STRAtifying Treatments In the multi-morbid Frail elderlY (STRATIFY): Statins
Using 'big data' to better understand the relationship between blood pressure lowering treatment and adverse events
- Use data from the medical records of 100,000s of patients in England to derive mathematical models which predict an individual’s likelihood of suffering side effects associated with statin treatment.
- Quantify the association between statins and side effects such as myopathy, diabetes and haemorrhagic stroke using data from previous clinical trials and electronic health records.
- Develop a statin drug harm calculator and combine this with existing drug benefit calculators to create a decision support tool for patients and doctors to use for shared decision making.
Why is this important?
Statins lower cholesterol and reduce an individual’s risk of suffering from heart attacks and stroke. These medications are usually started in high risk patients and then continued for many years. Much debate exists about the side effects of statins: what side effects exist and how commonly they occur. Much of this is stimulated by conflicting findings from different research studies and media hype. Despite a lack of evidence for some proposed side effects (e.g. cognitive impairment), and evidence that most are very rare, many patients refuse to start therapy, or stop taking it due to fears they are experiencing side effects. This work aims individualise the understanding of side effects associated with statins to support better targeting of treatment and improved adherence to medication.
This study will use three approaches to better understand the relationship between blood pressure lowering treatment and side effect: (1) use electronic health records from the Clinical Practice Research Datalink (CPRD) to create prognostic models for adverse events thought to be associated with statins including myopathy, diabetes, haemorrhagic stroke, cognitive impairment, and cataracts. These models will be derived and externally validated using electronic health records from primary and secondary care. (2) Undertake a systematic review of previous clinical trials examining the association between statins and side effects. Data describing this association in each trial will be extracted and combined in a meta-analysis. These estimates will be unbiased (due to randomisation in the original trials) but may not be reflective of the general population, since many trials include healthier populations less likely to suffer side effects to treatment. (3) Use data from the CPRD to derive treatment effect estimates for side effect using multivariate regression and propensity score matching to control for confounding. This approach will minimise (but not eliminate) bias from confounding by indication for treatment and provide estimates which reflect the real world population. Using both of these approaches will enable minimum and maximum likely treatment effects to be quantified.
How will this benefit patients?
This work will lead to the development of a new clinical decision support tool for both patients and doctors to use in shared decision making about whether to start or continue taking statin medications.