Abstract
IntroductionCOVID-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.ObjectivesTo validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK.MethodsWe 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.Results1,956,760 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.ConclusionsThe QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population.
Highlights
Introduction COVID19 risk prediction algorithms can be used to identify at-risk individuals from short-term serious adverse COVID-19 outcomes such as hospitalisation and death
Research has shown that increased age, being male, certain minority ethnic groups, and having preexisting conditions such as diabetes, cardiovascular disease, and obesity are associated with serious adverse COVID-19 outcomes, including hospitalisation and death [4,5,6,7,8,9]
To protect the most vulnerable, and to minimise the burden on the National Health Service (NHS) and its staff, it is important to identify those at greatest risk of serious adverse COVID-19 outcomes [10, 11]
Summary
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. COVID-19 risk prediction algorithms can be used to identify and prioritise at-risk individuals for targeting vaccination and treatments as well as to inform risk management decisions and policy as the pandemic evolves [12]. Predictive demographic, clinical, and pharmaceutical variables (Box 1) were based on the clinical vulnerability group criteria used to identify those advised to shield at the start of the pandemic, and risk factors associated with adverse outcomes for respiratory diseases [15, 16]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
