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

INTRODUCTION: COVID-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...

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Autores principales: Lyons, Jane, Nafilyan, Vahé, Akbari, Ashley, Davies, Gareth, Griffiths, Rowena, Harrison, Ewen M, Hippisley-Cox, Julia, Hollinghurst, Joe, Khunti, Kamlesh, North, Laura, Sheikh, Aziz, Torabi, Fatemeh, Lyons, Ronan A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Swansea University 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8900650/
https://www.ncbi.nlm.nih.gov/pubmed/35310465
http://dx.doi.org/10.23889/ijpds.v5i4.1697
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author Lyons, Jane
Nafilyan, Vahé
Akbari, Ashley
Davies, Gareth
Griffiths, Rowena
Harrison, Ewen M
Hippisley-Cox, Julia
Hollinghurst, Joe
Khunti, Kamlesh
North, Laura
Sheikh, Aziz
Torabi, Fatemeh
Lyons, Ronan A
author_facet Lyons, Jane
Nafilyan, Vahé
Akbari, Ashley
Davies, Gareth
Griffiths, Rowena
Harrison, Ewen M
Hippisley-Cox, Julia
Hollinghurst, Joe
Khunti, Kamlesh
North, Laura
Sheikh, Aziz
Torabi, Fatemeh
Lyons, Ronan A
author_sort Lyons, Jane
collection PubMed
description INTRODUCTION: COVID-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. OBJECTIVES: To validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK. METHODS: We 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 24(th) January 2020, registered with a SAIL-providing general practice, and followed-up to death or study end (28(th) 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 R(2) values (explained variation), Brier scores, and measures of discrimination and calibration were calculated for two periods (24(th) January–30(th) April 2020 and 1(st) May–28(th) July 2020) to assess algorithm performance. RESULTS: 1,956,760 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. CONCLUSIONS: The QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population.
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spelling pubmed-89006502022-03-17 Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK Lyons, Jane Nafilyan, Vahé Akbari, Ashley Davies, Gareth Griffiths, Rowena Harrison, Ewen M Hippisley-Cox, Julia Hollinghurst, Joe Khunti, Kamlesh North, Laura Sheikh, Aziz Torabi, Fatemeh Lyons, Ronan A Int J Popul Data Sci Population Data Science INTRODUCTION: COVID-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. OBJECTIVES: To validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK. METHODS: We 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 24(th) January 2020, registered with a SAIL-providing general practice, and followed-up to death or study end (28(th) 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 R(2) values (explained variation), Brier scores, and measures of discrimination and calibration were calculated for two periods (24(th) January–30(th) April 2020 and 1(st) May–28(th) July 2020) to assess algorithm performance. RESULTS: 1,956,760 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. CONCLUSIONS: The QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population. Swansea University 2022-02-15 /pmc/articles/PMC8900650/ /pubmed/35310465 http://dx.doi.org/10.23889/ijpds.v5i4.1697 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Population Data Science
Lyons, Jane
Nafilyan, Vahé
Akbari, Ashley
Davies, Gareth
Griffiths, Rowena
Harrison, Ewen M
Hippisley-Cox, Julia
Hollinghurst, Joe
Khunti, Kamlesh
North, Laura
Sheikh, Aziz
Torabi, Fatemeh
Lyons, Ronan A
Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK
title Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK
title_full Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK
title_fullStr Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK
title_full_unstemmed Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK
title_short Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK
title_sort validating the qcovid risk prediction algorithm for risk of mortality from covid-19 in the adult population in wales, uk
topic Population Data Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8900650/
https://www.ncbi.nlm.nih.gov/pubmed/35310465
http://dx.doi.org/10.23889/ijpds.v5i4.1697
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