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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 2021More...
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: Suggested Citation