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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/N014391/2
负责人：
John Terry
金额：
$ 30.92万
依托单位：
University of Birmingham
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FN014391%2F2
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，重点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。这种独特的方法以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为基础。目前，许多慢性疾病是根据临床收集数据中易于识别的现象来诊断和管理的。例如，心脏或大脑的电活动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，心理测试用于痴呆和精神分裂症。然而，人们越来越认识到，这些临床观察结果不是静态的，而是单个时间快照中高度动态和不断发展的系统的反映。这些标准的定性性质，加上不完整且随时间变化的观测数据，可能会导致非最佳决策。随着人口老龄化，预计患有慢性疾病的人数将急剧增加，这会增加本已不可持续的社会医疗费用经济负担，并可能导致许多受影响个体的生活质量大幅下降。避免这种情况的关键是早期准确的诊断、现有药物的最佳使用以及新的手术方法。我们中心将通过开发必要的数学和统计工具来促进这些工作，为每个患者的临床决策提供信息。这种方法的基础是患者特定的数学模型，其参数直接根据从患者获得的临床数据确定。举个例子，我们最近在癫痫领域的研究表明，癫痫发作可能是由于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而产生的。这一假设已经在一组癫痫患者中进行了测试，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，它们在计算机中产生癫痫发作的倾向显着增加，我们提出将其作为癫痫的新型生物标志物。为了验证这一点，伦敦 King's Health Partners 的一项早期临床试验最近开始，该试验的成功最终可能导致癫痫诊断领域的一场革命，因为即使没有癫痫发作，也可以通过标记物进行诊断；减少临床时间并提高诊断准确性。事实上，它甚至可以使全科医生诊所的诊断成为现实。然而，癫痫只是冰山一角！针对患者的数学模型有可能彻底改变各种临床状况。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，例如皮质醇增多症和糖尿病，存在一系列治疗选择，根据个人情况确定最佳药物及其给药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需要的副作用。