Each year, diseases of the heart and blood vessels such as stroke and heart attack result in millions of deaths around the world. Many individuals who end up having these diseases have a very high chance of having another event, which could be fatal. Previous studies have developed ways to help clinicians identify patients at high risk of developing these conditions in the future. However, few studies have developed methods to aid clinicians to manage people who already have these disease conditions and are at high risk of having severe complications.
Defining patients will help clinicians manage patients with more severe forms of heart/blood vessel diseases to prevent these conditions from reoccurring. Improvements in electronic patient record quality and advanced analytic methods now available, make it more efficient to develop more accurate ways of defining characteristics and understanding the underlying causes of heart/blood vessel diseases in these patients. This might eventually help to tailor and target early treatment and interventions to patients who would most likely benefit.
Using electronic patient records from general practices across the UK, this study aims to define characteristics of these patients who are most likely to have the greatest complications of these conditions.
Background: Prevention of cardiovascular disease (CVD) requires timely identification of people at increased risk to target effective interventions. With advances in diagnosis and treatment of CVD and increasing life expectancy, more people are surviving initial CVD events, and secondary prevention risk prediction models are increasingly being utilised. A refined classification of CVD by disease severity could provide a novel tool to identify individuals with increased risk of severe disease and individualise management.
Aim: To develop and validate a novel clinical severity index for CVD severity risk classification in adults with an established CVD.
Study design: Retrospective open cohort
Setting: UK General Practices
Participants: Patients aged 18 years and over, with a first diagnosis of incident non-fatal CVD.
Methods: Use a data driven cluster analysis in patients with newly diagnosed CVD to determine patient clusters based on potential risk factors for severe CVD outcomes. This will be related to retrospective data from patient records on development of severe CVD complications or outcomes. Cox and competing-risk regressions will be used to compare risk of severe CVD complications/outcomes among clusters.
Outputs: A novel risk severity tool built from data-driven methods for use in primary care, aimed at secondary prevention of CVD. The clinical decision tool would sub-stratify patients based on risk of severe CVD outcomes.
The tool will eventually help to tailor and target early treatment to patients who would benefit most from the respective effective interventions that already exist, thereby representing a first step towards personalised medicine in secondary prevent of CVD.
The health-related outcomes that are expected to be assessed are:
Mortality outcomes
Death due to cardiovascular event
Death due to other causes
Morbidity outcomes
Coronary heart disease (angina, heart attack, heart failure)
Stroke (ischaemic and haemorrhagic)
Transient ischaemic attacks (TIAs)
Peripheral vascular disease
Atherosclerotic aortic disease
Hospitalisation
Cardiovascular disease-related
All-cause hospitalisation
Ralph Kwame Akyea - Chief Investigator - University of Nottingham
Ralph Kwame Akyea - Corresponding Applicant - University of Nottingham
Evangelos Kontopantelis - Collaborator - University of Manchester
Folkert Asselbergs - Collaborator - University College London ( UCL )
Joe Kai - Collaborator - University of Nottingham
Nadeem Qureshi - Collaborator - University of Nottingham
Stephen Weng - Collaborator - University of Nottingham
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation