Genetic Haemochromatosis is a common inherited condition which causes a build-up of iron in the body, known as iron overload. If untreated, individuals with advanced disease may present with life threatening complications including liver disease, liver cancer, diabetes, and heart disease. The main goal of treatment is to avoid iron overload and to remove excess iron from the body stores before the development of complications. Unfortunately, while it is thought that approximately 1.2 million people in the UK are affected by the genetic defect for hereditary haemochromatosis, fewer than 20,000 are diagnosed with the disease, with the majority of individuals being symptomatic for up to ten years before diagnosis. The low rate of diagnosis of hemochromatosis in the early stages of the disease is partly because not all individuals will present with symptoms, symptoms of the disease are relatively non-specific and are common presentations in clinical practice, but there is also poor awareness of the condition among health care professionals.
To aid earlier diagnosis of genetic haemochromatosis in primary care, the proposed research aims to use electronic health records to extensively investigate the interaction that patients have with health services many years before their diagnosis of haemochromatosis, with a view to developing a predictive tool that could aid prompt recognition and early diagnosis of patients with the disease.
Background:
Genetic haemochromatosis (GH) leads to a build-up of iron in the body and increases the risk of liver cancer, liver disease, diabetes, heart disease, other health complications and death. Earlier identification and treatment of GH can reduce the risk of these complications, but current evidence suggests that the majority of individuals with GH remain undiagnosed. In current practice, there are no predictive algorithms to aid early identification of individuals in primary care who could be prioritised for further clinical assessment for GH.
Aim: To improve the identification and understanding of genetic haemochromatosis in the UK General population.
Study population: Patients aged 18 years and over, with documented diagnosis of haemochromatosis. Each patient with haemochromatosis will be propensity-score matched with up to five patients without haemochromatosis or any other secondary causes of haemochromatosis.
Setting and data sources: General practices in the UK providing data to CPRD GOLD and Aurum databases. Hospital Episode Statistics (HES) admission data will be used to determine hospitalisations, diagnosis of haematochromatosis as well as other outcome conditions and comorbidities.
Study design: Cohort study design
Methods:
The incidence rate of genetic haemochromatosis between 2000 and 2021 will be determined, and then Cox Proportional Hazards models will be used to estimate the risk for clinical outcomes in individuals with and without genetic haemochromatosis.
Using risk factors (candidate predictors) identified from existing literature and stakeholder engagement sessions, supervised machine learning approach will be used to assess and rank candidate predictors for genetic haemochromatosis based on their ‘importance’. Competing-risk model(s) will be used to develop and validate a case-identification tool for genetic haemochromatosis.
Intended public health benefits:
To improve understanding and provide contemporary evidence on the incidence of genetic haemochromatosis in primary care, adverse health outcomes associated with the condition, and improve case-identification of haemochromatosis in primary care.
Primary outcome: Clinical diagnoses of genetic haemochromatosis
Secondary outcomes:
• Morbidity outcomes – endocrine disorders (diabetes mellitus, hypothyroidism), impotence, osteoarthritis, osteoporosis, rheumatoid arthritis, atrial fibrillation, liver disease, neurological disorders (movement disorders such as Parkinson’s disease), bacterial infections including pneumonia, and cardiovascular disease.
• Mortality outcomes – cardiovascular disease related mortality and all-cause mortality
Barbara Iyen - Chief Investigator - University of Nottingham
Barbara Iyen - Corresponding Applicant - University of Nottingham
Kafayat Adeoye - Collaborator - University of Nottingham
Matthew Grainge - Collaborator - University of Nottingham
Nadeem Qureshi - Collaborator - University of Nottingham
Neil McClements - Collaborator - Haemochromatosis UK
Ralph Kwame Akyea - Collaborator - University of Nottingham
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation