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STRATIFY antihypertensives

Better understanding the relationship between blood pressure lowering treatment and adverse events

Tablets

 Aims

  1. 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 blood pressure lowering treatment.
  2. Quantify the association between blood pressure lowering drugs and side effects such as fainting, falls and kidney problems using data from previous clinical trials and electronic health records
  3. Develop a antihypertensive 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?

High blood pressure is prevalent across the world, and many patients receive drugs to lower it and prevent cardiovascular disease. Medications are often started when patient has certain outlook and then continued for many years, with doctors reluctant to stop prescribing them despite changes in an individual’s risk/benefit profile. However, some patients who take medications for many years may suffer side effects such as falls and kidney problems which can significantly reduce an individual’s quality of life, particularly those who are old and frail. Currently, doctors and patients have little information to inform their understanding of when this might happen.

Methods

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 associated with antihypertensive treatment, including falls, kidney problems, fainting, gout and electrolyte disorders. 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 blood pressure lowering treatment and side effects. Data describing the 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 some 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 effects 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 blood pressure lowering medications.

 

Our team