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

Abstract

Objectives: To derive and validate risk prediction algorithms to estimate the risk of covid-19 related mortality and hospital admission in UK adults after one or two doses of covid-19 vaccination.

Design: Prospective, population based cohort study using the QResearch database linked to data on covid-19 vaccination, SARS-CoV-2 results, hospital admissions, systemic anticancer treatment, radiotherapy, and the national death and cancer registries.

Settings: Adults aged 19-100 years with one or two doses of covid-19 vaccination between 8 December 2020 and 15 June 2021.

Main outcome measures: Primary outcome was covid-19 related death. Secondary outcome was covid-19 related hospital admission. Outcomes were assessed from 14 days after each vaccination dose. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance was evaluated in a separate validation cohort of general practices.

Results: Of 6 952 440 vaccinated patients in the derivation cohort, 5 150 310 (74.1%) had two vaccine doses. Of 2031 covid-19 deaths and 1929 covid-19 hospital admissions, 81 deaths (4.0%) and 71 admissions (3.7%) occurred 14 days or more after the second vaccine dose. The risk algorithms included age, sex, ethnic origin, deprivation, body mass index, a range of comorbidities, and SARS-CoV-2 infection rate. Incidence of covid-19 mortality increased with age and deprivation, male sex, and Indian and Pakistani ethnic origin. Cause specific hazard ratios were highest for patients with Down's syndrome (12.7-fold increase), kidney transplantation (8.1-fold), sickle cell disease (7.7-fold), care home residency (4.1-fold), chemotherapy (4.3-fold), HIV/AIDS (3.3-fold), liver cirrhosis (3.0-fold), neurological conditions (2.6-fold), recent bone marrow transplantation or a solid organ transplantation ever (2.5-fold), dementia (2.2-fold), and Parkinson's disease (2.2-fold). Other conditions with increased risk (ranging from 1.2-fold to 2.0-fold increases) included chronic kidney disease, blood cancer, epilepsy, chronic obstructive pulmonary disease, coronary heart disease, stroke, atrial fibrillation, heart failure, thromboembolism, peripheral vascular disease, and type 2 diabetes. A similar pattern of associations was seen for covid-19 related hospital admissions. No evidence indicated that associations differed after the second dose, although absolute risks were reduced. The risk algorithm explained 74.1% (95% confidence interval 71.1% to 77.0%) of the variation in time to covid-19 death in the validation cohort. Discrimination was high, with a D statistic of 3.46 (95% confidence interval 3.19 to 3.73) and C statistic of 92.5. Performance was similar after each vaccine dose. In the top 5% of patients with the highest predicted covid-19 mortality risk, sensitivity for identifying covid-19 deaths within 70 days was 78.7%.

Conclusion: This population based risk algorithm performed well showing high levels of discrimination for identifying those patients at highest risk of covid-19 related death and hospital admission after vaccination.

Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the NIHR for the submitted work; JH-C reports grants from NIHR Biomedical Research Centre, Oxford, grants from John Fell Oxford University Press Research Fund, grants from Cancer Research UK, through the Cancer Research UK Oxford Centre, grants from the Oxford Wellcome Institutional Strategic Support Fund and other research councils, during the conduct of the study; JH-C is an unpaid director of QResearch, a not-for-profit organisation that is a partnership between the University of Oxford and EMIS Health, which supplied the QResearch database used for this work; JH-C is a founder and shareholder of ClinRisk and was its medical director until 31 May 2019 (ClinRisk produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems); JH-C is chair of the NERVTAG risk stratification subgroup and a member of SAGE covid-19 groups and the NHS group advising on prioritisation of use of monoclonal antibodies in covid-19 infection; CACC reports receiving personal fees from ClinRisk outside this work, and is a member of the NERVTAG risk stratification subgroup; KK is supported by the NIHR Applied Research Collaboration-East Midlands and the Leicester BRC and is a member of SAGE; RHK was supported by a UKRI Future Leaders Fellowship (MR/S017968/1); KD-O was supported by a grant from the Alan Turing Institute Health Programme (EP/T001569/1); AS is a member of the Scottish Government Chief Medical Officer’s covid-19 advisory group and a member of AstraZeneca’s thrombotic thrombocytopenic advisory group (both roles are unremunerated); RAL is a member of the Welsh government covid-19 technical advisory group (unrenumerated); MGS reports grants from Department of Health and Social Care NIHR UK, grants from the UK Medical Research Council, grants from Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, during the conduct of the study, and other funds from Integrum Scientific, Greensboro, NC, USA, outside the submitted work; MGS is a member of NERVTAG and attends SAGE covid-19; AH is a member of NERVTAG and the NHS group advising on prioritisation of use of monoclonal antibodies in covid-19 infection; JV is national clinical director for diabetes and obesity at NHS England and Improvement; FK is a member of the Northern Ireland Chief Medical Officer’s pandemic modelling group and strategic intelligence group; JSN-V-T is seconded to the Department of Health and Social Care, England. The views expressed in this manuscript are those of the authors and not necessarily those of Department of Health and Social Care or the UK government.

© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Fig 1

Use of QCovid3 model showing adjusted cause specific hazard ratios for covid-19 death after vaccination, mutually adjusted and adjusted for fractional polynomial terms for age, body mass index, vaccination dose, and background infection rate at time of vaccination. CKD=chronic kidney disease; HbA1c=glycated haemogoblin

Fig 2

Use of QCovid3 model showing adjusted cause specific hazard ratios for covid-19 hospital admission after vaccination, mutually adjusted and adjusted for fractional polynomial terms for age, body mass index, vaccination dose, and background infection rate at time of vaccination. CKD=chronic kidney disease; HbA1c=glycated haemogoblin

Fig 3

Calibration of the QCovid3 risk model to predict covid-19 related death after vaccination. Data source: QResearch England, 8 December 2020 to 15 June 2021, https://www.qresearch.org/

Fig 4

Calibration of the QCovid3 risk model to predict covid-19 related hospital admission after vaccination. Data source: QResearch England, 8 December 2020 to 15 June 2021, https://www.qresearch.org/