New EHR-based multimorbidity index for diverse populations across the lifespan: development, validation, and application

针对不同人群整个生命周期的新的基于 EHR 的多病指数：开发、验证和应用

基本信息

批准号：
10720597
负责人：
Melissa Wei
金额：
$ 61.7万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-30 至 2028-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10720597
关键词：
Acute Adult Advisory Committees Affect Age Age Years Aging Algorithms Biological Markers COVID-19 COVID-19 mortality COVID-19 prognosis California Censuses Chronic Clinical Code Communities Data Data Linkages Data Set Development Disease Elderly Electronic Health Record Equipment and supply inventories Ethnic Origin Future General Population Goals Guidelines Health Health Policy Health system Hospitalization Immunization Intervention Life Cycle Stages Link Longevity Measurement Measures Methods Modeling Outcome Patient Care Patients Phenotype Physical Function Policies Policy Maker Polypharmacy Population Population Heterogeneity Population Policies Quality of Care Race Recommendation Research Research Personnel Resource Allocation Risk Risk Adjustment Standardization Surveys Testing Vaccinated Vaccines Validation Weight burden of illness clinical care clinically relevant cohort comorbidity coronavirus disease cost disability effective intervention electronic health data epidemiology study frailty functional status health care service utilization health data health management health related quality of life improved improved outcome indexing interest mortality mortality risk multiple chronic conditions patient oriented poor health outcome population health risk stratification sex shared decision making social deprivation tool unvaccinated virtual

项目摘要

Project Summary / Abstract Virtually all U.S. adults will develop multimorbidity (coexistence of multiple chronic conditions) by late adulthood. The sequelae are substantial: vulnerability to acute illness, disease exacerbation, hospitalization, disability, poor health-related quality of life, and mortality. Despite this, there are no viable, patient-centered measures for multimorbidity in the electronic health record (EHR) that include a comprehensive inventory of conditions based on their impacts on physical functioning in community-dwelling adults and are thus broadly applicable for the general population. The absence of such tools impedes systematic efforts to develop effective interventions for patients with multimorbidity. To bridge these gaps, this proposal aims to develop and validate a robust, clinically relevant, readily-available EHR-based multimorbidity-weighted index (eMWI) that accurately ascertains disease presence using EHR data and is applicable for diverse populations across the lifespan. The central hypothesis is that a comprehensive multimorbidity index that weights conditions based on their impacts on physical functioning can more precisely quantify multimorbidity and provide a better model fit to predict key health outcomes than prior measures. This hypothesis is strongly supported by our preliminary results using large national surveys and survey-linked claims data, in which we rigorously developed and validated a comprehensive set of 91 chronic conditions weighted by their average impacts on physical functioning over the disease life course, thus incorporating illness burden and physical functioning into a clinically meaningful measure applicable for the general population. As a transformative step for multimorbidity measurement in patient care, population health, and research using EHR data, the team aims to 1) improve multimorbidity measurement by more accurately ascertaining disease cases, and merging these with validated physical functioning disease weights to create a new patient-centered eMWI applicable to diverse populations; 2) assess the validity of eMWI via its association with key clinical outcomes: multimorbidity progression, hospitalization, and mortality; and 3) test the applicability of eMWI to national population health and policy by applying it to evaluate the risk of severe and fatal COVID-19 among vaccinated vs. unvaccinated adults based on their multimorbidity. This study uses large, diverse EHR data from 6 California health systems (>6 million adults) with unique data linkages to census data, and the largest, most nationally-representative National COVID Cohort Collaborative (N3C) dataset (>5 million COVID cases). The results will yield a new, validated, patient-centered multimorbidity index for EHR data – the eMWI – to help guide clinical decisions, population- health management, policy, and research for diverse populations. The team anticipates that eMWI can directly impact future practice and outcomes in which multimorbidity and functional status impact everyday treatment decisions and outcomes. eMWI will be readily available in standardized code for other EHR data. Overall, these results will improve the quality of care and health outcomes for diverse, aging adults with multimorbidity.

项目概要/摘要几乎所有美国成年人到了晚期都会患上多种疾病（多种慢性病共存）成年后的后遗症是巨大的：容易患急性疾病、病情恶化、住院、尽管如此，仍然没有可行的、以患者为中心的解决方案。电子健康记录 (EHR) 中的多发病措施，包括全面的清单条件基于其对社区居住成年人身体机能的影响，因此广泛适用于一般人群的工具的缺乏阻碍了系统性的开发工作。为了弥合这些差距，本提案旨在制定和实施针对多种疾病患者的有效干预措施。验证一个稳健的、临床相关的、易于使用的基于 EHR 的多发病加权指数 (eMWI) 使用 EHR 数据准确确定疾病存在，适用于世界各地的不同人群中心假设是一个综合的多重发病指数，根据条件进行加权。它们对身体机能的影响可以更精确地量化多种疾病并提供更好的模型拟合我们的初步结果有力地支持了这一假设。使用大型全国调查和与调查相关的索赔数据得出的结果，其中我们严格开发和验证了一组全面的 91 种慢性病，并按其对身体的平均影响进行加权在疾病生命过程中发挥功能，从而将疾病负担和身体功能纳入一个整体适用于普通人群的具有临床意义的措施作为多发病的变革性步骤。使用 EHR 数据测量患者护理、人口健康和研究，该团队的目标是 1) 改进通过更准确地确定疾病病例并将其与经过验证的病例合并来进行多发病测量身体功能疾病权重，以创建适用于不同人群的新的以患者为中心的 eMWI； 2) 通过与关键临床结果的关联来评估 eMWI 的有效性：多发病进展、住院率和死亡率；以及 3) 通过以下方式测试 eMWI 对国家人口健康和政策的适用性：它旨在评估疫苗与未接种疫苗的成人之间应用严重和致命的 COVID-19 的风险这项研究使用了来自 6 个加州卫生系统的大量、多样化的 EHR 数据（>600 万）。成人）与人口普查数据有独特的数据联系，并且是最大、最具全国代表性的国家 COVID 队列协作 (N3C) 数据集（超过 500 万例新冠病例）将产生一个新的、经过验证的、以患者为中心的 EHR 数据多病指数 - eMWI - 帮助指导临床决策、人群该团队预计 eMWI 可以直接为不同人群提供健康管理、政策和研究。影响未来的实践和结果，其中多发病和功能状态影响日常治疗 eMWI 将很容易以标准化代码形式提供给其他 EHR 数据。这些结果将改善患有多种疾病的多元化老年人的护理质量和健康结果。