Computational modelling of care needs in the rare dementias

关于罕见痴呆症的出现顺序和症状变化率以及与护理相关的需求，信息很少。当“下一步是什么？当一个“问题被提出时，痴呆症患者（PLWD）和护理人员经常会收到诸如“很难说”或“它对每个人的影响都不同”之类的回答-这些回答既真实又完全没有帮助。阿尔茨海默病的大致阶段指南已经产生（例如Reisberg的阿尔茨海默病的7个阶段），但这些都是基于临床经验，而不是定量数据，并且不适用于大多数罕见，非典型或非典型的疾病。该项目的总体目标是为医生，患者和家庭提供工具，以更好地规划人们的未来-最终回答“下一步是什么？“问题。数据驱动的定量方法来了解神经系统疾病的进展出现在过去十年的早期，受益于神经退行性疾病（如阿尔茨海默病，痴呆症的最常见原因）的大型医学数据集的可用性。“Imaging Plus X”计算模型现在已经是一个成熟的研究领域[Oxtoby 2017; Khatami 2019]。其中一种原始方法，即基于事件的模型（EBM [Fonteijn 2012; Young 2014]），是一种描述疾病进展的稳健计算方法，无需先验临床分期，仅需横断面数据。EBM已应用于一系列神经系统疾病，包括阿尔茨海默病、亨廷顿病和多发性硬化症，使用基于生物标志物的事件，包括大脑中的局部萎缩（萎缩）、脑脊液中的异常蛋白水平，以及最近在认知能力测量方面的工作[Firth 2019; Firth 2020]。虽然这项工作产生了独特的基于生物标志物的疾病进展的理解，它告诉我们很少关于病人和照顾者的经验。该项目将通过开发和应用数据驱动的计算建模和机器学习技术来解决这一差距，以获得PLWD及其护理人员的自我报告数据。

Very little information is available about the order of appearance and rates of change for symptoms and care-related needs in the rarer dementias. When the "What next?" question is raised, people living with dementia (PLWD) and carers all too often receive responses such as "It is difficult to say" or "It affects everyone differently" - which are simultaneously true and completely unhelpful. Approximate guides to the stages of Alzheimer's disease have been produced (e.g. Reisberg's 7 stages of Alzheimer's) but these are based on clinical experience, not quantitative data, and are not available for most rare, atypical or young-onset conditions.The overarching goal of the project is to provide doctors, patients and families with tools to better plan for people's future - finally answering the "What next?" question. Data-driven quantitative approaches to understanding neurological disease progression emerged early in the last decade, benefitting from the availability of large medical data sets in neurodegenerative diseases such as Alzheimer's disease, the most common cause of dementia. "Imaging Plus X" computational models are now a mature research field in their own right [Oxtoby2017; Khatami2019]. One of the original methods, the event-based model (EBM [Fonteijn2012; Young2014]), is a robust computational approach to describing disease progression without requiring a priori clinical staging and requiring only cross-sectional data. EBMs have been applied across a range of neurological diseases including Alzheimer's disease, Huntington's disease, and Multiple Sclerosis, using biomarker-based events including regional atrophy (shrinkage) in the brain, levels of abnormal proteins in cerebrospinal fluid, and more recently in work on measures of cognitive ability [Firth2019; Firth2020]. While this work has generated unique biomarker-based understanding of disease progression, it tells us very little about patient and carer experience. This project will address this gap by developing and applying data-driven computational modelling and machine learning techniques to self-report data from PLWD and their carers.