EPSRC Centre for Predictive Modelling in Healthcare

我们的中心将世界领先的数学家、统计学家和临床医生团队与一系列行业合作伙伴、患者和其他利益相关者聚集在一起，专注于开发使用预测性数学模型管理和治疗慢性健康状况的新方法。这一独特的方法是基于该中心团队的专业知识和丰富的经验，以及在研究和翻译方面的创新方法。目前，许多慢性疾病的诊断和治疗是基于临床收集的数据中容易识别的现象。例如，大脑心脏电活动的特征被用来诊断心律失常和癫痫。血液中的激素水平抽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，这些临床观察数据并不是静态的，而是反映了一个高度动态和不断演变的系统在时间上的单个快照。这些标准的定性性质与不完整且随时间变化的观测数据相结合，导致了非最佳决策的可能性。随着我们的人口老龄化，预计患有慢性病的人数将大幅上升，增加社会本已不可持续的医疗成本的财政负担，并可能大幅降低许多受影响个人的生活质量。避免这种情况的关键是早期和准确的诊断，最佳使用可用药物，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一点，以便为逐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和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进行了测试，我们发现，与健康志愿者相比，他们的大脑网络存在差异。对这些网络的数学分析表明，它们在硅胶中显著增加了癫痫发作的倾向，我们建议将其作为癫痫的一种新的生物标志物。为了验证这一点，伦敦国王健康伙伴公司最近开始了一项早期临床试验，该试验的成功最终可能导致癫痫诊断的一场革命，因为即使在没有癫痫发作的情况下，也能利用存在的标志物进行诊断；减少临床花费的时间，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！特定于患者的数学模型有可能彻底改变一系列的临床情况。例如，痴呆症的早期诊断可以更有效地利用现有药物，并提高数百万人的生活质量和数量。对于其他情况，如皮质醇中毒和糖尿病，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，同时将不想要的副作用降至最低。

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.