Integrating Risk Trajectories and Social Determinants to Enhance Cardiovascular Risk Assessment in Older Adults

项目摘要：心血管疾病(CVD)是美国残疾和死亡的主要原因 而且，老年人患病的风险最大。半个多世纪以来，心血管疾病风险 计算器已经被用来估计一个人患心血管疾病的风险，并指导治疗策略 预防疾病。不幸的是，当前指南推荐的工具在老年人和 在弱势群体中。对这些缺点提出的理由有两个。首先， 现有的模型没有考虑风险因素的变化--例如，胆固醇、血压等 与年龄相关的血管功能下降和/或预防性治疗的使用而发生。第二，现有的 模型没有考虑到健康的社会决定因素(SDOH)和其他非临床因素 显示改善了不同人口群体的风险预测。如果不解决这些问题，一个至关重要的 预防心血管疾病的工具将仍然是次优的，并有机会减少成人心血管疾病的发展 那些疾病风险最大的人将仍然是紧急的和得不到满足的。我们的中心假设是心血管疾病风险 在老年人中，通过同步可以大大改善预测及其对疾病预防的转换 在考虑SDOH和其他因素的同时，多种已建立的风险因素之间的年龄相关变化 增加心血管疾病风险。使用具有全国代表性的数据(健康和退休研究[HRS])并汇集 社区队列(社区动脉粥样硬化风险[ARIC]，心血管健康研究[CHS]， 动脉粥样硬化的多种族研究[MESA]，FHS[FHS]原始，和FHS后代)，我们的目标是 三个方面：首先，我们将使用基于群体的轨迹模型来对年龄相关的变化进行分类 在具有全国代表性的老年人样本中，心血管疾病的主要危险因素(65)。结果将会是 使用外部(集合队列)数据进行验证，以确定与年龄相关的最有效和最可靠的分类 多个既定风险因素之间的变化。其次，我们将根据以下数据预测心血管疾病的发生 风险因素的多轨迹分布。通过考虑风险因素和轨迹组成员的变化 随着年龄的增长，这种方法将反映出现实世界的临床环境，在那里，后续措施将成为 在常规护理中可用，患者风险因素的变化会转化为更大(或更小)的风险 CVD事件。第三，我们将研究广泛的社会经济、心理社会和行为因素 确定SDOH和其他与个体风险状况和心血管疾病结局相关的关键因素。的主要目标是 这项提议与美国国立卫生研究院(NIH)的使命高度一致，目的是更好地了解 跨学科的老龄化动力学以预防老年人疾病。我们的研究响应了这一号召，使用了 多个数据集和创新方法，以优化老年人心血管疾病风险评估。利用这一集成 新的知识，我们的研究将有很大的潜力更好地为设计、目标和时机提供信息 在多样化和老龄化的美国人口中改善心血管疾病一级预防的临床干预措施。

PROJECT SUMMARY: Cardiovascular disease (CVD) is a leading cause of disability and death in the United States; and the risks for disease are greatest among older adults. For more than half a century, CVD risk calculators have been used to estimate a person’s risk of developing CVD and to guide treatment strategies to prevent disease. Unfortunately, the tool recommended by current guidelines performs poorly in older adults and among dis/advantaged population groups. The proposed reasons for these shortcomings are twofold. First, the existing model does not account for changes in risk factors—e.g., cholesterol, blood pressure (BP), etc.—that occur with age-related declines in vascular function and/or the use of preventive therapies. Second, the existing model does not account for social determinants of health (SDOH) and other non-clinical factors that have been shown to improve risk prediction across diverse population groups. Without addressing these issues, a vital tool for CVD prevention will remain suboptimal and opportunities to reduce the development of CVD in adults who are at greatest risk of disease will remain urgent and unmet. Our central hypothesis is that CVD risk prediction and its translation to disease prevention can be greatly improved in older adults by synchronizing age-related changes among multiple established risk factors while accounting for SDOH and other factors that contribute to CVD risks. Using nationally-representative data (Health and Retirement Study [HRS]) and pooled community-based cohorts (Atherosclerosis Risk in Communities [ARIC], Cardiovascular Health Study [CHS], Multiethnic Study of Atherosclerosis [MESA], Framingham [FHS] Original, and FHS Offspring), our aims are threefold: First, we will use group-based trajectory models to classify age-related changes within and among the major risk factors for CVD in a nationally-representative sample of older adults (65+). Results will then be validated with external (pooled cohort) data to identify the most efficient and robust classifications of age-related changes among the multiple established risk factors. Second, we will predict the onset of CVD based on the multi-trajectory profiles of risk factors. By accounting for changes in risk factors and trajectory group-membership with advancing age, this approach will mirror a real-world clinical setting where follow-up measures become available during routine care and a patient’s changes in risk factors translate into greater (or lesser) risks for a CVD event. Third, we will examine a wide array of socioeconomic, psychosocial, and behavioral factors to identify SDOH and other key factors related to individual risk profiles and CVD outcomes. The major aims of this proposal are highly aligned with the National Institutes of Health’s (NIH) mission to better understand the interdisciplinary dynamics of aging to prevent disease in older adults. Our study responds to this call by using multiple datasets and innovative methods to optimize CVD risk assessment in older adults. With this integrated new knowledge, our study will have significant potential to better inform the design, targeting, and timing of clinical interventions to improve the primary prevention of CVD in a diverse and aging U.S. population.