TY - JOUR
AU - Hippisley-Cox, Julia
AU - Khunti, Kamlesh
AU - Sheikh, Aziz
AU - Nguyen-Van-Tam, Jonathan S
AU - Coupland, Carol A.C.
PY - 2023
DA - June
TI - Risk prediction of covid-19 related death or hospital admission in adults testing positive for SARS-CoV-2 infection during the omicron wave in England (QCOVID4): cohort study
JO - The BMJ
VL - 381
IS - 8388
DO - https://doi.org/10.1136/bmj-2022-072976
AB - Objectives To derive and validate risk prediction algorithms (QCOVID4) to estimate the risk of covid-19 related death and hospital admission in people with a positive SARS-CoV-2 test result during the period when the omicron variant of the virus was predominant in England, and to evaluate performance compared with a high risk cohort from NHS Digital.Design Cohort study.Setting QResearch database linked to English national data on covid-19 vaccinations, SARS-CoV-2 test results, hospital admissions, and cancer and mortality data, 11 December 2021 to 31 March 2022, with follow-up to 30 June 2022.Participants 1.3 million adults in the derivation cohort and 0.15 million adults in the validation cohort, aged 18-100 years, with a positive test result for SARS-CoV-2 infection.Main outcome measures Primary outcome was covid-19 related death and secondary outcome was hospital admission for covid-19. Risk equations with predictor variables were derived from models fitted in the derivation cohort. Performance was evaluated in a separate validation cohort.Results Of 1 297 922 people with a positive test result for SARS-CoV-2 infection in the derivation cohort, 18 756 (1.5 had a covid-19 related hospital admission and 3878 (0.3 had a covid-19 related death during follow-up. The final QCOVID4 models included age, deprivation score and a range of health and sociodemographic factors, number of covid-19 vaccinations, and previous SARS-CoV-2 infection. The risk of death related to covid-19 was lower among those who had received a covid-19 vaccine, with evidence of a dose-response relation (422. Previous SARS-CoV-2 infection was associated with a reduction in the risk of covid-19 related death (49. The QCOVID4 algorithm for covid-19 explained 76.0953.98.2 of the variation in time to covid-19 related death in men with a D statistic of 3.65 (3.43 to 3.86) and Harrell’s C statistic of 0.970 (0.962 to 0.979). Results were similar for women. QCOVID4 was well calibrated. QCOVID4 was substantially more efficient than the NHS Digital algorithm for correctly identifying patients at high risk of covid-19 related death. Of the 461 covid-19 related deaths in the validation cohort, 333 (72.2 were in the QCOVID4 high risk group and 95 (20.6 in the NHS Digital high risk group.Conclusion The QCOVID4 risk algorithm, modelled from data during the period when the omicron variant of the SARS-CoV-2 virus was predominant in England, now includes vaccination dose and previous SARS-CoV-2 infection, and predicted covid-19 related death among people with a positive test result. QCOVID4 more accurately identified individuals at the highest levels of absolute risk for targeted interventions than the approach adopted by NHS Digital. QCOVID4 performed well and could be used for targeting treatments for covid-19 disease.To guarantee the confidentiality of personal and health information, only the authors have had access to the data during the study in accordance with the relevant licence agreements. Access to the QResearch data are according to the information on the QResearch website (www.qresearch.org). The full model, model coefficients, functional form, and cumulative incidence function are published on the www.qcovid.org website (https://bmj2022.qcovid.org).
PB - BMJ Publishing Group
UR - https://www.research.ed.ac.uk/en/publications/99488ee6-e34b-4768-ad9e-a4af3b46dd6a
KW - Coronavirus (COVID-19)
KW - Hospital care
ER