The inherited heart conditions, long QT syndrome and bicuspid aortic valve (BAV) disease, are significant causes of ill health and premature death.
Long QT syndrome is a rare, affecting 1 in 2000-3000 individuals, heart rhythm problem, where the heart muscle takes longer than normal to recharge between beats. This results in an increased risk of an irregular heartbeat that can lead to fainting, collapse, seizures and avoidable sudden death.
Bicuspid aortic valve disease is the most common congenital (present at birth) heart defect, affecting between 1 in 50 and 1 in 200 individuals. It occurs when the normal three leaflets of the aortic valve as it leaves the heart, (the aorta is the main artery in the body), are fused resulting in only two leaflets and hence the term bicuspid aortic valve. This causes an increased risk of problems with this valve and the first part of the aorta that can cause both ill health and premature death.
These two conditions are both under-recognised and frequently associated with a long delay before diagnosis.
We plan to explore these two heart conditions in a population-based study. We will use anonymised primary care data, to better understand the clinical features that patients with these condition have and how they present to their GPs. This will give doctors and other health care workers a better understanding of what symptoms to look out for and thereby improve the diagnosis of these conditions.
Background: Both bicuspid aortic valve (BAV) and long QT syndrome (LQTS) are associated with significant morbidity and mortality and are under recognised. Bicuspid aortic valve disease affects 0.5-2% of the population and is associated with greater morbidity and mortality across the population than all other congenital heart defects combined. Long QT syndrome affects 1 in 2000-3000, and is a significant cause of avoidable sudden cardiac death.
Objectives: To explore the feasibility that primary care electronic patient records (EPR) can identify patterns as well as gaps in clinical coding for patients with under recognized inherited disorders, as exemplars of this we will look at the inherited cardiac conditions: bicuspid aortic valves and long QT syndrome.
Study Design: For each of the conditions, BAV & LQTS.
- Part 1: Exploratory analysis to identify signals using Machine-Learning (Random Forest (RF) model method).
- Part 2: Case control design to verify/test variables that have been identified in part 1.
Setting: UK General Practice
Participants: Individuals identified with LQTS and BAV diagnosed in their EPR compared with propensity scored matched controls.
Exposures: As this is a matched case-control study, identification of the exposures is the result of the study. These are the clinical features identified in primary care that can indicate such a diagnosis.
Comparator: Propensity scored matched control group.
Outputs: Those diagnosed with BAV and LQTS by Read code in the EPR.
The identification of these ‘exposures’, clinical features, could then be used to inform guidance, alert clinicians and develop diagnostic prompts for these conditions.
The recognition of phenotypic features identified in primary care that can indicate such a diagnosis. This could then be used to inform guidance, alert clinicians and develop diagnostic prompts for these conditions.
Nadeem Qureshi - Chief Investigator - University of Nottingham
William Evans - Corresponding Applicant - University of Nottingham
- Collaborator -
Nadeem Qureshi - Collaborator - University of Nottingham
Stephen Weng - Chief Investigator - University of Nottingham
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation