Developing, translating and evaluating risk growth charts for chronic diseases and multimorbidities using population-wide electronic health records

使用全民电子健康记录开发、翻译和评估慢性病和多种疾病的风险增长图

• 批准号: 2601322
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2601322
• 关键词: Developing; translating; evaluating; risk; growth

Multimorbidity describes the presence of two or more chronic health conditions. Multimorbidities are particularly relevant in the context of older adults: it is estimated that 80% of adults over the age of 80 have two or more chronic conditions, often including one or more cardiovascular diseases (CVD). The proposed research project will aim to develop risk growth charts modelling dynamic risk of developing diseases to predict multimorbidities using population-wide electronic health records (EHRs). By leveraging full medical histories of millions of participants we will be able monitor and calculate their risk of developing one or multiple chronic diseases across the patient life course. EHRs are a type of data with unique advantages. With the modernisation and digitalisation of primary care, EHRs have become more readily available, with regular up-to-date addition of new data.Furthermore, EHRs include repeated measurements of clinical features, such as blood pressure and LDL and HDL cholesterol measurements, which can be used to predict an "estimated current value" which is thought to be a less error-prone than single measures of risk factors. Moreover, the availability of a full medical history allows us to observe the general trend of a patient's health. By using population health data and electronic health records, including the repeated measurements of epidemiological risk factors, we aim to develop multimorbidities risk prediction models. The way different chronic conditions are interconnected will be investigated by observing at the overlap of multiple diseases and their severity in the population. Data will be sourced from the UK Biobank and NHS Digital. The CVD-COVID-UK Consortium can also be used to explore the relevance of COVID severity as a risk factor for CVD and how it is associated with known risk factors of CVD. CVD will be considered on both an individual scale (for example just investigating myocardial infarction) and on a disease group scale investigating multiple CVDs at a time (for example investigating myocardial infarction and strokes).Various statistical approaches will be used to develop risk prediction models including regression, clustering and machine learning techniques. PhD training will include attending Machine Learning and Artificial Intelligence lectures from the University of Cambridge and attending a three-day course from Keele University: "Statistical Methods for Risk Prediction and Prognostic Models". Towards later stages of the project, we plan to explore future prospects of combining EHRs and genomic data, in the form of polygenic risk scores, to predict cardiovascular disease risk.In the future, multimorbidities risk prediction models based on EHRs can be used in clinical practices to monitor patients and their chronic diseases risk by considering their full medical history. Being able to acknowledge multiple chronic conditions at once will shift primary care from a disease-focused approach to considering the patient and their health as a whole. Risk prediction models will assist clinicians in detecting early signs of increased chronic diseases risk, prioritise which patients should undergo further risk assessments or screening and encourage lifestyle changes in populations at higher risk.

多发病是指存在两种或两种以上的慢性健康状况。多病在老年人中尤其相关：据估计，80%的80岁以上的成年人患有两种或多种慢性疾病，通常包括一种或多种心血管疾病（CVD）。拟议的研究项目旨在开发风险增长图表，模拟疾病发展的动态风险，以使用全民电子健康记录（EHR）预测多种疾病。通过利用数百万参与者的完整病史，我们将能够监测和计算他们在整个患者生命过程中患一种或多种慢性疾病的风险。EHR是一种具有独特优势的数据类型。随着初级保健的现代化和数字化，电子病历变得更加容易获得，并定期添加最新的新数据。此外，电子病历包括对临床特征的重复测量，例如血压以及低密度脂蛋白和高密度脂蛋白胆固醇测量，可用于预测“估计当前值”，这被认为比单一风险因素测量更不易出错。此外，提供完整的病史可以让我们观察病人健康的一般趋势。通过使用人群健康数据和电子健康记录，包括流行病学危险因素的重复测量，我们的目标是建立多病风险预测模型。将通过观察多种疾病的重叠及其在人口中的严重程度来研究不同慢性病相互联系的方式。数据将来自英国生物银行和NHS数字。CVD-COVID-UK联盟还可用于探索COVID严重程度作为CVD风险因素的相关性，以及它如何与CVD的已知风险因素相关。CVD将在个体规模（例如仅研究心肌梗死）和疾病组规模（例如研究心肌梗死和中风）上进行研究，同时研究多种CVD。将使用各种统计方法开发风险预测模型，包括回归，聚类和机器学习技术。博士培训将包括参加剑桥大学的机器学习和人工智能讲座，并参加基尔大学为期三天的课程：“风险预测和预后模型的统计方法”。在该项目的后期阶段，我们计划探索将EHR和基因组数据相结合的未来前景，以多基因风险评分的形式预测心血管疾病的风险。未来，基于EHR的多发病风险预测模型可以用于临床实践，通过考虑患者的完整病史来监测患者及其慢性病风险。能够同时确认多种慢性病将使初级保健从以疾病为中心的方法转变为将患者及其健康作为一个整体来考虑。风险预测模型将帮助临床医生发现慢性病风险增加的早期迹象，优先考虑哪些患者应该接受进一步的风险评估或筛查，并鼓励高风险人群改变生活方式。