Ischaemic heart disease (IHD), which includes angina and heart attacks, is one of the biggest causes of people being ill, developing heart failure (HF) or dying across the world and certain ethnic groups, such as South Asian people, are more susceptible to IHD. Treatments for IHD have improved a lot over the past 20 years meaning that more people with IHD are surviving to older age. Unfortunately, this means that the number of people with heart failure (HF) is rising. HF causes people to have poor quality of life, frequent admissions to hospital and to die earlier. Previous studies have shown that the proportion of people with HF also differs among ethnic groups. But we dont know whether this difference is simply because certain ethnic groups have more IHD, or whether IHD progresses more quickly for some ethnic groups than others, or whether other risk factors such as limited access to good treatments, explains these differences. This project will investigate (i) whether HF rates in people with IHD and without, have changed overtime for different ethnic groups, (ii) by how much the presence of IHD increases the risk of developing HF in different ethnic groups (iii) what different patient factors alongside IHD predict HF in different ethnic groups and (iv) whether ethnicity by itself leads to more HF.
In four objectives, this study aims to quantify and characterise the progression of IHD to new HF onset in different ethnic groups. Small scale cross-sectional studies have shown that the prevalence of antecedent IHD in HF populations is common but differs significantly according to ethnicity status. Aetiological evidence on the progression of IHD to HF has underrepresented minority ethnic groups and been limited by baseline approaches that ignore the accrual of IHD and other risk factors over time. Objective 1 will investigate whether HF incidence rates have changed overtime in those with and without IHD and in different ethnic groups. Objective 2 will use Cox regression to investigate the association between IHD and HF incidence, the influence of ethnicity on this association and influence of other common comorbidities. The competing event of non-HF death will also be assessed. A marginal structural cox model will next be used to quantify the influence of IHD status and risk factors that change over time on HF incidence. Objective 3 will investigate whether independent predictors of HF incidence differ according to ethnicity status and Objective 4 will quantify the independent association between ethnicity and HF incidence using Cox regression models stratified by IHD status.
Incident heart failure
HF incidence will be identified using a combination of Read and ICD-10 codes in the CPRD, HES and Office of National Statistics (ONS) data files. The CPRD HF code set has been used previously (protocol 12_162A) and was based on Read code CG58 and daughter codes. Additional codes were added following a structured search using the CPRD medical code dictionary browser for the clinical terms ventricular, cardiac or heart in combination with failure. All but one process code (HF confirmed) was eliminated from this latter search as they represented ongoing care or symptoms in a prevalent cohort rather than the index date of HF. The code set was validated by HF specialists and against previous literature32,33 (see appendix 1). The ICD-10 HF code set will be based on I50 and its sub codes (see appendix 1). Date of HF incidence will be defined by the first event in any of the files. In the HES and ONS files, an ICD-10 code for HF in any position will be used. HF incidence indicated at death will be identified by the ONS linked data file. The date of HF incidence for these subjects will be defined by the death date. Given that there are a number of different entry types within the CPRD that indicate a death event and each has an associated date, there may be multiple records and dates for any one patient. The study HF incidence date (date of death) will be derived using a CPRD verified algorithm which takes the earliest of the patient transfer out date (with reason death), first statement of death Read code or date of death/record added in the death administration area of the CPRD. Where death dates occur in all 3 linked records the earliest will be used.
Competing outcome
Death by any cause other than HF will be a competing event for the development of HF. This will be defined as death by any cause recorded in the patients CPRD or linked HES or ONS records that was not identified as a HF incidence event in the linked ONS record that supplies cause of death. Again, the study date of competing event (non-HF death) will be derived using a CPRD verified algorithm which takes the earliest of the patient transfer out date (with reason death), first statement of death Read code or date of death/record added in the death administration area of the CPRD. Where death dates occur in all 3 linked records the earliest will be used.
Claire Lawson - Chief Investigator - University of Leicester
Claire Lawson - Corresponding Applicant - University of Leicester
Francesco Zaccardi - Collaborator - University of Leicester
Gabriele Messina - Collaborator - Sienna University
Gerry McCann - Collaborator - University of Leicester
Kamlesh Khunti - Collaborator - University of Leicester
Laura Gray - Collaborator - University of Leicester
Melanie Davies - Collaborator - University of Leicester
Umesh T Kadam - Collaborator - Keele University
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation