QCovid 4 - Predicting risk of death or hospitalisation from COVID-19 in adults testing positive for SARS-CoV-2 infection during the Omicron wave in England
Authors: Hippisley-Cox Julia , Khunti Kamlesh , Sheikh Aziz , Nguyen-Van-Tam Jonathan S , Coupland Carol AC 
Published: August 2022

Objectives

To (a) derive and validate risk prediction algorithms (QCovid4) to estimate risk of COVID-19 mortality and hospitalisation in UK adults with a SARS-CoV-2 positive test during the ‘Omicron’ pandemic wave in England and (b) evaluate performance with earlier versions of algorithms developed in previous pandemic waves and the high-risk cohort identified by NHS Digital in England.

Design

Population-based cohort study using the QResearch database linked to national data on COVID-19 vaccination, high risk patients prioritised for COVID-19 therapeutics, SARS-CoV-2 results, hospitalisation, cancer registry, systemic anticancer treatment, radiotherapy and the national death registry.

Settings and study period

1.3 million adults in the derivation cohort and 0.15 million adults in the validation cohort aged 18-100 years with a SARS-CoV-2 positive test between 11th December 2021 and 31st March 2022 with follow up to 30th June 2022.

Main outcome measures

Our primary outcome was COVID-19 death. The secondary outcome of interest was COVID-19 hospital admission. Models fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance evaluated in a separate validation cohort.

Results

Of 1,297,984 people with a SARS-CoV-2 positive test in the derivation cohort, 18,756 (1.45%) had a COVID-19 related hospital admission and 3,878 (0.3%) had a COVID-19 death during follow-up. Of the 145,404 people in the validation cohort, there were 2,124 (1.46%) COVID-19 admissions and 461 (0.3%) COVID-19 deaths.

The COVID-19 mortality rate in men increased with age and deprivation. In the QCovid4 model in men hazard ratios were highest for those with the following conditions (for 95% CI see Figure 1): kidney transplant (6.1-fold increase); Down’s syndrome (4.9-fold); radiotherapy (3.1-fold); type 1 diabetes (3.4-fold); chemotherapy grade A (3.8-fold), grade B (5.8-fold); grade C (10.9-fold); solid organ transplant ever (2.4-fold); dementia (1.62-fold); Parkinson’s disease (2.2-fold); liver cirrhosis (2.5-fold). Other conditions associated with increased COVID-19 mortality included learning disability, chronic kidney disease (stages 4 and 5), blood cancer, respiratory cancer, immunosuppressants, oral steroids, COPD, coronary heart disease, stroke, atrial fibrillation, heart failure, thromboembolism, rheumatoid/SLE, schizophrenia/bipolar disease sickle cell/HIV/SCID; type 2 diabetes. Results were similar in the model in women.

COVID-19 mortality risk was lower among those who had received COVID-19 vaccination compared with unvaccinated individuals with evidence of a dose response relationship. The reduced mortality rates associated with prior SARS-CoV-2 infection were similar in men (adjusted hazard ratio (HR) 0.51 (95% CI 0.40, 0.64)) and women (adjusted HR 0.55 (95%CI 0.45, 0.67)).

The QCOVID4 algorithm explained 76.6% (95%CI 74.4 to 78.8) of the variation in time to COVID-19 death (R2) in women. The D statistic was 3.70 (95%CI 3.48 to 3.93) and the Harrell’s C statistic was 0.965 (95%CI 0.951 to 0.978). The corresponding results for COVID-19 death in men were similar with R2 76.0% (95% 73.9 to 78.2); D statistic 3.65 (95%CI 3.43 to 3.86) and C statistic of 0.970 (95%CI 0.962 to 0.979). QCOVID4 discrimination for mortality was slightly higher than that for QCOVID1 and QCOVID2, but calibration was much improved.

Conclusion

The QCovid4 risk algorithm modelled from data during the UK’s Omicron wave now includes vaccination dose and prior SARS-CoV-2 infection and predicts COVID-19 mortality among people with a positive test. It has excellent performance and could be used for targeting COVID-19 vaccination and therapeutics. Although large disparities in risks of severe COVID-19 outcomes among ethnic minority groups were observed during the early waves of the pandemic, these are much reduced now with no increased risk of mortality by ethnic group.

What is known

The QCOVID risk assessment algorithm for predicting risk of COVID-19 death or hospital admission based on individual characteristics has been used in England to identify people at high risk of severe COVID-19 outcomes, adding an additional 1.5 million people to the national shielded patient list in England and in the UK for prioritising people for COVID-19 vaccination.

There are ethnic disparities in severe COVID-19 outcomes which were most marked in the first pandemic wave in 2020.

COVID-19 vaccinations and therapeutics (monoclonal antibodies and antivirals) are available but need to be targeted to those at highest risk of severe outcomes.

What this study adds

The QCOVID4 risk algorithm using data from the Omicron wave now includes number of vaccination doses and prior SARS-CoV-2 infection. It has excellent performance both for ranking individuals (discrimination) and predicting levels of absolute risk (calibration) and can be used for targeting COVID-19 vaccination and therapeutics as well as individualised risk assessment.

QCOVID4 more accurately identifies individuals at highest levels of absolute risk for targeted interventions than the ‘conditions-based’ approach adopted by NHS Digital based on relative risk of a list of medical conditions.

Although large disparities in risks of severe COVID-19 outcomes among ethnic minority groups were observed during the early waves of the pandemic, these are much reduced now with no increased risk of mortality by ethnic group.