People are living for longer, with more long-term physical and mental conditions which worsen their health. One example is high blood pressure, where people can take 3-4 drugs to prevent stroke. However, dozens of people have to be treated for at least a year to prevent a stroke in one person. This is because these drugs only reduce the possibility of stroke, they do not remove it altogether. Some of these patients may be prone to side effects such as falls and kidney problems which may be more common than any benefits.
This proposal aims to use information from the medical records from hundreds of thousands of patients to establish the link between drugs used to prevent heart attack and stroke and side effects. We will focus on drugs that lower blood pressure (known as antihypertensives), lower cholesterol (known as statins) or prevent blood clots (known as antiplatelets). This information will be used to develop a calculator which predicts a persons risk of experiencing side effects. This calculator will form part of a support tool which will help patients and doctors make better informed decisions about starting or continuing drugs.
Background
The population is ageing and consequently, the number of people living with age-related chronic conditions is increasing. Polypharmacy (five or more prescribed medications) is common in older people and is associated with an increased risk of adverse drug reactions. Preventative medications, such as those used to manage blood pressure and cholesterol, are common in polypharmacy and often require large numbers of people to be treated to prevent a small number of cardiovascular disease (CVD) events. This leaves many individuals on drugs of little benefit, some of whom may be susceptible to side effects such as falls, kidney problems and muscle pain.
Aims
This proposal aims to quantify the harms of cardiovascular prevention medication, and the characteristics of those people most likely to suffer them. This research is one part of a larger research programme to develop a clinical decision tool which estimates an individuals likelihood of benefiting or suffering harm from treatment.
Methods
Aim 1: Derive prognostic models for an individuals risk of adverse events associated with cardiovascular prevention treatment (antihypertensives, statins and antiplatelets) using data from the CPRD GOLD. Adverse event outcomes will include falls (antihypertensives), myopathy (statins) and bleeding (antiplatelets).
Aim 2: Externally validate each model using data from the CPRD Aurum.
Aim 3: Use causal inference methods (multivariable regression, propensity score adjustment/matching, instrumental variable analysis) to examine whether modification of treatment could have an important impact on the risk of adverse events.
Primary outcomes - Falls; Myopathy/muscle pain; Intracerebral haemorrhage
Secondary outcomes Fracture; Syncope; Hypotension (symptomatic); Acute kidney injury; Electrolyte abnormalities; Gout; Diabetes mellitus; Intracerebral haemorrhage; Cataract; Liver dysfunction; Dementia; Memory problems (including dementia); Gastrointestinal haemorrhage; Dyspnoea; Any adverse event
Specific outcomes will be examined in relation to exposure to either antihypertensives, statins or antiplatelets (details given in section N).
James Sheppard - Chief Investigator - University of Oxford
James Sheppard - Corresponding Applicant - University of Oxford
Ariel Wang - Collaborator - University of Oxford
Constantinos Koshiaris - Collaborator - University of Oxford
Ghadeer Ghosheh - Collaborator - University of Oxford
Kym Snell - Collaborator - Keele University
Lucinda Archer - Collaborator - Keele University
Lucinda Archer - Collaborator - University of Birmingham
Niamh Gould - Collaborator - University of Oxford
Richard Hobbs - Collaborator - University of Oxford
Richard McManus - Collaborator - University of Oxford
Richard Riley - Collaborator - Keele University
Richard Stevens - Collaborator - University of Oxford
Rupert Payne - Collaborator - University of Bristol
Sarah Lay-Flurrie - Collaborator - University of Oxford
Subhashisa Swain - Collaborator - University of Oxford
Takeshi Fujiwara - Collaborator - University of Oxford
Tingting Zhu - Collaborator - University of Oxford
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation