权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

EPSRC Centre for Predictive Modelling in Healthcare

EPSRC 医疗保健预测建模中心

基本信息

批准号：
EP/N014391/1
负责人：
John Terry
金额：
$ 255.98万
依托单位：
UNIVERSITY OF EXETER
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FN014391%2F1
关键词：
EPSRC Centre Predictive Modelling Healthcare

项目摘要

Our Centre brings together a world leading team of mathematicians, statisticians and clinicians with a range of industrial partners, patients and other stakeholders to focus on the development of new methods for managing and treating chronic health conditions using predictive mathematical models. This unique approach is underpinned by the expertise and breadth of experience of the Centre's team and innovative approaches to both the research and translational aspects.At present, many chronic disorders are diagnosed and managed based upon easily identifiable phenomena in clinically collected data. For example, features of the electrical activity of the heart of brain are used to diagnose arrhythmias and epilepsy. Sampling hormone levels in the blood is used for a range of endocrine conditions, and psychological testing is used in dementia and schizophrenia. However, it is becoming increasingly understood that these clinical observables are not static, but rather a reflection of a highly dynamic and evolving system at a single snapshot in time. The qualitative nature of these criteria, combined with observational data which is incomplete and changes over time, results in the potential for non-optimal decision-making. As our population ages, the number of people living with a chronic disorder is forecast to rise dramatically, increasing an already unsustainable financial burden of healthcare costs on society and potentially a substantial reduction in quality of life for the many affected individuals. Critical to averting this are early and accurate diagnoses, optimal use of available medications, as well as new methods of surgery. Our Centre will facilitate these through developing mathematical and statistical tools necessary to inform clinical decision making on a patient-by-patient basis. The basis of this approach is patient-specific mathematical models, the parameters of which are determined directly from clinical data obtained from the patient. As an example of this, our recent research in the field of epilepsy has revealed that seizures may emerge from the interplay between the activity in specific regions of the brain, and the network structures formed between those regions. This hypothesis has been tested in a cohort of people with epilepsy and we identified differences in their brain networks, compared to healthy volunteers. Mathematical analysis of these networks demonstrated that they had a significantly increased propensity to generate seizures, in silico, which we proposed as a novel biomarker of epilepsy. To validate this, an early phase clinical trial at King's Health Partners in London has recently commenced, the success of which could ultimately lead to a revolution in diagnosis of epilepsy by enabling diagnosis from markers that are present even in the absence of seizures; reducing time spent in clinic and increasing accuracy of diagnosis. Indeed it may even make diagnosis in the GP clinic a reality.However, epilepsy is just the tip of the iceberg! Patient-specific mathematical models have the potential to revolutionise a wide range of clinical conditions. For example, early diagnosis of dementia could enable much more effective use of existing medication and result in enhanced quality and quantity of life for millions of people. For other conditions, such as cortisolism and diabetes where a range of treatment options exist, identifying the optimal medication, and the pattern of its delivery, based upon the profile of the individual will enable us to maximise efficacy, whilst minimising unwanted side effects.

我们的中心汇集了世界领先的数学家，统计学家和临床医生团队，以及一系列工业合作伙伴，患者和其他利益相关者，专注于开发使用预测数学模型管理和治疗慢性健康状况的新方法。该中心团队的专业知识和丰富的经验以及研究和转化方面的创新方法支持了这种独特的方法。目前，许多慢性疾病的诊断和管理基于临床收集数据中容易识别的现象。例如，大脑心脏的电活动的特征用于诊断心律失常和癫痫。血液中激素水平的采样用于一系列内分泌疾病，心理测试用于痴呆症和精神分裂症。然而，越来越多的人认识到，这些临床可观测值不是静态的，而是在一个时间快照上反映了一个高度动态和不断发展的系统。这些标准的定性性质，加上不完整和随时间变化的观测数据，导致可能作出非最佳决策。随着我们的人口老龄化，预计慢性疾病患者的数量将急剧增加，增加了社会上已经不可持续的医疗保健费用的财务负担，并可能大幅降低许多受影响的个人的生活质量。避免这种情况的关键是早期和准确的诊断，最佳使用现有药物以及新的手术方法。我们的中心将通过开发必要的数学和统计工具来促进这些，以告知临床决策对患者的基础上。这种方法的基础是患者特定的数学模型，其参数直接从患者获得的临床数据中确定。作为一个例子，我们最近在癫痫领域的研究表明，癫痫发作可能来自大脑特定区域的活动与这些区域之间形成的网络结构之间的相互作用。这一假设已经在一组癫痫患者中得到了验证，我们发现了他们的大脑网络与健康志愿者相比的差异。对这些网络的数学分析表明，它们具有显著增加的癫痫发作倾向，我们提出将其作为癫痫的新生物标志物。为了验证这一点，伦敦的King's Health Partners最近开始了一项早期临床试验，该试验的成功可能最终导致癫痫诊断的革命，即使在没有癫痫发作的情况下也能从标记物中进行诊断;减少在临床上花费的时间并提高诊断的准确性。事实上，它甚至可能使全科医生诊所的诊断成为现实。然而，癫痫只是冰山一角！患者特定的数学模型有可能彻底改变广泛的临床条件。例如，痴呆症的早期诊断可以使现有药物的使用更加有效，并提高数百万人的生活质量和数量。对于其他疾病，如皮质醇症和糖尿病，存在一系列治疗选择，根据个人的情况确定最佳药物及其输送模式将使我们能够最大限度地提高疗效，同时最大限度地减少不必要的副作用。