A Hierarchical Bayesian approach to optimising hypertension management strategies

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.

Academic dissemination and impact:
To maximise the impact of our research project within both the astronomical and medical academic communities, we aim to provide an extensive and efficient dissemination in two distinct ways: We will promote our work through the publication of peer-reviewed manuscripts and presentations at national/international conferences.
2. 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.

Societal dissemination and impact:
Public engagement will involve face-to-face activities with three main aims:
i) Increase the interest/awareness and knowledge/understanding about the Astronomy, medical science and the interconnection between them and the impact they have on our lives; ii) Attract new audiences; iii) Communicate results to the general public via mainly online material.
We will assess the impact of the outreach and public engagement activities via quantitative and qualitative evaluations. The feedback results from the evaluations will allow us to improve activities for the next round.
We will also produce online blogs, organise at least one general public talk, prepare talks/posters for large public outreach events, organise visits to local schools and participate in open-door days.

Economic impact:
i) Current success. Through the promising results of our initial work with St Thomas' Hospital,
PhD Astronomy student Lorenzo Zanisi 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. This project is in fact designed to fit in very well with the University of Southampton's, and more generally STFC's, distinctive interdisciplinary research and enterprise primary goal to "generate research and technologies that give real economic and social benefits".

ii) Savings for healthcare delivery. The patient-centric outcomes of blood pressure misclassification are a failure to reduce preventable cardiovascular events and/or side-effects from excessive medication or blood pressure-lowering. There is a subsequent significant economic burden on the NHS as a result of misclassification, and therefore an equal opportunity for savings. The potential societal impact of this project is thus enormous given that approximately one third of adults are hypertensive. Improvement to the efficiency and efficacy of hypertensive management would lead to improved patient outcomes through reduced cardiovascular events, reduced overtreatment and economic savings.

iii) Future technological developments. After the end of the grant we expect, partly supported by additional funding, to incorporate the smart algorithm into an App which will be piloted within the Hypertension Clinic at St Thomas' Hospital. The latter will incorporate the simulations' outputs and will aim to help practitioners decide the best way to proceed with treatment for a given clinical encounter.

ii) Research Excellence Framework. Last but not least, with the close collaboration of St Thomas' Hospital and the additional support of the current co-Is, we believe this research project can lay the grounds for more substantial interdisciplinary grant proposals and a competitive impact case for the next Research Excellence Framework assessments (2021, 2028). The project in fact directly transposes statistical and numerical algorithms, developed in the context of extra-galactic Astronomy, to vital medical applications with a clear societal impact, and accompanied by a detailed programme for testing and validation.