Forecasting personal health in an uncertain environment

医疗保健服务是在嘈杂和不确定的环境中进行的。一些患者在首次出现症状后就出现了，而另一些患者可能会等到出现症状后才去看医生。一些患者遵守并对治疗有反应，而另一些则没有。因此，如果不粗略地使用随机数来确定风险，就很难预测个人的健康状况。另一方面，人体生理学的机制模型显示出作为研究工具的希望，但很难有效地应用于医疗保健问题，因为没有框架来嵌入在个体和人群中看到的结构和功能的可变性。这个大胆而雄心勃勃的提议的挑战是，通过开发新的数学工具，在每个层面嵌入不确定性，使描述个体通过医疗保健的轨迹的模型能够通过器官系统的详细机制和多尺度模型得到信息，从而为医疗保健建模带来一个步骤的变化。这个项目的独特之处在于，它将生理学的机械模型与个性化医学模型结合起来，并在所有尺度上整合不确定性。我们认识到我们的愿景是大胆和雄心勃勃的。作为解决这个问题的“途径”，我们选择了两个特定的范例，其中项目合作伙伴已经拥有专业知识并可以访问模型、工具和数据。第一种是流感在人群中的传播，第二种是普遍存在的心律失常（心房颤动）。我们将采用“中间退出”的方法，并将从患者（个体）规模开始，每个样本都有丰富的数据。从这一点开始，我们将向上研究人口规模，向下研究分子规模。模拟流感传播的传统方法将考虑个体之间的接触，以及感染从一个人传染到另一个人的可能性。我们的方法是分解这种可能性，代之以一个基于个人健康状况的感染可能性模型（他们是否吸烟，他们是否患有哮喘等），再加上人口变异性（例如气道几何形状，这是吸入颗粒如何运输到肺气道黏液层的关键决定因素）。一种传统的方法来模拟心房颤动在心脏将考虑到人类心房的几何形状，并结合触发器。该模型将对中庭的结构和功能进行重塑。我们的方法是使用纵向研究的数据来参数化心房颤动模型，并使用模型的输出来确定对该患者最有益的治疗方法。在医疗保健系统面临的诸多挑战中，根据不完整的信息做出的临床决策可能导致治疗成功率低、资源使用效率低和患者预后差。该建议旨在使临床医生能够更好地利用由机制模型提供信息的患者特定数据。我们试图将多尺度生理建模推向一个新的复杂水平，通过在不同的组织尺度上系统地整合不确定性和可变性，并将结果转化为临床。我们的目标是让我们的方法在整个医疗保健部门找到实用性：临床医生将能够对每位患者的结果做出更明智的预测；人口模型将为政策决定提供信息；制药公司将能够评估药物对个人的疗效；病人也会享受到更好的治疗效果。

Healthcare delivery takes place in a noisy and uncertain environment. Some patients present following their first symptom, whereas others may wait before presenting to a healthcare provider. Some patients comply with and respond to treatment, while others don't. Forecasting the health status of an individual is consequently difficult without the crude use of random numbers to determine risk. On the other hand, mechanistic models of human physiology show promise as research tools, but are difficult to apply effectively to healthcare problems because there is no framework to embed the variability in structure and function that is seen in individuals and human population. The challenge of this bold and ambitious proposal is to bring about a step change in both modelling for healthcare by developing new mathematical tools that embed uncertainty at every level, enabling models that describe the trajectory of an individual through healthcare to be informed by detailed mechanistic and multiscale models of organ systems. This project is unique in that it combines mechanistic models of physiology with the modelling personalised medicine with the integration of uncertainty at all scales.We recognise that our vision is bold and ambitious. As a "way in" to this problem we have selected two specific exemplars where the project partners already have expertise and access to models, tools, and data. The first of these is the transmission of influenza within a population, and the second is a prevalent cardiac arrhythmia (atrial fibrillation). We will adopt a "middle out" approach, and will start at the patient (individual) scale where there are rich data for each exemplar. From this point we will work upwards to population scale, and downwards to the molecular scale. A conventional approach to model transmission of influenza would take into account contact between individuals, with a probability of infection passing from one to another. Our approach will be to unpick this probability, replacing it with a model of the likelihood of infection based on knowledge of the health status of an individual (do they smoke, do they have asthma, etc.), coupled with population variability (for example of airway geometry, a key determinant of how inhaled particles are transported to the mucus layer of lung airways). A conventional approach to modelling atrial fibrillation in the heart would take into account the geometry of the human atrium, combined with a trigger. Remodelling of the structure and function of the atria would be imposed on the model. Our approach will be to parameterise a model of atrial fibrillation using data from longitudinal studies, and use output from the model to determine the treatment that would have been most beneficial for that patient. Among the many challenges faced by the healthcare system, clinical decisions informed by incomplete information can lead to low treatment success rates, inefficient use of resources and poor patient outcomes. This proposal seeks to enable clinicians to make better use of patient-specific data, informed by mechanistic modelling. We seek to push multiscale physiological modelling to a new level of sophistication, by incorporating uncertainty and variability systematically across different scales of organization and translating the outcomes to the clinic. Our ambition is for our approach to find utility across the healthcare sector: the clinician will be able to make better-informed forecast of outcomes for each patient; policy decisions will be informed by population models; pharma companies will be able to assess the efficacy of drugs on individuals; and the patient will enjoy improved outcomes.