An external validation of the QCovid risk prediction algorithm for risk of mortality from COVID-19 in adults: a national validation cohort study in England

Abstract

Background Public policy measures and clinical risk assessments relevant to COVID-19 need to be aided by risk prediction models that are rigorously developed and validated. We aimed to externally validate a risk prediction algorithm (QCovid) to estimate mortality outcomes from COVID-19 in adults in England. MethodsWe did a population-based cohort study using the UK Office for National Statistics Public Health Linked Data Asset, a cohort of individuals aged 19-100 years, based on the 2011 census and linked to Hospital Episode Statistics, the General Practice Extraction Service data for pandemic planning and research, and radiotherapy and systemic chemotherapy records. The primary outcome was time to COVID-19 death, defined as confirmed or suspected COVID-19 death as per death certification. Two periods were used: (1) Jan 24 to April 30, 2020, and (2) May 1 to July 28, 2020. We assessed the performance of the QCovid algorithms using measures of discrimination and calibration. Using predicted 90-day risk of COVID-19 death, we calculated r² values, Brier scores, and measures of discrimination and calibration with corresponding 95% CIs over the two time periods. Findings We included 34 897 648 adults aged 19-100 years resident in England. 26 985 (0•08%) COVID-19 deaths occurred during the first period and 13 177 (0•04%) during the second. The algorithms had good discrimination and calibration in both periods. In the first period, they explained 77•1% (95% CI 76•9-77•4) of the variation in time to death in men and 76•3% (76•0-76•6) in women. The D statistic was 3•761 (3•732-3•789) for men and 3•671 (3•640-3•702) for women and Harrell's C was 0•935 (0•933-0•937) for men and 0•945 (0•943-0•947) for women.Similar results were obtained for the second time period. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths in the first period was 65•94% for men and 71•67% for women.Interpretation The QCovid population-based risk algorithm performed well, showing high levels of discrimination for COVID-19 deaths in men and women for both time periods. QCovid has the potential to be dynamically updated as the pandemic evolves and, therefore, has potential use in guiding national policy.

Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK.

“…Similar results were found for females and in the second time period. Similar results were also found in the English validation, the D statistics was 3.761 (3.732-3.789), Harrell's C statistic was 0.935 (95% CI: 0.933-0.937) and Brier score was 0.0013 in males in the first period, with similar results found in females and in the second time period [17]. Performance metrics by age band, ethnicity and Townsend deprivation quintile can be found in the Appendices (Supplementary Tables 2-5).…”

Risk prediction of covid-19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study

“…During the first waves of the covid-19 pandemic (March 2020 to August 2020), before the introduction doi: 10.1136/bmj.n2244 | BMJ 2021;374:n2244 | the bmj of vaccines, it was essential to be able to identify people at highest risk of adverse outcomes if they were infected with SARS-CoV-2. The QCovid risk assessment tool for predicting risk of covid-19 related death or hospital admission based on individual characteristics was developed, 1 independently externally validated, 2 and found to have performed well at identifying those individuals at high risk of severe outcomes from covid-19. The tool was used in England to identify patients at high risk of severe covid-19 outcomes, adding an additional 1.5 million people to the national shielded patient list in February 2021 and, on a UK basis, prioritising them for vaccination (if they had not already been offered the vaccine on account of their age or occupation).…”

COVID-19 vaccination uptake amongst ethnic minority communities in England: a linked study exploring the drivers of differential vaccination rates

“…Health conditions were defined as comorbidities identified in the QCovid risk prediction model. 13 Health conditions included body mass index, diabetes, coronary heart disease, stroke and congestive cardiac failure, which are known to differ by ethnicity. 14–17 A full list of health conditions included can be found in the legend of Fig.…”