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An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK

Jane Lyons, Vahé Nafilyan, Ashley Akbari Orcid Logo, Stuart Bedston, Ewen Harrison, Andrew Hayward, Julia Hippisley-Cox, Frank Kee, Kamlesh Khunti Orcid Logo, Shamim Rahman, Aziz Sheikh, Fatemeh Torabi Orcid Logo, Ronan Lyons Orcid Logo

PLOS ONE, Volume: 18, Issue: 5, Start page: e0285979

Swansea University Authors: Jane Lyons, Ashley Akbari Orcid Logo, Stuart Bedston, Fatemeh Torabi Orcid Logo, Ronan Lyons Orcid Logo

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DOI (Published version): 10.1371/journal.pone.0285979

Abstract

Introduction: At the start of the COVID-19 pandemic there was an urgent need to identify individuals at highest risk of severe outcomes, such as hospitalisation and death following infection. The QCOVID risk prediction algorithms emerged as key tools in facilitating this which were further developed...

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ISSN: 1932-6203
Published: Public Library of Science (PLoS) 2023
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The QCOVID risk prediction algorithms emerged as key tools in facilitating this which were further developed during the second wave of the COVID-19 pandemic to identify groups of people at highest risk of severe COVID-19 related outcomes following one or two doses of vaccine. Objectives: To externally validate the QCOVID3 algorithm based on primary and secondary care records for Wales, UK. Methods: We conducted an observational, prospective cohort based on electronic health care records for 1.66m vaccinated adults living in Wales on 8th December 2020, with follow-up until 15th June 2021. Follow-up started from day 14 post vaccination to allow the full effect of the vaccine. Results: The scores produced by the QCOVID3 risk algorithm showed high levels of discrimination for both COVID-19 related deaths and hospital admissions and good calibration (Harrell C statistic: ≥ 0.828). Conclusion: This validation of the updated QCOVID3 risk algorithms in the adult vaccinated Welsh population has shown that the algorithms are valid for use in the Welsh population, and applicable on a population independent of the original study, which has not been previously reported. This study provides further evidence that the QCOVID algorithms can help inform public health risk management on the ongoing surveillance and intervention to manage COVID-19 related risks.</abstract><type>Journal Article</type><journal>PLOS ONE</journal><volume>18</volume><journalNumber>5</journalNumber><paginationStart>e0285979</paginationStart><paginationEnd/><publisher>Public Library of Science (PLoS)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>1932-6203</issnElectronic><keywords>Medical risk factors, COVID 19, Vaccination and immunization, Dose prediction methods, Pandemics, Wales, Vaccines, Algorithms.</keywords><publishedDay>18</publishedDay><publishedMonth>5</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-05-18</publishedDate><doi>10.1371/journal.pone.0285979</doi><url>http://dx.doi.org/10.1371/journal.pone.0285979</url><notes/><college>COLLEGE NANME</college><department>Health Data Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>HDAT</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>This work was supported by the Con-COV team funded by the Medical Research Council (grant number: MR/V028367/1). This work was supported by Health Data Research UK, which receives its funding from HDR UK Ltd (HDR-9006) and the Medical Research Council (MR/ S027750/1). HDR UK Ltd is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust. This work was supported by the ADR Wales programme of work. The ADR Wales programme of work is aligned to the priority themes as identified in the Welsh Government’s national strategy: Prosperity for All. ADR Wales brings together data science experts at Swansea University Medical School, staff from the Wales Institute of Social and Economic Research, Data and Methods (WISERD) at Cardiff University and specialist teams within the Welsh Government to develop new evidence which supports Prosperity for All by using the SAIL Databank at Swansea University, to link and analyse anonymised data. ADR Wales is part of the Economic and Social Research Council (part of UK Research and Innovation) funded ADR UK (grant ES/S007393/1). This work was supported by the Wales COVID-19 Evidence Centre, funded by Health and Care Research Wales. The original development and validation of the QCOVID algorithms were funded by the National Institute for Health Research (NIHR) following a commission by the Chief Medical Officer for England. QResearch was supported by funds from the John Fell Oxford University Press Research Fund, grants from Cancer Research UK (grant C5255/A18085), through the Cancer Research UK Oxford Centre, and, grants from the Oxford Wellcome Institutional StrategicSupport Fund (204826/Z/16/Z), during the conduct of the study. KK is supported by the National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and the NIHR Leicester Biomedical Research Centre (BRC). 