Bringing Innovative Research Methods to Clustering Analysis of Multimorbidity (BIRM-CAM)

将创新研究方法引入多病态聚类分析 (BIRM-CAM)

• 批准号: MR/S027602/1
• 负责人: Tom Marshall
• 金额: $ 77.71万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS027602%2F1
• 关键词: Bringing; Innovative; Research; Methods; Clustering

Multimorbidity is when people suffer from more than one long-term illness. It is increasingly common as people live longer. It is important because individual illnesses have knock-on effects on others, it is more complex managing multiple than single illnesses, and multimorbid patients are heavy users of medications and health services.To understand multimorbidity we need to know which illnesses tend to occur together and which illness combinations most affect health. To adapt health services we need to know which types of people develop multimorbidity: their age, sex, ethnicity, socio-economic status and whether they tend to live in the same households. To learn how to prevent it we need to identify lifestyle factors (physical activity, diet, smoking, alcohol) linked to multimorbidity and the measurements (laboratory test results, weight, blood pressure) that might be early signs.Electronic health records are a good source of information on multimorbidity because they include information on the same patient over many years. They include information on illnesses, medications, hospital admissions; measurements (laboratory tests, weight, blood pressure) and lifestyle (smoking, alcohol). Previous research has studied multimorbidity using a variety of statistical methods. It finds some illnesses, such as diabetes and heart disease tend to occur together. But different statistical methods often find different groups of illnesses. We need a single, consistent approach to this type of analysis to ensure we are researching the same groups of illnesses. Previous research generally has not made best use of all the available information. For example, patients are considered either to have or not have diabetes but research did not make use of laboratory measurements (such as blood glucose) identifying some people as likely to develop diabetes. Previous research grouped illnesses according to how commonly they occur together, without giving any special significance to combinations of illnesses linked to risk of death or hospital admission. Clearly such combinations of illness are of more importance. There are more advanced analysis methods which can address these and other shortcomings.The first part of our research will develop methods of data analysis. We will review research on different statistical methods for grouping illnesses together. We will hold a workshop involving leading UK researchers in the field to try to agree on the best approach to this type of analysis. Informed by this we will analyse two large databases of electronic health records, each including several million patients. In each database we will identify the groups of illnesses that co-occur and check our findings in the other database. This is considered good practice in analysis. At the end of this step we will produce software to analyse and find groups of illnesses in electronic health records and make this freely available for other researchers to use.The next part of our research will use additional information from two large surveys. Both surveys include details not always available in health records e.g. occupation, diet, lifestyle and measures of frailty. One includes 500,000 people the other has information on the same people over a period of 14 years. We will describe the consequences for patients of different combinations of illnesses: their levels of frailty because it is linked to need for social care; development of further illnesses; medications, use of health services and death. We will work with patient advisors to help guide analysis of patients journeys through health services. We will investigate possible causes of multimorbidity including people's social circumstances, the environment, lifestyle (smoking, alcohol, diet and exercise) and laboratory test results that might help indicate causes. This step will point to the areas of environment and lifestyle which should be investigated further as possible causes.

多发病是指人们患有一种以上的长期疾病。随着人们寿命的延长，这种情况越来越普遍。这一点很重要，因为个体疾病会对其他疾病产生连锁反应，管理多种疾病比管理单一疾病更复杂，而多重疾病患者是药物和医疗服务的重度使用者。为了了解多重疾病，我们需要知道哪些疾病倾向于同时发生，哪些疾病组合最影响健康。为了调整卫生服务，我们需要知道哪些类型的人会患上多重人格：他们的年龄、性别、种族、社会经济地位以及他们是否倾向于生活在同一个家庭中。为了了解如何预防它，我们需要确定与多发性硬化症相关的生活方式因素（体力活动，饮食，吸烟，酒精）和可能是早期症状的测量结果（实验室检查结果，体重，血压）电子健康记录是多发性硬化症信息的良好来源，因为它们包含了多年来同一患者的信息。它们包括关于疾病、药物、入院情况、测量（实验室检查、体重、血压）和生活方式（吸烟、饮酒）的信息。以前的研究已经使用各种统计方法研究了多变量。它发现一些疾病，如糖尿病和心脏病往往同时发生。但不同的统计方法往往发现不同的疾病组。我们需要一种单一的、一致的方法来进行这种分析，以确保我们研究的是同一组疾病。以前的研究一般没有充分利用所有可用的信息。例如，患者被认为患有或未患有糖尿病，但研究没有利用实验室测量（如血糖）来确定某些人可能患糖尿病。以前的研究根据疾病一起发生的频率对疾病进行了分类，没有对与死亡或住院风险相关的疾病组合给予任何特殊意义。显然，这种疾病的组合更为重要。有更先进的分析方法可以解决这些和其他缺点。我们的研究的第一部分将开发数据分析方法。我们将回顾对疾病分组的不同统计方法的研究。我们将举办一个研讨会，邀请英国该领域的主要研究人员参加，试图就这类分析的最佳方法达成一致。在此基础上，我们将分析两个大型电子健康记录数据库，每个数据库都包括数百万患者。在每个数据库中，我们将确定共同发生的疾病组，并在另一个数据库中检查我们的发现。这被认为是分析中的良好做法。在这一步的最后，我们将制作软件来分析和发现电子健康记录中的疾病组，并免费提供给其他研究人员使用。我们研究的下一部分将使用来自两个大型调查的额外信息。这两项调查都包括健康记录中不一定提供的详细信息，例如职业、饮食、生活方式和虚弱程度。一个包括50万人，另一个有14年来相同人的信息。我们将描述不同疾病组合对患者的影响：他们的虚弱程度，因为它与社会护理的需要有关;进一步疾病的发展;药物治疗，卫生服务的使用和死亡。我们将与患者顾问合作，帮助指导对患者通过医疗服务的旅程进行分析。我们将调查多重死亡的可能原因，包括人们的社会环境，环境，生活方式（吸烟，酒精，饮食和运动）和可能有助于指示原因的实验室测试结果。这一步骤将指出环境和生活方式领域，作为可能的原因应进一步调查。

项目成果

Sex-specific temporal trends in the incidence and prevalence of cardiovascular disease in young adults: a population-based study using UK primary care data.

年轻人心血管疾病发病率和患病率的性别特异性时间趋势：一项使用英国初级保健数据的基于人群的研究。

• DOI: 10.1093/eurjpc/zwac024
• 发表时间: 2022
• 期刊: European journal of preventive cardiology
• 影响因子: 8.3
• 作者: Okoth K

A Bayesian semi-parametric model for thermal proteome profiling.

用于热蛋白质组分析的贝叶斯半参数模型。

• DOI: 10.17863/cam.71065
• 发表时间: 2021
• 作者: Fang S

In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm

在模拟数据和健康记录中，潜在类别分析是最佳的多病聚类算法

• DOI: 10.17863/cam.89191
• 发表时间: 2022
• 作者: Nichols L

Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England.

• DOI: 10.1371/journal.pmed.1004310
• 发表时间: 2023-11
• 期刊: PLoS medicine
• 影响因子: 15.8

In simulated data and health records, latent class analysis was the optimum multimorbidity clustering algorithm.

• DOI: 10.1016/j.jclinepi.2022.10.011
• 发表时间: 2022-12
• 期刊: Journal of clinical epidemiology
• 影响因子: 7.2
• 作者: Nichols L;Taverner T;Crowe F;Richardson S;Yau C;Kiddle S;Kirk P;Barrett J;Nirantharakumar K;Griffin S;Edwards D;Marshall T
• 通讯作者: Marshall T