Steroids are a group of medications that are widely used to treat many different diseases of long duration, such as asthma, and their prolonged use has increased over time in the UK despite their side effects. Side effects can develop within days (e.g. mental illness) or after prolonged use (e.g. high blood pressure). Side effects can have a major impact on quality of life, independence and wellbeing, particularly as people get older, and lead to many hidden costs to the individual and the healthcare system.
For many conditions there are no approved alternatives to steroids and it is important we obtain accurate information about the side effects of treatment and the full costs of these to the individual and the NHS. This information is very helpful in building a strong case to support the introduction of new treatment, which have been shown to be effective in clinical trials, into the NHS.
With CPRD data we intend to develop new methods that allow clinicians and policy makers to balance risks and benefits of steroid treatment in patients with common long-lasting diseases. This will be achieved first by describing the side effects and what makes these more likely to happen; and then developing methods that help to identify patients at higher risk of developing side effects.
The aim of this research is to develop tools that enable clinicians and policy decision makers to balance harms and benefits of glucocorticoids for the treatment of chronic inflammatory diseases. This will be achieved through; 1) description of glucocorticoid toxicity profiles and its predictors; and 2) development of treatment stratification tools for clinical evaluation to accurately identify patients at increased risk of developing toxicity. This research will enable informed personalised adaptation of treatment for patients identified with high risk of toxicity (e.g. earlier dose reduction or use of glucocorticoid-sparing therapies), improvement of clinical guidelines and quality of care and equity in health care provision, ultimately improving long-term patient outcomes.
To estimate toxicity rates and describe toxicity patterns we will expand our phenotyping work to identify patients with common chronic inflammatory diseases and adverse events. The description of common patterns of toxicity, overall and in patients with different underlying diseases and age groups, and the exploration of the relative importance of duration of medication and dose will inform the construction of risk prediction models to enable patient risk stratification according to risk of toxicity development and the future development of decision cost-models to guide policy and clinical decision making.
Glucocorticoid toxicity All-cause hospitalisation Hospitalisation for toxicity Death for toxicity All-cause mortality
Ann Morgan - Chief Investigator - University of Leeds
Ann Morgan - Corresponding Applicant - University of Leeds
Chris Bojke - Collaborator - University of Leeds
Mar Pujades Rodriguez - Collaborator - University of Leeds
Paul David Baxter - Collaborator - University of Leeds
Paul Stewart - Collaborator - University of Leeds
Richard Hubbard - Collaborator - University of Nottingham
Samantha Crossfield - Collaborator - University of Leeds