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spelling v2 63512 2023-05-19 An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK 1b74fa5125a88451c52c45bcf20e0b47 Jane Lyons Jane Lyons true false aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false c79d07eaba5c9515c0df82b372b76a41 Stuart Bedston Stuart Bedston true false f569591e1bfb0e405b8091f99fec45d3 0000-0002-5853-4625 Fatemeh Torabi Fatemeh Torabi true false 83efcf2a9dfcf8b55586999d3d152ac6 0000-0001-5225-000X Ronan Lyons Ronan Lyons true false 2023-05-19 HDAT Introduction: At the start of the COVID-19 pandemic there was an urgent need to identify individuals at highest risk of severe outcomes, such as hospitalisation and death following infection. The QCOVID risk prediction algorithms emerged as key tools in facilitating this which were further developed during the second wave of the COVID-19 pandemic to identify groups of people at highest risk of severe COVID-19 related outcomes following one or two doses of vaccine. Objectives: To externally validate the QCOVID3 algorithm based on primary and secondary care records for Wales, UK. Methods: We conducted an observational, prospective cohort based on electronic health care records for 1.66m vaccinated adults living in Wales on 8th December 2020, with follow-up until 15th June 2021. Follow-up started from day 14 post vaccination to allow the full effect of the vaccine. Results: The scores produced by the QCOVID3 risk algorithm showed high levels of discrimination for both COVID-19 related deaths and hospital admissions and good calibration (Harrell C statistic: ≥ 0.828). Conclusion: This validation of the updated QCOVID3 risk algorithms in the adult vaccinated Welsh population has shown that the algorithms are valid for use in the Welsh population, and applicable on a population independent of the original study, which has not been previously reported. This study provides further evidence that the QCOVID algorithms can help inform public health risk management on the ongoing surveillance and intervention to manage COVID-19 related risks. Journal Article PLOS ONE 18 5 e0285979 Public Library of Science (PLoS) 1932-6203 Medical risk factors, COVID 19, Vaccination and immunization, Dose prediction methods, Pandemics, Wales, Vaccines, Algorithms. 18 5 2023 2023-05-18 10.1371/journal.pone.0285979 http://dx.doi.org/10.1371/journal.pone.0285979 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University SU Library paid the OA fee (TA Institutional Deal) This work was supported by the Con-COV team funded by the Medical Research Council (grant number: MR/V028367/1). This work was supported by Health Data Research UK, which receives its funding from HDR UK Ltd (HDR-9006) and the Medical Research Council (MR/ S027750/1). HDR UK Ltd is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust. This work was supported by the ADR Wales programme of work. The ADR Wales programme of work is aligned to the priority themes as identified in the Welsh Government’s national strategy: Prosperity for All. ADR Wales brings together data science experts at Swansea University Medical School, staff from the Wales Institute of Social and Economic Research, Data and Methods (WISERD) at Cardiff University and specialist teams within the Welsh Government to develop new evidence which supports Prosperity for All by using the SAIL Databank at Swansea University, to link and analyse anonymised data. ADR Wales is part of the Economic and Social Research Council (part of UK Research and Innovation) funded ADR UK (grant ES/S007393/1). This work was supported by the Wales COVID-19 Evidence Centre, funded by Health and Care Research Wales. The original development and validation of the QCOVID algorithms were funded by the National Institute for Health Research (NIHR) following a commission by the Chief Medical Officer for England. QResearch was supported by funds from the John