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An External Validation of the QCovid Risk Prediction Algorithm for Risk of Mortality from COVID-19 in Adults: National Validation Cohort Study in Scotland

21 Pages Posted: 16 Mar 2021

See all articles by Colin Simpson

Colin Simpson

University of Edinburgh - Usher Institute of Population Health Sciences and Informatics

Chris Robertson

University of Strathclyde - Department of Mathematics & Statistics

Steven Kerr

University of Edinburgh - Usher Institute of Population Health Sciences and Informatics

Ting Shi

University of Edinburgh - Centre for Global Health

Eleftheria Vasileiou

University of Edinburgh - Asthma UK Centre for Applied Research

Emily Moore

Public Health Scotland

Colin McCowan

University of St Andrews - School of Medicine

Utkarsh Agrawal

University of St. Andrews - School of Medicine

Helen Stagg

University of Edinburgh - Usher Institute of Population Health Sciences and Informatics

Annemarie Docherty

University of Edinburgh - Centre for Medical Informatics

Rachel Mulholland

University of Edinburgh - Usher Institute of Population Health Sciences and Informatics

Josephine-L.K Murray

Public Health Scotland

Lewis Ritchie

University of Aberdeen - Academic Primary Care

Jim McMenamin

Public Health Scotland

Julia Hippisley-Cox

University of Oxford - Nuffield Department of Primary Care Health Sciences

Aziz Sheikh

University of Edinburgh - Asthma UK Centre for Applied Research

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Abstract

Background: The QCovid algorithm is a risk prediction tool for COVID-19 hospitalisation and mortality that can be used to stratify patients by risk into vulnerability groups . We carried out an external validation of the QCovid algorithm in Scotland.

Methods: We established a national COVID-19 data platform using individual level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription polymerase chain reaction (RT-PCR) virology testing, hospitalisation and mortality data. We assessed the performance of the QCovid algorithm in predicting COVID-19 hospitalisation and deaths in our dataset for two time periods: 1 March, 2020 to 30 April, 2020, and 1 May, 2020 to 30 June, 2020.

Findings: Our dataset comprised 5,384,819 individuals, representing 99% of the estimated population (5,463,300) resident in Scotland in 2020. The algorithm showed excellent calibration in both time periods with close correspondence between observed and predicted risks. Harrell ’s C for deaths in males and females in the first period was 0.946 (95% CI: 0.941 - 0.951) and 0.925 (95% CI: 0.919 - 0.931) respectively. Harrell’s C for hospitalisations in males and females in the first period was 0.809 (95% CI: 0.801 - 0.817) and 0.816 (95% CI: 0.808 - 0.823) respectively.

Interpretation: The QCovid algorithm shows high levels of external validity in predicting the risk of COVID- 19 hospitalisation and death in the population of Scotland.

Funding: Medical Research Council, National Institute for Health Research Health Technology Assessment Programme, funded through the UK Research and Innovation Industrial Strategy Challenge Fund Health Data Research UK.

Declaration of Interests: Dr. Hippisley-Cox reports grants from MRC, grants from Wellcome Trrust, grants from NIHR, during the conduct of the study; other from ClinRisk Ltd, outside the submitted work. Dr. Sheikh reports grants from NIHR, grants from MRC, grants from HRR UK, during the conduct of the study. All other authors report no conflict of interest.

Ethics Approval Statement: Ethical permission for this study was granted from South East Scotland Research Ethics Committee 02 [12/SS/0201]. The Public Benefit and Privacy Panel Committee of Public Health Scotland, approved the linkage and analysis of the de-identified datasets for this project [1920-0279].

Suggested Citation

Simpson, Colin and Robertson, Chris and Kerr, Steven and Shi, Ting and Vasileiou, Eleftheria and Moore, Emily and McCowan, Colin and Agrawal, Utkarsh and Stagg, Helen and Docherty, Annemarie and Mulholland, Rachel and Murray, Josephine-L.K and Ritchie OBE FRSE, Professor Sir Lewis and McMenamin, Jim and Hippisley-Cox, Julia and Sheikh, Aziz, An External Validation of the QCovid Risk Prediction Algorithm for Risk of Mortality from COVID-19 in Adults: National Validation Cohort Study in Scotland. Available at SSRN: https://ssrn.com/abstract=3805856 or http://dx.doi.org/10.2139/ssrn.3805856

Colin Simpson (Contact Author)

University of Edinburgh - Usher Institute of Population Health Sciences and Informatics ( email )

Teviot Place
Edinburgh, EH8 9AG
United Kingdom

Chris Robertson

University of Strathclyde - Department of Mathematics & Statistics ( email )

26 Richmond Street
Glasgow G1 1XH
United Kingdom

Steven Kerr

University of Edinburgh - Usher Institute of Population Health Sciences and Informatics

Teviot Place
Edinburgh, EH8 9AG
United Kingdom

Ting Shi

University of Edinburgh - Centre for Global Health

Old College
South Bridge
Edinburgh, Scotland EH8 9JY
United Kingdom

Eleftheria Vasileiou

University of Edinburgh - Asthma UK Centre for Applied Research ( email )

Edinburgh
United Kingdom

Emily Moore

Public Health Scotland

Colin McCowan

University of St Andrews - School of Medicine

St Andrews, KY16 9AL
United Kingdom

Utkarsh Agrawal

University of St. Andrews - School of Medicine

United Kingdom

Helen Stagg

University of Edinburgh - Usher Institute of Population Health Sciences and Informatics ( email )

Teviot Place
Edinburgh, EH8 9AG
United Kingdom

Annemarie Docherty

University of Edinburgh - Centre for Medical Informatics ( email )

Rachel Mulholland

University of Edinburgh - Usher Institute of Population Health Sciences and Informatics

Teviot Place
Edinburgh, EH8 9AG
United Kingdom

Josephine-L.K Murray

Public Health Scotland ( email )

Professor Sir Lewis Ritchie OBE FRSE

University of Aberdeen - Academic Primary Care

Dunbar Street
Aberdeen, AB24 3QY
United Kingdom

Jim McMenamin

Public Health Scotland

Glasgow, Scotland
United Kingdom

Julia Hippisley-Cox

University of Oxford - Nuffield Department of Primary Care Health Sciences ( email )

Oxford
United Kingdom

Aziz Sheikh

University of Edinburgh - Asthma UK Centre for Applied Research ( email )

Edinburgh
United Kingdom

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