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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
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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&#x2013;30th April 2020 and 1st May&#x2013;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&#x2019;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.</abstract><type>Journal Article</type><journal>International Journal of Population Data Science</journal><volume>5</volume><journalNumber>4</journalNumber><paginationStart/><paginationEnd/><publisher>Swansea University</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2399-4908</issnElectronic><keywords>COVID-19 outcomes; QCOVID algorithm; risk prediction models; SAIL Databank; populationdata-linkage</keywords><publishedDay>10</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-02-10</publishedDate><doi>10.23889/ijpds.v5i4.1697</doi><url/><notes/><college>COLLEGE NANME</college><department>Health Data Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>HDAT</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>NIHR, CR-UK</funders><projectreference/><lastEdited>2022-08-15T14:31:08.7086210</lastEdited><Created>2022-02-22T20:01:42.4528821</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Medicine</level></path><authors><author><firstname>Jane</firstname><surname>Lyons</surname><orcid/><order>1</order></author><author><firstname>Vah&#xE9;</firstname><surname>Nafilyan</surname><order>2</order></author><author><firstname>Ashley</firstname><surname>Akbari</surname><orcid>0000-0003-0814-0801</orcid><order>3</order></author><author><firstname>Gareth</firstname><surname>Davies</surname><orcid>0000-0001-9005-1618</orcid><order>4</order></author><author><firstname>Rowena</firstname><surname>Griffiths</surname><order>5</order></author><author><firstname>Ewen</firstname><surname>Harrison</surname><order>6</order></author><author><firstname>Julia</firstname><surname>Hippisley-Cox</surname><order>7</order></author><author><firstname>Joe</firstname><surname>Hollinghurst</surname><order>8</order></author><author><firstname>Kamlesh</firstname><surname>Khunti</surname><order>9</order></author><author><firstname>Laura</firstname><surname>North</surname><order>10</order></author><author><firstname>Aziz</firstname><surname>Sheikh</surname><order>11</order></author><author><firstname>Fatemeh</firstname><surname>Torabi</surname><orcid>0000-0002-5853-4625</orcid><order>12</order></author><author><firstname>Ronan</firstname><surname>Lyons</surname><orcid>0000-0001-5225-000X</orcid><order>13</order></author></authors><documents><document><filename>59436__22548__4b2f54859da943b7bd0d396d4aa4ccdb.pdf</filename><originalFilename>59436.pdf</originalFilename><uploaded>2022-03-08T15:17:51.8119366</uploaded><type>Output</type><contentLength>1589753</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>2022 &#xA9; The Authors. This work is licensed under a Creative Commons Attribution 4.0 International License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2022-08-15T14:31:08.7086210 v2 59436 2022-02-22 Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK. 1b74fa5125a88451c52c45bcf20e0b47 Jane Lyons Jane Lyons true false aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false 98490239b86cc892a382416d048cdb3c 0000-0001-9005-1618 Gareth Davies Gareth Davies true false 381464f639f98bd388c29326ca7f862c Rowena Griffiths Rowena Griffiths true false d7c51b69270b644a11b904629fe56ab0 Joe Hollinghurst Joe Hollinghurst true false a255822cf77a0184cb6922e9fbea39e9 Laura North Laura North true false f569591e1bfb0e405b8091f99fec45d3 0000-0002-5853-4625 Fatemeh Torabi Fatemeh Torabi true false 83efcf2a9dfcf8b55586999d3d152ac6 0000-0001-5225-000X Ronan Lyons Ronan Lyons true false 2022-02-22 HDAT 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. Journal Article International Journal of Population Data Science 5 4 Swansea University 2399-4908 COVID-19 outcomes; QCOVID algorithm; risk prediction models; SAIL Databank; populationdata-linkage 10 2 2022 2022-02-10 10.23889/ijpds.v5i4.1697 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University NIHR, CR-UK 2022-08-15T14:31:08.7086210 2022-02-22T20:01:42.4528821 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Jane Lyons 1 Vahé Nafilyan 2 Ashley Akbari 0000-0003-0814-0801 3 Gareth Davies 0000-0001-9005-1618 4 Rowena Griffiths 5 Ewen Harrison 6 Julia Hippisley-Cox 7 Joe Hollinghurst 8 Kamlesh Khunti 9 Laura North 10 Aziz Sheikh 11 Fatemeh Torabi 0000-0002-5853-4625 12 Ronan Lyons 0000-0001-5225-000X 13 59436__22548__4b2f54859da943b7bd0d396d4aa4ccdb.pdf 59436.pdf 2022-03-08T15:17:51.8119366 Output 1589753 application/pdf Version of Record true 2022 © The Authors. This work is licensed under a Creative Commons Attribution 4.0 International License. true eng https://creativecommons.org/licenses/by/4.0/
title Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK.
spellingShingle Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK.
Jane Lyons
Ashley Akbari
Gareth Davies
Rowena Griffiths
Joe Hollinghurst
Laura North
Fatemeh Torabi
Ronan Lyons
title_short 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_sort Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK.
author_id_str_mv 1b74fa5125a88451c52c45bcf20e0b47
aa1b025ec0243f708bb5eb0a93d6fb52
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381464f639f98bd388c29326ca7f862c
d7c51b69270b644a11b904629fe56ab0
a255822cf77a0184cb6922e9fbea39e9
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author_id_fullname_str_mv 1b74fa5125a88451c52c45bcf20e0b47_***_Jane Lyons
aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari
98490239b86cc892a382416d048cdb3c_***_Gareth Davies
381464f639f98bd388c29326ca7f862c_***_Rowena Griffiths
d7c51b69270b644a11b904629fe56ab0_***_Joe Hollinghurst
a255822cf77a0184cb6922e9fbea39e9_***_Laura North
f569591e1bfb0e405b8091f99fec45d3_***_Fatemeh Torabi
83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan Lyons
author Jane Lyons
Ashley Akbari
Gareth Davies
Rowena Griffiths
Joe Hollinghurst
Laura North
Fatemeh Torabi
Ronan Lyons
author2 Jane Lyons
Vahé Nafilyan
Ashley Akbari
Gareth Davies
Rowena Griffiths
Ewen Harrison
Julia Hippisley-Cox
Joe Hollinghurst
Kamlesh Khunti
Laura North
Aziz Sheikh
Fatemeh Torabi
Ronan Lyons
format Journal article
container_title International Journal of Population Data Science
container_volume 5
container_issue 4
publishDate 2022
institution Swansea University
issn 2399-4908
doi_str_mv 10.23889/ijpds.v5i4.1697
publisher Swansea University
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
active_str 0
description 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.
published_date 2022-02-10T04:16:44Z
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