ObjectivesTo (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. DesignPopulation-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 period1.3 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 measuresOur 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. ResultsOf 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); Downs 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); Parkinsons 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. O_FIG O_LINKSMALLFIG WIDTH=100 HEIGHT=200 SRC="FIGDIR/small/22278733v1_fig1.gif" ALT="Figure 1"> View larger version (35K) org.highwire.dtl.DTLVardef@4e93b7org.highwire.dtl.DTLVardef@c3e600org.highwire.dtl.DTLVardef@1311bd4org.highwire.dtl.DTLVardef@11a3246_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1C_FLOATNO QCOVID4 (mortality) Adjusted hazard ratios for COVID-19 death in men mutually adjusted and also adjusted for fractional polynomial terms for age and BMI C_FIG 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 Harrells 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. ConclusionThe QCovid4 risk algorithm modelled from data during the UKs 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 knownO_LIThe 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. C_LIO_LIThere are ethnic disparities in severe COVID-19 outcomes which were most marked in the first pandemic wave in 2020. C_LIO_LICOVID-19 vaccinations and therapeutics (monoclonal antibodies and antivirals) are available but need to be targeted to those at highest risk of severe outcomes. C_LI What this study addsO_LIThe 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. C_LIO_LIQCOVID4 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. C_LIO_LIAlthough 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. C_LI