Uncovering Contributors to Hypertension through Experimental and Computational Simulation (CHECS)

High blood pressure, or hypertension, is one of the most important causes of global morbidity and mortality in the developed world [1]. It has been shown that hypertensive people have a high risk of stroke, heart attack, heart failure and renal failure. The Health Survey for England in 2006 demonstrated that the prevalence of hypertension in the UK increased from 17% in the age group 40-49 years to 77% in those aged 70-79 years [2].

Hypertensive patients are usually identified by a threshold diagnosis of their systolic or diastolic pressures exceeding 140 or 90 mmHg respectively. However this diagnosis tends to misdiagnose the individuals in the large population in and around the threshold making the selection for appropriate therapy difficult. For example one important determinant of hypertension is the flexibility of the aorta (the first artery leading from the heart), which becomes stiffer with age and arteriosclerosis. However, such "stiffness" is only one among other geometrical and mechanical factors that influence the pressure pulse and thus hypertension. Therefore, non-invasive measurement of pulse pressure waveforms has been of interest for more than 100 years, and includes tonometry, Ultrasound and Magnetic Resonance Imaging (MRI). Although the non-invasive measurement of waveforms has become fast, the current analysis of the measured waveform data is relatively simplistic. In particular, the analysis of certain waveform features are performed in isolation and are impeded by a lack of understanding of the relative contributions from arterial stiffness/geometry, wave reflection and ventricular/arterial interaction to hypertensive pressure. Over the last two decades, computational modelling has been established as a new discipline to study the interaction of different parameters in the cardiovascular system. These models can help to separate the various contributions to the pressure waveform and elucidate complex interaction of parameters affecting hypertension. More recently, imaging data of the patient's anatomy and physiology has been introduced in numerical simulations to produce patient-specific models. Although, different models have been developed to investigate the influence of geometrical and mechanical factors, a model validation remains challenging since it would require large studies in animals and patients.

This proposal aims at the identification of high-risk individuals by determining the mechanical factors which cause their pressure to be pathological. This approach would allow a better selection of appropriate treatments for the individual patient. For this, we propose the construction of a comprehensive experimental arterial model with which to determine and quantify main contributors to hypertensive pressure as well as to validate our existing computational arterial simulation frameworks (1D and 3D). Translation of these technologies towards the clinic will be facilitated with the construction of full-scale silicone arterial model, which will experimentally simulate haemodynamics of a hypertensive patient dataset. This will be followed by a clinical validation of a computational analysis tools in volunteers and a small patient cohort.

References:
[1] MacMahon, S., et al.: Blood-pressure-related disease is a global healthy priority. Lancet, 2006. 371: p. 1480-1482.
[2] NHS, Health survey for england 2006 latest trends. 2008: Leeds.

We propose a collaborative project between different disciplines to improve the clinical management of hypertension by understanding its main haemodynamic contributors. For this, we will develop novel experimental and numerical models of the aorta using medical measurements (e.g. imaging and pressure). The proposed project has a strong focus on clinical translation of biomedical engineering techniques by exploiting our existing academic and clinical network. Furthermore, we will investigate different pathways to impact of research results for different beneficiaries:

1. Hypertensive patients are usually identified by a threshold diagnosis, based normally on their systolic or diastolic pressures exceeding 140 or 90 mmHg respectively. Advice for these thresholds are based on large population studies such as the HOT study [1] rather than individual assessments. However such a pressure threshold diagnosis creates a substantial grey area of subjects within and around these values, and does not identify the individual causes of hypertension making the selection of an appropriate therapy difficult. This proposal aims to identify high-risk individuals and the determining mechanical factors, which cause their pressure to be pathological allowing better selection of appropriate treatments for the individual patient.

2. Socio-Economic. Despite progress in recent years in the prevention, detection, and treatment of high blood pressure, hypertension remains an important public health challenge. Hypertension affects approximately 1 billion people worldwide and contributes to approximately 50% of all cardiovascular deaths, (estimated 8 million deaths/year worldwide). The American Heart Association estimated the direct and indirect costs of high blood pressure in 2010 as $76.6 billion [2]. In the UK, the clinical management of hypertension is one of the most common interventions in primary care, accounting for approximately £1 billion in drug costs alone in 2006 [NICE clinical guideline 127- Hypertension]. Our project aims to develop tools to identify those patients who would benefit from drug treatments.

3. NHS. Our project aims at assessment through data integration of different clinical measurements using a computational model. The NHS would greatly benefit from a more a patient specific approach to reduce the number of hospital visits and to increase the overall success of treatments.

4. Industry. The demonstration of clinical feasibility of data integration with computational modelling tool has a high potential for commercialization. In addition, the application of the experimental model could be of great interest for device companies to develop and test new devices. The well-defined haemodynamic environment could be also of high benefit for medical diagnostic companies to test the accuracy and reproducibility of haemodynamic waveform measurements.

5. Academia. The UK has a leading international position in clinical research and medical imaging. Over the last decade tremendous achievements in computational modelling were made to describe haemodynamics with an integrative biophysical theory. Our project avoids the extensive use of animal models to study the different contributors and thus follows the NC3R-guidelines. Furthermore, our proposed experimental model can replace partly expensive, and ethically involved animal models in haemodynamic research. Our project will further strengthen and expand the position of UK academia both in biomedical engineering and medicine.

References:
[1] Hansson, L., et al.: Effects of intensive blood-pressure lowering and low-dose aspirin in patients with hypertension: Principal results of the hypertension optimal treatment (hot) randomised trial. The Lancet, 1998. 351(9118): p. 1755-1762.
[2] Lloyd-Jones, D., et al.: Heart disease and stroke statistics-2010 update. Circulation, 2010. 121(7): p. e46-e215.