Generating novel predictive models to estimate the risk of future ASCVD & Dementia in older adults

生成新的预测模型来估计未来 ASCVD 的风险

基本信息

批准号：
10302672
负责人：
Michael Nanna
金额：
$ 16.75万
依托单位：
YALE UNIVERSITY
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：
2021-09-01 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10302672
关键词：
Aging Alzheimer&apos s Disease Alzheimer&apos s disease risk Antihypertensive Agents Atherosclerosis Benefits and Risks Blood Pressure Calibration Cardiology Cardiovascular Diseases Caring Cause of Death Cognition Cognitive Communities Data Data Set Decision Making Dementia Diabetes prevention Discrimination Disease Outcome Elderly Electronic Health Record Epidemiology Etiology Event Foundations Freedom Funding Future Geriatrics Goals Grant Guidelines Hyperlipidemia Hypertension Impaired cognition Individual Internet Intervention Intervention Trial K-Series Research Career Programs Lipids Marital Status Measures Medical Mental Depression Mentorship Modeling Morbidity - disease rate National Heart, Lung, and Blood Institute Obesity Older Population Operative Surgical Procedures Patients Persons Population Prevalence Prevention Prevention therapy Preventive Preventive care Preventive therapy Preventive treatment Primary Prevention Public Health Reasons for Geographic And Racial Differences in Stroke Research Research Personnel Risk Risk Assessment Risk Estimate Risk Factors Smoking Socioeconomic Status Specialist Therapeutic Time Training United States Validation Visit Work aged atherosclerosis risk base blood pressure intervention blood pressure regulation cardiovascular disorder risk cholesterol control clinical care clinical practice clinical risk cognitive function cohort dementia risk disability evidence base functional status high risk interest mild cognitive impairment model building model development mortality novel older patient patient oriented predictive modeling preventive intervention randomized trial response risk prediction risk stratification shared decision making tool treatment strategy young adult

项目摘要

Response to Grants for Early Medical/Surgical Specialists' Transition to Aging Research (GEMSSTAR) Competition Title: Generating novel predictive models to estimate the risk of future ASCVD & Dementia in older adults Project Summary/Abstract: A person’s baseline risk determines to a large extent their anticipated benefit from many preventive treatments. Older patients desire to live longer while maintaining cognitive function and freedom from dementia, including Alzheimer’s disease, the #1 cause of morbidity and disability in older adults. Older adults also prioritize avoiding atherosclerotic cardiovascular disease (ASCVD), the #1 killer of older adults. Importantly, many risk factors for Alzheimer’s disease and dementia also increase risk for ASCVD. Alzheimer’s disease is the most common etiologic basis for incident mild cognitive impairment and dementia in older adults and can be identified as the cause in 70-75% of cases. Thus, providing older patients with personalized risk estimates for both dementia, including Alzheimer’s disease, and ASCVD could facilitate a comprehensive, evidence-based and patient-centered approach to therapeutic decision making in older adults. Unfortunately, current risk models were derived in younger adults, and fail to accurately predict risk in older adults. Second it remains unclear whether existing ASCVD risk models can also predict dementia risk and vice versa. Finally, to date, no one has evaluated whether these risk estimates help stratify therapeutic benefits of intervention in older adults. Leveraging a mentorship team of world experts in geriatrics, cardiology, and epidemiology, I will utilize data from subjects ≥75 years old from the National Heart, Lung, and Blood Institute (NHLBI) Pooled Cohorts in order to develop a clinical risk model to estimate risk of dementia, including Alzheimer’s disease, at 5 years from the selected baseline visit (Aim 1). In parallel, we will develop a clinical risk model to estimate the risk of ASCVD over the same time period in the same population of individuals ≥75 years old. In addition to traditional risk factors, we will derive these models using a novel set of candidate predictors not previously included in prior risk models including baseline cognition, functional status, depression, and mobility. Both models will then be externally validated using data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort (Aim 2). Finally, we will apply our model to patients ≥75 years old from the Systolic Blood Pressure Intervention Trial (SPRINT) in order to determine whether therapeutic benefit from intensive vs. conservative anti-hypertensive therapy in older adults differs across levels of predicted risk (Aim 3). Once developed and validated, we will develop an electronic health record-based version of the model for widespread dissemination and use in clinical care. The training I will receive through this work will give me expertise in model building and deployment and broaden my research interest in dementia including Alzheimer’s disease. It will also lay the groundwork for a future application for the Paul B. Beeson Emerging Leaders Career Development Award and other independent funding, with the ultimate goal of becoming an independent clinician-researcher focused on the care of older adults.

对早期医学/外科专家向老龄化研究过渡 (GEMSSTAR) 竞赛拨款的回应标题：生成新的预测模型来估计老年人未来 ASCVD 和痴呆症的风险项目摘要/摘要：一个人的基线风险在很大程度上决定了他们从许多预防性治疗中获得的预期益处，同时保持认知功能和免于痴呆症的困扰，其中包括阿尔茨海默病，这是老年人发病和残疾的第一大原因。避免动脉粥样硬化性心血管疾病 (ASCVD)，这是老年人的第一大杀手。重要的是，阿尔茨海默病和痴呆症的许多危险因素也会增加患阿尔茨海默病的风险，而阿尔茨海默病是最常见的病因。老年痴呆症（包括阿尔茨海默病）和 ASCVD 的个性化风险评估可以确定为 70-75% 病例的病因。不幸的是，当前的风险模型是在年轻人中得出的，无法准确预测老年人的风险。其次，目前尚不清楚现有的 ASCVD 风险模型是否也可以预测痴呆风险。最后，反之亦然。迄今为止，没有人评估这些风险估计是否有助于对老年人进行干预的治疗效果进行分层。利用由老年病学、心脏病学和流行病学领域的世界专家组成的指导团队，我将利用来自国家心脏中心的 75 岁以上受试者的数据。肺和血液研究所 (NHLBI) 汇集了队列，以开发临床风险模型来估计自选定的基线访视起 5 年后患痴呆症（包括阿尔茨海默病）的风险（目标 1）。与此同时，我们将开发一个临床风险模型来估计同一时期≥75 岁个体人群的 ASCVD 风险。除了传统的风险因素之外，我们将使用一组新的候选预测因子来推导这些模型。先前未包含在之前的风险模型中，包括基线认知、功能状态、抑郁和活动能力，然后将使用来自卒中地理和种族差异原因 (REGARDS) 队列的数据对这两个模型进行外部验证（目标 2）。最后，我们将我们的模型应用于收缩压干预试验 (SPRINT) 中≥75 岁的患者，以确定老年人强化抗高血压治疗与保守抗高血压治疗的治疗效果是否因预测风险水平的不同而不同（目的3).一旦开发和验证，我们将开发一个基于电子健康记录的模型版本，以便在临床护理中广泛传播和使用。我将通过这项工作接受的培训将为我提供模型构建和部署方面的专业知识，并拓宽我的视野。研究它也将为未来申请 Paul B. Beeson 新兴领袖职业发展奖和其他独立资助奠定基础，最终目标是成为专注于老年人护理的独立临床医生研究员。成年人。