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Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK.

Jane Lyons, Vahé Nafilyan, Ashley Akbari Orcid Logo, Gareth Davies Orcid Logo, Rowena Griffiths, Ewen Harrison, Julia Hippisley-Cox, Joe Hollinghurst, Kamlesh Khunti, Laura North, Aziz Sheikh, Fatemeh Torabi Orcid Logo, Ronan Lyons Orcid Logo

International Journal of Population Data Science, Volume: 5, Issue: 4

Swansea University Authors: Jane Lyons, Ashley Akbari Orcid Logo, Gareth Davies Orcid Logo, Rowena Griffiths, Joe Hollinghurst, Laura North, Fatemeh Torabi Orcid Logo, Ronan Lyons Orcid Logo

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Abstract

IntroductionCOVID-19 risk prediction algorithms can be used to identify at-risk individuals from short-term serious adverse COVID-19 outcomes such as hospitalisation and death. It is important to validate these algorithms in different and diverse populations to help guide risk management decisions a...

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Published in: International Journal of Population Data Science
ISSN: 2399-4908
Published: Swansea University 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa59436
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Abstract: IntroductionCOVID-19 risk prediction algorithms can be used to identify at-risk individuals from short-term serious adverse COVID-19 outcomes such as hospitalisation and death. It is important to validate these algorithms in different and diverse populations to help guide risk management decisions and target vaccination and treatment programs to the most vulnerable individuals in society.ObjectivesTo validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK.MethodsWe conducted a retrospective cohort study using routinely collected individual-level data held in the Secure Anonymised Information Linkage (SAIL) Databank. The cohort included individuals aged between 19 and 100 years, living in Wales on 24th January 2020, registered with a SAIL-providing general practice, and followed-up to death or study end (28th July 2020). Demographic, primary and secondary healthcare, and dispensing data were used to derive all the predictor variables used to develop the published QCOVID algorithm. Mortality data were used to define time to confirmed or suspected COVID-19 death. Performance metrics, including R2 values (explained variation), Brier scores, and measures of discrimination and calibration were calculated for two periods (24th January–30th April 2020 and 1st May–28th July 2020) to assess algorithm performance.Results1,956,760 individuals were included. 1,192 (0.06%) and 610 (0.03%) COVID-19 deaths occurred in the first and second time periods, respectively. The algorithms fitted the Welsh data and population well, explaining 68.8% (95% CI: 66.9-70.4) of the variation in time to death, Harrell’s C statistic: 0.929 (95% CI: 0.921-0.937) and D statistic: 3.036 (95% CI: 2.913-3.159) for males in the first period. Similar results were found for females and in the second time period for both sexes.ConclusionsThe QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population.
Keywords: COVID-19 outcomes; QCOVID algorithm; risk prediction models; SAIL Databank; populationdata-linkage
College: Swansea University Medical School
Funders: NIHR, CR-UK
Issue: 4