TY - JOUR
T1 - An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19
T2 - An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK
AU - Lyons, Jane
AU - Nafilyan, Vahé
AU - Akbari, Ashley
AU - Bedston, Stuart
AU - Harrison, Ewen
AU - Hayward, Andrew
AU - Hippisley-Cox, Julia
AU - Kee, Frank
AU - Khunti, Kamlesh
AU - Rahman, Shamim
AU - Sheikh, Aziz
AU - Torabi, Fatemeh
AU - Lyons, Ronan A.
N1 - Publisher 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.
PY - 2023/5
Y1 - 2023/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85159762789&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0285979
DO - 10.1371/journal.pone.0285979
M3 - Article
C2 - 37200350
AN - SCOPUS:85159762789
SN - 1932-6203
VL - 18
JO - PLoS ONE
JF - PLoS ONE
IS - 5 May
M1 - e0285979
ER -
