Advanced Development and Utilization of Assembled Aging Trajectory Files from Multiple Datasets

来自多个数据集的组装老化轨迹文件的高级开发和利用

基本信息

批准号：
10225864
负责人：
Olga F. Jarrín Montaner
金额：
$ 103.05万
依托单位：
RUTGERS BIOMEDICAL/HEALTH SCIENCES-RBHS
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10225864
关键词：
Activities of Daily Living Acute Advanced Development Affect Age Age-Years Aging Air Pollution Alzheimer&apos s disease related dementia Area Bathing Behavioral Behavioral Mechanisms Biological COVID-19 COVID-19 diagnosis Cancer Institute of New Jersey Caregiver Burden Caring Censuses Chronic Chronic Kidney Failure Clinical Clinical Sciences Cognition Disorders Collaborations Community Healthcare Complex Data Data Set Development Diabetes Mellitus Diagnosis Disease Disseminated Malignant Neoplasm Distal Documentation Eating Economics Elderly End stage renal failure Environmental Exposure Exposure to Genes Geography Goals HIV Health Health Policy Healthcare Healthcare Systems Heart failure Home Impaired cognition Incontinence Independent Living Individual Institutes Institutional Racism Institutionalization Insurance Intervention Life Maintenance Maps Measures Medicare Mental Depression Methodology Methods Mission Modeling Neighborhoods New Jersey Outcome Outcomes Research Pain Pathway interactions Patients Pattern Persons Physical Function Policies Pollution Population Population Heterogeneity Predictive Factor Process Provider Quality of Care Recording of previous events Recovery Recovery of Function Research Research Infrastructure Research Personnel Resources Risk Factors Science Shapes Societies Source Code Sterile coverings Stroke Surveys Syndrome System Testing Time Translational Research United States National Institutes of Health Variant Ventilator Walking Work analytical method base beneficiary built environment care costs cohort cost estimate data infrastructure data repository decubitus ulcer disability economic determinant experience falls functional decline functional disability functional independence functional status health care availability health care quality health disparity health inequalities hospice environment improved indexing informal care infrastructure development innovation inpatient service interest learning algorithm machine learning algorithm machine learning method mortality multiple datasets outcome prediction patient oriented patient population person centered premature repository resilience respiratory response risk sharing sex social social culture socioeconomics therapy design

项目摘要

PROJECT SUMMARY This goal of this project is to create a unique and comprehensive research repository of aging trajectory da- tasets, related resources, and analytic methods that can be used to answer new and important questions in aging and related sciences. Specifically, by harmonizing and merging multiple data sets this project will gener- ate the data infrastructure needed to understand change over time in care settings, geriatric syndromes, physi- cal functioning, and shared risk factors at multiple levels (patient, provider, community, healthcare system, and society) and across multiple domains (biological, behavioral, sociocultural, and physical/built environments) including chronic conditions and history of acute illness such as COVID-19, exposure to air pollution, neighbor- hood socioeconomic, and health care system factors (Aim 1). Analytic strategies will be developed for user- defined cohorts and their propensity score-matched controls, e.g., older adults who were living with chronic conditions including Alzheimer's disease and related dementias (ADRD), diabetes, heart failure, end-stage re- nal disease, metastatic cancer, and HIV. State-of-the-art analytic methods are used to identify patterns of ag- ing trajectories (care setting, geriatric syndromes, physical functioning) experienced by older adults during the final years of life and their association with shared risk factors and distal outcomes (Aim 2). From the assem- bled trajectory file in Aim 1, cohorts are derived by aligning an originating index time such as age cutoff point and time at diagnosis (e.g., ADRD, stroke, chronic kidney disease). Both a model-based approach and ma- chine learning algorithms are then used to discover multilevel and potentially interactive predictors of trajecto- ries (e.g., rapid functional decline in independent living beneficiaries) and specific outcomes (e.g., respiratory ventilator usage among Medicare beneficiaries diagnosed with COVID-19) (Aim 3). The unique resources are then shared to disseminate resources including datasets, documentation, source code, and methodology (Aim 4). At the end of this project, the research infrastructure to investigate the relationship between shared risk factors and aging trajectories will be ready to use and replicate, giving investigators unprecedented ability to solve new challenges in aging science. This will allow researchers to understand the underlying processes and systems associated with reversible periods of disability across care settings, and interventions that may be used to support recovery of function and reduction of geriatric syndromes including cognitive decline, for the purpose of reducing burdensome care transitions, and maintenance of functional independence. This project will also create the resources and methods needed to evaluate the impact of innovations and interventions im- plemented at the patient, provider, community, healthcare system, and society/policy levels to improve care quality and outcomes for older adults.

项目概要该项目的目标是创建一个独特且全面的衰老轨迹数据研究库可用于回答新的重要问题的测试集、相关资源和分析方法老龄化及相关科学。具体来说，通过协调和合并多个数据集，该项目将生成需要了解护理环境、老年综合症、身体状况随时间的变化所需的数据基础设施钙功能，以及多个层面（患者、提供者、社区、医疗保健系统和社会）和跨多个领域（生物、行为、社会文化和物理/建筑环境）包括慢性病和急性病史，例如 COVID-19、暴露于空气污染、邻居社会经济和医疗保健系统因素（目标 1）。将为用户制定分析策略定义的队列及其倾向得分匹配的对照，例如患有慢性病的老年人疾病包括阿尔茨海默氏病和相关痴呆症（ADRD）、糖尿病、心力衰竭、终末期再治疗癌症、转移性癌症和艾滋病毒。最先进的分析方法用于识别 ag- 的模式老年人在治疗期间经历的轨迹（护理环境、老年综合症、身体机能）生命的最后几年及其与共同危险因素和远端结果的关系（目标 2）。从装配- 目标 1 中的 bleed 轨迹文件，通过对齐原始索引时间（例如年龄截止点）来导出队列以及诊断时间（例如 ADRD、中风、慢性肾脏病）。基于模型的方法和ma- 然后使用网络学习算法来发现轨迹的多级且潜在的交互预测因子条件（例如，独立生活受益人的功能迅速下降）和具体结果（例如，呼吸系统疾病）诊断患有 COVID-19 的 Medicare 受益人使用呼吸机）（目标 3）。独特的资源有然后共享以传播资源，包括数据集、文档、源代码和方法（目标 4).在该项目结束时，研究基础设施将调查分担风险之间的关系因素和衰老轨迹将随时可供使用和复制，使研究人员能够获得前所未有的能力解决衰老科学的新挑战。这将使研究人员能够了解基本过程并与跨护理环境的可逆残疾期相关的系统，以及可能的干预措施用于支持功能恢复和减少老年综合症（包括认知能力下降）目的是减少繁重的护理过渡，并保持功能独立性。这个项目还将创建评估创新和干预措施影响所需的资源和方法在患者、提供者、社区、医疗保健系统和社会/政策层面实施，以改善护理老年人的质量和结果。