Congenital heart disease (CHD) affects ~7 in 1,000 liveborn babies and often needs surgery to correct defects that would otherwise prove fatal. There are many different forms of heart defect and in many cases patients can survive well into adulthood but can be at risk of developing a range of other medical conditions. Individuals with heart defects are all currently classified as being at high risk from COVID-19, however many will only experience mild symptoms or none at all. To investigate the relationship between existing heart defects and COVID-19 response we will use the CPRD dataset to determine the risk of COVID-19 in CHD patients. We will assess risk factors over a range of different heart defects as well as establishing which other medical conditions are associated with them. The study has implications for the clinical management of CHD patients across all age groups.
COVID-related recommendations to congenital heart disease (CHD) patients have thus far been made based on clinical consensus. COVID outcomes for different groups of CHD patients have not been accurately quantified in largescale datasets. The phenotypic complexity of CHD, and the increasingly recognised presence of significant comorbidities (for example coronary artery disease) in adults with even mild CHD, mandates a largescale analysis taking into account both the heterogeneity of CHD and the potential influence of comorbidities on COVID outcome.
We will use the CPRD Aurum database to classify CHD patients and non-CHD patients, using a schema based on OPCS and ICD codes we developed in the analysis of UK Biobank data. Age, sex and ethnicity matched non-CHD patients from the same general practice will be selected as a control group, matched 4:1 with the cases.
First, we will investigate the prevalence of comorbid conditions in CHD cases versus controls using Cox regression, accounting for confounders. We will use marginal structural models for more complex comorbidity analysis where the association may be conditional on time-dependent exposures with time-dependent covariates.
Second, we will assess COVID-19 outcomes in the CHD cohort compared to controls, adjusting for comorbidities by logistic regression. We will determine COVID positivity via SGSS linkage. The CHESS, ICNARC and ONS death registration data will enable us to determine severity of COVID-19 health outcomes with regard to the primary endpoint of mortality, and the secondary endpoint of hospital admission. We will use instrumental variable analysis to examine whether any difference in COVID outcome between cases and controls is accounted for by the presence of comorbidities.
The study will inform health policy regarding the clinical management of CHD patients during COVID-19. In addition, it will potentially highlight comorbidities of CHD that require intensified monitoring, particularly as the pandemic moves to an endemic situation.
COVID-19 hospital admission rates for congenital heart disease patients; COVID-19 death rates for congenital heart disease patients; Comorbidities associated with congenital heart disease and subtypes; The overall risk of COVID-19 and other medical complications in patients with congenital heart disease
Bernard Keavney - Chief Investigator - University of Manchester
Simon Williams - Corresponding Applicant - University of Manchester
Darren Ashcroft - Collaborator - University of Manchester
Dominic Byrne - Collaborator - University of Manchester
Farideh Jalalinajafabadi - Collaborator - University of Manchester
Matthew Carr - Collaborator - University of Manchester
Simon Frain - Collaborator - University of Manchester
jing yang - Collaborator - University of Manchester
CHESS (Hospitalisation in England Surveillance System);HES Admitted Patient Care;ICNARC (COVID-19 Intensive Care National Audit and Research Centre);ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation (index other than the most recent);SGSS (Second Generation Surveillance System);COVID-19 Linkages