children aged 01.18 in the derivation data and 0.5 million in the validation data. We will use a general population approach rather than those who are infected, and therefore the risk prediction tool will assess the combined risk of both getting the virus, and then experiencing serious adverse outcomes. Methods 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, accounting for competing risks. Predictors considered will include age, sex, ethnicity, deprivation, region, 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 since there are currently too few serious events to allow development of a risk model. Outcome Our primary outcome in adults is COVID-19 mortality (including out of hospital and in hospital deaths) defined as confirmed or suspected COVID-19 mentioned on the death certificate (ICD-10 codes U071 & UO72), or death occurring in a patient with a SARS-COV-2 confirmed infection in the period 24th January 2020 to 27th April 2020. Our secondary outcomes in adults are a. hospital admission in a patient with a SARS-COV-2 confirmed infection. b. composite measure including either COVID-19 mortality (as defined above) OR hospital admission SARS-COV-2 confirmed infection. The risks of interest in this study refer to risks of adverse outcomes in the general population and are not restricted to individuals infected with SARS-5 COV-2. The predicted risks therefore refer to combined risks of becoming infected and then having the adverse outcome in question.

Lay abstract: Objective To derive and evaluate a risk prediction tool to estimate short term risks of adverse outcomes from COVID-19 in adults and children which can be used to risk-stratify the general population. Design Prospective population based open cohort study using routinely collected data from 1205 GP practices in England in the QResearch database (derived from EMIS electronic medical records) between 24th January and May 2020. We will use the QResearch database to develop the risk equations. Models will be developed on data from a subset of 904 practices (referred to as the training data) and evaluated in the remaining 301 practices (referred to as the test data), before the final model is fitted using the full database. The final model will be externally evaluated in (a) geographically separate practices and (b) other relevant datasets as they become available. Subjects We will study 6.4 million adults aged 19-100 years in the training data and 2 million patients in the test data. We will study 1.7m children aged 01.18 in the derivation data and 0.5 million in the validation data. We will use a general population approach rather than those who are infected, and therefore the risk prediction tool will assess the combined risk of both getting the virus, and then experiencing serious adverse outcomes. Methods 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, accounting for competing risks. Predictors considered will include age, sex, ethnicity, deprivation, region, 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 since there are currently too few serious events to allow development of a risk model. Outcome Our primary outcome in adults is COVID-19 mortality (including out of hospital and in hospital deaths) defined as confirmed or suspected COVID-19 mentioned on the death certificate (ICD-10 codes U071 & UO72), or death occurring in a patient with a SARS-COV-2 confirmed infection in the period 24th January 2020 to 27th April 2020. Our secondary outcomes in adults are a. hospital admission in a patient with a SARS-COV-2 confirmed infection. b. composite measure including either COVID-19 mortality (as defined above) OR hospital admission SARS-COV-2 confirmed infection. The risks of interest in this study refer to risks of adverse outcomes in the general population and are not restricted to individuals infected with SARS-5 COV-2. The predicted risks therefore refer to combined risks of becoming infected and then having the adverse outcome in question.

Research activity codes: 2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT;2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: INFECTION