Improving diagnosis and treatment for the quantified patient

常见疾病往往有许多替代预防和治疗选择，但效果各不相同。分层医学的主要目标是确定类似干预措施可能对其最有效的患者亚群，或具有进一步并发症的高风险患者。该项目旨在找到定义这些亚群的方法，并根据他们的基因数据、他们的疾病随时间的临床表现以及他们所暴露的环境来预测任何给定患者属于哪个亚群。牛皮癣，一种炎症性皮肤病，将是这项研究的主要焦点，但将寻求机会将研究结果扩展到其他疾病。首先，将利用现有的临床和遗传患者数据来识别对一种相对较新的牛皮癣药物生物制剂有效和无效的患者之间的遗传差异。将开发统计方法，以确定这些遗传差异是否可以与先前建立的临床测量相结合，以预测哪些牛皮癣患者将对每种生物做出反应。将推荐一种“治疗算法”(一套规则，帮助皮肤科医生尽快确定最有效的生物)。第二，该项目将寻找使牛皮癣患者有可能进展为严重疾病或发展为其他疾病形式的并发症(“合并症”)的遗传和环境/生活方式因素。这将涉及到使用英国生物银行的数据，该银行包括来自英国普通人口的50万名志愿者。众所周知，参与者并不总是准确地报告他们的健康状况，因此最初的挑战将是准确地确定哪些参与者患有牛皮癣，以便我们能够进一步研究他们。将检查患有牛皮癣的参与者和没有患牛皮癣的参与者之间的遗传差异，以完善我们对牛皮癣遗传基础的理解。随后将进行统计分析，以确定与(1)牛皮癣的严重程度和(2)合并症风险相对应的遗传因素，并在考虑到患者潜在的遗传风险后，检查患者的环境暴露如何导致牛皮癣风险。第三，该项目将研究使用人工智能(AI)自动分析患者数据的方法，并帮助皮肤科医生或其他医疗保健提供者做出知情的医疗决策。要使这些先进的方法有效，需要比目前可用的数据集大得多的数据集，包括随时间推移的单个患者的常规数据。相关类型的数据可以包括皮肤图像或描述性数据。将探索收集这些数据的新方法，例如开发手机应用程序(LICATION)以直接从牛皮癣患者那里收集数据，以及支持收集和随后的人工智能分析所需的数据基础设施。还提议确定可用于运行初始测试人工智能分析的现有但目前未开发的数据资源。这可能包括对图像数据的分析(例如，牛皮癣皮损的照片能否自动与其他类型的炎症性皮肤病区分开来？)或患者报告的“生活质量”数据(例如，能否在早期阶段预测到感知生活质量大幅下降的患者？)。最后，将寻求与其他研究小组合作的机会，以便在这项工作中开发的结果和方法可以支持对其他常见长期疾病的分层。

There are often many alternative prevention and treatment options for common diseases, with varying effectiveness. Key aims of stratified medicine are to identify subgroups of patients for whom similar interventions are likely to be most effective, or who are at high risk of further complications. This project aims to find ways of defining these subgroups and predicting which subgroup any given patient belongs to based on their genetic data, clinical presentation of their disease over time, and the environment they have been exposed to. Psoriasis, an inflammatory skin disease, will be the primary focus of the research but opportunities will be sought to extend the findings to other diseases.Firstly, existing clinical and genetic patient data will be used to identify genetic differences between patients that respond well and those that do not respond to a relatively new class of psoriasis drugs called biologics. Statistical methods will be developed to determine whether these genetic differences can be combined with previously established clinical measurements to predict which psoriasis patients will respond to each biologic. A "treatment algorithm" (a set of rules to help dermatologists identify the most effective biologic as quickly as possible) will be recommended.Secondly, the project will seek genetic and environmental/lifestyle factors that put psoriasis patients at risk of progressing to severe disease or developing complications in the form of other diseases ("comorbidities"). This will involve using data from UK Biobank, which comprises 500,000 volunteers from the general UK population. It is known that participants do not always accurately report their health conditions, so an initial challenge will be to accurately determine which participants have psoriasis so that we can study them further. Genetic differences will be examined between participants that have psoriasis and those that do not to refine our understanding of the genetic basis of psoriasis. Subsequently statistical analyses will be performed to identify genetic factors that correspond to (1) severity of psoriasis and (2) risk of comorbidities, and to examine how a patient's environmental exposures contribute to psoriasis risk after accounting for their underlying genetic risk.Thirdly, the project will examine ways of using artificial intelligence (AI) to automatically analyse patient data and help dermatologists or other healthcare providers to make informed healthcare decisions. For these advanced methods to be effective much larger datasets are needed than are currently available, including regular data on individual patients over time. Relevant types of data could include skin images or descriptive data. New methods of collecting these data will be explored, such as the development of a mobile phone app(lication) to collect data directly from psoriasis patients, and the data infrastructure that would be required to support collection and subsequent AI analysis. It is also proposed to identify existing but currently untapped data resources that can be used to run initial test AI analyses. This could include analysis of image data (e.g. can photographs of psoriasis skin lesions be distinguished from other types of inflammatory skin disease automatically?) or patient-reported "life-quality" data (e.g. can patients that experience a large drop in perceived life quality be predicted at an early stage?).Finally, opportunities will be sought to collaborate with other research groups so that results and methodology developed during this work can support stratification of other common long-term diseases.