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Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK.
Lyons, Jane; Nafilyan, Vahé; Akbari, Ashley; Davies, Gareth; Griffiths, Rowena; Harrison, Ewen M; Hippisley-Cox, Julia; Hollinghurst, Joe; Khunti, Kamlesh; North, Laura; Sheikh, Aziz; Torabi, Fatemeh; Lyons, Ronan A.
  • Lyons J; Population Data Science, Health Data Research UK, Swansea University Medical School, Swansea, SA2 8PP.
  • Nafilyan V; Health Analysis and Life Events Division, Office for National Statistics, NP10 8XG.
  • Akbari A; Population Data Science, Health Data Research UK, Swansea University Medical School, Swansea, SA2 8PP.
  • Davies G; Population Data Science, Health Data Research UK, Swansea University Medical School, Swansea, SA2 8PP.
  • Griffiths R; Population Data Science, Health Data Research UK, Swansea University Medical School, Swansea, SA2 8PP.
  • Harrison EM; Centre for Medical Informatics, Usher Institute, University of Edinburgh, EH16 4SA.
  • Hippisley-Cox J; Nuffield Dept of Primary Care Health Sciences, University of Oxford, OX2 6GG.
  • Hollinghurst J; Population Data Science, Health Data Research UK, Swansea University Medical School, Swansea, SA2 8PP.
  • Khunti K; Diabetes Research Centre, University of Leicester, Leicester LE5 4PW.
  • North L; Population Data Science, Health Data Research UK, Swansea University Medical School, Swansea, SA2 8PP.
  • Sheikh A; Usher Institute and Health Data Research UK BREATHE Hub, University of Edinburgh, Edinburgh EH8 9AG.
  • Torabi F; Population Data Science, Health Data Research UK, Swansea University Medical School, Swansea, SA2 8PP.
  • Lyons RA; Population Data Science, Health Data Research UK, Swansea University Medical School, Swansea, SA2 8PP.
Int J Popul Data Sci ; 5(4): 1697, 2020.
Article in English | MEDLINE | ID: covidwho-1754159
ABSTRACT

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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Observational study / Prognostic study Topics: Vaccines Limits: Adult / Aged / Female / Humans / Male / Middle aged / Young adult Country/Region as subject: Europa Language: English Journal: Int J Popul Data Sci Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Observational study / Prognostic study Topics: Vaccines Limits: Adult / Aged / Female / Humans / Male / Middle aged / Young adult Country/Region as subject: Europa Language: English Journal: Int J Popul Data Sci Year: 2020 Document Type: Article