Suppr超能文献

伦敦多元现代人群心血管健康与风险因素调查(TOGETHER研究):纵向医疗记录链接方案

Investigation of Cardiovascular Health and Risk Factors Among the Diverse and Contemporary Population in London (the TOGETHER Study): Protocol for Linking Longitudinal Medical Records.

作者信息

Dharmayat Kanika, Woringer Maria, Mastellos Nikolaos, Cole Della, Car Josip, Ray Sumantra, Khunti Kamlesh, Majeed Azeem, Ray Kausik K, Seshasai Sreenivasa Rao Kondapally

机构信息

Department of Primary Care and Public Health, Imperial Centre for Cardiovascular Disease Prevention, Imperial College London, London, United Kingdom.

Cardiovascular Sciences Research Centre, St George's, University of London, London, United Kingdom.

出版信息

JMIR Res Protoc. 2020 Oct 2;9(10):e17548. doi: 10.2196/17548.

Abstract

BACKGROUND

Global trends in cardiovascular disease (CVD) exhibit considerable interregional and interethnic differences, which in turn affect long-term CVD risk across diverse populations. An in-depth understanding of the interplay between ethnicity, socioeconomic status, and CVD risk factors and mortality in a contemporaneous population is crucial to informing health policy and resource allocation aimed at mitigating long-term CVD risk. Generating bespoke large-scale and reliable data with sufficient numbers of events is expensive and time-consuming but can be circumvented through utilization and linkage of data routinely collected in electronic health records (EHR).

OBJECTIVE

We aimed to characterize the burden of CVD risk factors across different ethnicities, age groups, and socioeconomic groups, and study CVD incidence and mortality by EHR linkage in London.

METHODS

The proposed study will initially be a cross-sectional observational study unfolding into prospective CVD ascertainment through longitudinal follow-up involving linked data. The government-funded National Health System (NHS) Health Check program provides an opportunity for the systematic collation of CVD risk factors on a large scale. NHS Health Check data on approximately 200,000 individuals will be extracted from consenting general practices across London that use the Egton Medical Information Systems (EMIS) EHR software. Data will be analyzed using appropriate statistical techniques to (1) determine the cross-sectional burden of CVD risk factors and their prospective association with CVD outcomes, (2) validate existing prediction tools in diverse populations, and (3) develop bespoke risk prediction tools across diverse ethnic groups.

RESULTS

Enrollment began in January 2019 and is ongoing with initial results to be published mid-2021.

CONCLUSIONS

There is an urgent need for more real-life population health studies based on analyses of routine health data available in EHRs. Findings from our study will help quantify, on a large scale, the contemporaneous burden of CVD risk factors by geography and ethnicity in a large multiethnic urban population. Such detailed understanding (especially interethnic and sociodemographic variations) of the burden of CVD risk and its determinants, including heredity, environment, diet, lifestyle, and socioeconomic factors, in a large population sample, will enable the development of tailored and dynamic (continuously learning from new data) risk prediction tools for diverse ethnic groups, and thereby enable the personalized provision of prevention strategies and care. We anticipate that this systematic approach of linking routinely collected data from EHRs to study CVD can be conducted in other settings as EHRs are being implemented worldwide.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/17548.

摘要

背景

心血管疾病(CVD)的全球趋势存在显著的区域间和种族间差异,这反过来又影响了不同人群的长期CVD风险。深入了解种族、社会经济地位与CVD风险因素及死亡率之间在同一时期人群中的相互作用,对于制定旨在降低长期CVD风险的卫生政策和资源分配至关重要。生成具有足够事件数量的定制大规模可靠数据既昂贵又耗时,但可以通过利用和关联电子健康记录(EHR)中常规收集的数据来规避这一问题。

目的

我们旨在描述不同种族、年龄组和社会经济群体中CVD风险因素的负担情况,并通过EHR关联研究伦敦的CVD发病率和死亡率。

方法

拟开展的研究最初将是一项横断面观察性研究,通过纵向随访(涉及关联数据)发展为前瞻性CVD确诊研究。政府资助的国家医疗服务体系(NHS)健康检查计划为大规模系统整理CVD风险因素提供了机会。将从伦敦各地使用埃格顿医疗信息系统(EMIS)EHR软件且同意参与的全科诊所中提取约20万人的NHS健康检查数据。将使用适当的统计技术对数据进行分析,以(1)确定CVD风险因素的横断面负担及其与CVD结局的前瞻性关联,(2)在不同人群中验证现有的预测工具,以及(3)针对不同种族群体开发定制的风险预测工具。

结果

招募工作于2019年1月开始,目前仍在进行,初步结果将于2021年年中公布。

结论

迫切需要基于对EHR中可用的常规健康数据进行分析的更多实际人群健康研究。我们研究的结果将有助于大规模量化一个多民族城市大群体中按地理位置和种族划分的CVD风险因素的同期负担。在一个大的人群样本中,对CVD风险负担及其决定因素(包括遗传、环境、饮食、生活方式和社会经济因素)有如此详细的了解(尤其是种族间和社会人口统计学差异),将能够为不同种族群体开发量身定制的动态(不断从新数据中学习)风险预测工具,从而实现预防策略和护理的个性化提供。我们预计,随着EHR在全球范围内的实施,这种将EHR中常规收集的数据进行关联以研究CVD的系统方法可以在其他环境中进行。

国际注册报告识别码(IRRID):PRR1-10.2196/17548

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3844/7568219/737b16263a27/resprot_v9i10e17548_fig1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验