A Hierarchical Bayesian approach to optimising hypertension management strategies

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Many decisions in medicine are subject to measurement uncertainties and physiological variations which mean that treatment decisions may be made erroneously. These uncertainties are rarely explicitly considered in clinical management algorithms, limiting the efficacy and efficiency of clinical care. Management of raised blood pressure (hypertension) is a particularly important example, as hypertension is the single greatest cause of death and disability worldwide. In the UK approximately 1 in 3 adults require drug treatment for hypertension, imposing a huge burden on health care delivery. In an emergent collaboration between the Southampton Astronomy group and the Department of Clinical Pharmacology of St Thomas' Hospital at King's College London, we have adapted Monte Carlo simulations used in extra-galactic Astronomy to model the random effects of measurement uncertainty in a virtual population of hypertensive individuals. Our work showed that current treatment strategies for medication are too inefficient, with typically 40% of the population not optimally controlled, and thus at risk of adverse events. Our work obtained a Silver Award at the STEM for Britain competition 2019 at the House of Commons, which prizes "ground-breaking, frontier" projects in R&D. Building on the recent success of our collaboration, in this research proposal we aim to produce a tailored Hierarchical Bayesian Monte Carlo algorithm to develop the first smart blood pressure management algorithm. This algorithm will aim to combine patient-specific factors (for example starting blood pressure, sex, age and weight) with drug efficacy and measurement error, to predict the probability of an individual achieving blood pressure control for a given approach. The model will be validated using published data (from both clinical trials and observational cohorts) and real-world patient journeys from the St Thomas' Hospital Hypertension Clinic. More specifically, making use of anonymised data in the public domain, we will adopt the smart algorithm to conduct in silico clinical trials which aim to improve the proportion of hypertensive individuals achieving the desired blood pressure target with the minimal burden on both patient and healthcare system. This series of virtual clinical trials will aim to identify the most promising management approach(s) to take forward into real-world studies. Cardiovascular diseases have a huge cost of tens of millions pounds in the UK. Whilst the final evaluation of this work would require validation by means of a clinical trial comparing a final personalised treatment plan to standard care, the present approach has the potential to rapidly perform a large number of "in-silico" (i.e, virtual/simulated) comparisons to select a near-optimal treatment plan that can be tested in a clinical trial. Furthermore, it will provide a quantitative prediction of the degree of improvement expected, with the improved plan providing the necessary information to set up the clinical trial adequately. Our project has the potential to reduce cardiovascular events, improve efficiency of healthcare delivery, thus providing a substantial saving opportunity for the NHS. We will disseminate our work through the publication of peer-reviewed manuscripts and presentations at national/international conferences. We then envision a comprehensive research dissemination programme supported by in-house dissemination officers at the University of Southampton and at King's College London.