Fell Oxford University Press Research Fund, grants from Cancer Research UK (grant C5255/A18085), through the Cancer Research UK Oxford Centre, and, grants from the Oxford Wellcome Institutional StrategicSupport Fund (204826/Z/16/Z), during the conduct of the study. KK is supported by the National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and the NIHR Leicester Biomedical Research Centre (BRC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 2024-01-08T13:43:17.4268446 2023-05-19T16:24:15.3857900 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Jane Lyons 1 Vahé Nafilyan 2 Ashley Akbari 0000-0003-0814-0801 3 Stuart Bedston 4 Ewen Harrison 5 Andrew Hayward 6 Julia Hippisley-Cox 7 Frank Kee 8 Kamlesh Khunti 0000-0003-2343-7099 9 Shamim Rahman 10 Aziz Sheikh 11 Fatemeh Torabi 0000-0002-5853-4625 12 Ronan Lyons 0000-0001-5225-000X 13 63512__27841__91c02c27821a4733a022af3cb310540d.pdf 63512.pdf 2023-06-13T15:33:27.2805183 Output 670589 application/pdf Version of Record true Copyright: © 2023 Lyons et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. true eng http://creativecommons.org/licenses/by/4.0/
title An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK
spellingShingle An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK
Jane Lyons
Ashley Akbari
Stuart Bedston
Fatemeh Torabi
Ronan Lyons
title_short An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK
title_full An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK
title_fullStr An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK
title_full_unstemmed An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK
title_sort An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK
author_id_str_mv 1b74fa5125a88451c52c45bcf20e0b47
aa1b025ec0243f708bb5eb0a93d6fb52
c79d07eaba5c9515c0df82b372b76a41
f569591e1bfb0e405b8091f99fec45d3
83efcf2a9dfcf8b55586999d3d152ac6
author_id_fullname_str_mv 1b74fa5125a88451c52c45bcf20e0b47_***_Jane Lyons
aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari
c79d07eaba5c9515c0df82b372b76a41_***_Stuart Bedston
f569591e1bfb0e405b8091f99fec45d3_***_Fatemeh Torabi
83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan Lyons
author Jane Lyons
Ashley Akbari
Stuart Bedston
Fatemeh Torabi
Ronan Lyons
author2 Jane Lyons
Vahé Nafilyan
Ashley Akbari
Stuart Bedston
Ewen Harrison
Andrew Hayward
Julia Hippisley-Cox
Frank Kee
Kamlesh Khunti
Shamim Rahman
Aziz Sheikh
Fatemeh Torabi
Ronan Lyons
format Journal article
container_title PLOS ONE
container_volume 18
container_issue 5
container_start_page e0285979
publishDate 2023
institution Swansea University
issn 1932-6203
doi_str_mv 10.1371/journal.pone.0285979
publisher Public Library of Science (PLoS)
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 - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
url http://dx.doi.org/10.1371/journal.pone.0285979
document_store_str 1
active_str 0
description Introduction: At the start of the COVID-19 pandemic there was an urgent need to identify individuals at highest risk of severe outcomes, such as hospitalisation and death following infection. The QCOVID risk prediction algorithms emerged as key tools in facilitating this which were further developed during the second wave of the COVID-19 pandemic to identify groups of people at highest risk of severe COVID-19 related outcomes following one or two doses of vaccine. Objectives: To externally validate the QCOVID3 algorithm based on primary and secondary care records for Wales, UK. Methods: We conducted an observational, prospective cohort based on electronic health care records for 1.66m vaccinated adults living in Wales on 8th December 2020, with follow-up until 15th June 2021. Follow-up started from day 14 post vaccination to allow the full effect of the vaccine. Results: The scores produced by the QCOVID3 risk algorithm showed high levels of discrimination for both COVID-19 related deaths and hospital admissions and good calibration (Harrell C statistic: ≥ 0.828). Conclusion: This validation of the updated QCOVID3 risk algorithms in the adult vaccinated Welsh population has shown that the algorithms are valid for use in the Welsh population, and applicable on a population independent of the original study, which has not been previously reported. This study provides further evidence that the QCOVID algorithms can help inform public health risk management on the ongoing surveillance and intervention to manage COVID-19 related risks.
published_date 2023-05-18T13:43:19Z
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