PROOF-BP - predicting home blood pressure based on clinical readings
- Use patient characteristics and details of their clinic blood pressure to derive a model for predicting the difference between clinic and 24 hour ambulatory blood pressure.
- Validate this model and examine its application as a means to target 24 hour ambulatory blood pressure monitoring more efficiently in routine clinical practice.
Why this is important:
High blood pressure (hypertension) affects over 900 million people across the world. It is an important risk factor for heart attacks or stroke which are the leading cause of death and disability worldwide.
Better targeting of blood pressure monitoring could lead to more appropriate diagnosis and treatment of hypertension, reducing the risk of heart attack and stroke.
- Dr James Sheppard, University of Oxford.
The diagnosis and management of hypertension depends on accurate measurement of blood pressure in order to target treatment appropriately and avoid unnecessary healthcare costs. Traditionally, blood pressure measurement takes place in the GP’s clinic. However, measurements taken using a 24 hour ambulatory blood pressure monitor are considered more accurate and the GP’s ‘clinic blood pressure’ measurement is often different, leading to incorrect classification and hence inappropriate treatment.
The PRedicting Out-of-OFfice Blood Pressure in the clinic study aims to examine characteristics about a patient and their clinic blood pressure level to derive a prediction model (a mathematical equation which permits prediction of an outcome) for the difference between clinic and 24 hour ambulatory blood pressure. The analysis will involve linear regression techniques and use data from previous 6 research studies examining blood pressure measurements taken in over 2,000 patients.
How this could benefit patients:
The resulting prediction model could be used for targeted triaging of ambulatory or home blood pressure monitoring in routine clinical practice and easily be incorporated into GP computer systems, accessed as an online calculator or even built into smartphones linked to blood pressure monitors. Improved targeting of blood pressure monitoring could lead to more appropriate diagnosis and treatment of hypertension, and thus reduced risk of heart attack and stroke.
Further examination of the prediction model derived in this work will be conducted as part of the PROOF-ABPM study. In addition, the cost-effectiveness of this approach to the diagnosis and management of hypertension with be explored by adapting the economic model originally designed by the National Institute for Health and Care Excellence.