TY - JOUR
T1 - Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK
AU - Lyons, Jane
AU - Nafilyan, Vahé
AU - Akbari, Ashley
AU - Davies, Gareth
AU - Griffiths, Rowena
AU - Harrison, Ewen M
AU - Hippisley-Cox, Julia
AU - Hollinghurst, Joe
AU - Khunti, Kamlesh
AU - North, Laura
AU - Sheikh, Aziz
AU - Torabi, Fatemeh
AU - Lyons, Ronan A
N1 - Funding Information:
This study makes use of anonymised data held in the SAIL Databank. This work uses data provided by patients and collected by the NHS as part of their care and support and the Understanding Patient Data initiative. We would also like to acknowledge all data providers who make anonymised data available for research. We wish to acknowledge the collaborative partnership that enabled acquisition and access to the de-identified data, and sharing of necessary methodological documentation and scripts which led to this output. This is a collaboration between colleagues at University of Oxford, University of Edinburgh, University of Nottingham, Office for National Statistics, London School of Hygiene and Tropical Medicine, University College London, Office of the Chief Medical Officer, Department of Health and Social Care, NHS Digital, University of Leicester, University of Cambridge, NHS England, Queen Mary University of London, University of Liverpool, Queen’s University Belfast, Association of Local Authority Medical Advisors, Imperial College London, and Swansea University Health Data Research UK. Swansea University Health Data Research UK team is under the direction of the Welsh Government Technical Advisory Cell (TAC) and includes the following groups and organizations: the SAIL Databank, Administrative Data Research (ADR) Wales, Digital Health and Care Wales (DHCW), Public Health Wales, NHS Shared Services Partnership (NWSSP) and the Welsh Ambulance Service Trust (WAST). All research conducted has been completed under the permission and approval of the SAIL independent Information Governance Review Panel (IGRP) project number 0911. 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).
Funding Information:
AS is a member of the Scottish Government’s COVID-19 Chief Medical Officer’s Advisory Group and its Standing Committee on Pandemics; he is also a member of NERVTAG’s Risk Stratification Subgroup. KK is member of NERVTAG subgroup and member of the Scientific Advisory Group for Emergencies (SAGE). JHC reports grants from National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, grants from John Fell Oxford University Press Research Fund, grants from Cancer Research UK (CR-UK) grant number C5255/A18085, through the Cancer Research UK Oxford Centre, grants from the Oxford Wellcome Institutional Strategic Support Fund (204826/Z/16/Z) and other research councils, during the conduct of the study. JHC is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health who supply the QResearch database used for this work. JHC is a founder and shareholder of ClinRisk ltd and was its medical director until 31st May 2019. ClinRisk Ltd produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems. JHC is chair of the NERVTAG risk stratification subgroup and a member of SAGE COVID-19 groups and the NHS group advising on prioritisation of use of monoclonal antibodies in COVID-19 infection. RAL is a member of the Welsh Government COVID-19 Technical Advisory Group.
Funding Information:
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 anonymized 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.
Publisher Copyright:
© The Authors.
PY - 2022/2/16
Y1 - 2022/2/16
N2 - 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 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.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.
AB - 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 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.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.
KW - COVID-19 outcomes
KW - Population data-linkage
KW - QCOVID algorithm
KW - Risk prediction models
KW - SAIL Databank
U2 - 10.23889/ijpds.v5i4.1697
DO - 10.23889/ijpds.v5i4.1697
M3 - Article
C2 - 35310465
SN - 2399-4908
VL - 5
SP - 1697
JO - International Journal of Population Data Science
JF - International Journal of Population Data Science
IS - 4
M1 - 13
ER -