Modeling and Forecasting Atherosclerotic Risk: A Complex Systems Approach

动脉粥样硬化风险建模和预测：复杂的系统方法

• 批准号: 9287398
• 负责人: JARROD DALTON
• 金额: $ 58.27万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9287398
• 关键词: Accounting; Address; Affect; Affordable Care Act; African American; Age; Algorithms; American; American Heart Association; Assessment tool; Atherosclerosis; Behavior; Behavioral; Cardiology; Cardiovascular Diseases; Cardiovascular Models; Cardiovascular system; Caring; Cause of Death; Censuses; Cessation of life; Characteristics; Climate; Clinic; Clinical; Communities; Community Developments; Complex; Data; Dimensions; Disease Outcome; Disease Progression; Economics; Effectiveness; Elderly; Environment; Environmental Risk Factor; Equation; Event; Food Access; Foundations; Goals; Guidelines; Health; Health Food; Health Sciences; Health Services Accessibility; Health and Retirement Study; Health system; Healthcare Systems; Heart Diseases; Household; Hybrids; Immunology; Individual; Inequality; Institution; Internal Medicine; Lead; Life Expectancy; Light; Location; Low income; Measures; Methodology; Methods; Modeling; Myocardial Infarction; Nature; Neighborhoods; Ohio; Outcome; Participant; Patients; Performance; Physiological; Population; Preventive care; Primary Prevention; Provider; Quality of life; Registries; Research; Risk; Risk Assessment; Risk Factors; Secondary Prevention; Social Work; Socioeconomic Status; Stress; Stroke; System; Time; Translating; United States; Variant; Weather; Woman; Work; aged; atmospheric sciences; base; cardiovascular risk factor; career; caucasian American; cohort; college; contextual factors; cost; disorder risk; experience; falls; health data; heart disease risk; improved; insight; low socioeconomic status; men; patient population; population health; prevention service; profiles in patients; residence; socioeconomics; spatial epidemiology; support tools; tool; translational scientist; urban poverty

Project Summary/Abstract Inequality in health outcomes in relation to Americans' socioeconomic status (SES) is rising: a recent study by the Brookings Institution found that life expectancy for men and women in the top 10% of career earnings was over 10 years greater than those in the bottom 10%. Cardiovascular disease – still leading cause of death for Americans – merits study with respect to these findings. More research on how SES affects atherosclerotic risk is needed. The goal of our project is to develop advanced forecasting algorithms for atherosclerotic cardiovascular disease (ASCVD)-related events – both at baseline and longitudinally – using systems-based modeling methodologies which incorporate probabilistic representations of patients' socioeconomic and environmental characteristics. This represents a paradigm shift beyond existing models used in guiding primary and secondary prevention of atherosclerotic disease; in particular, risk models developed by the American College of Cardiology Foundation and the American Heart Association (ACCF/AHA) are based solely on physiological risk factors. We believe that the prediction performance of ASCVD risk models can be significantly improved by incorporating socioeconomic and environmental risks, especially in an era where improved primary and secondary prevention and increased socioeconomic inequality have resulted in complex phenomena among elderly Americans with respect to ASCVD risk. Our preliminary work indicates a significant degree of neighborhood-level variability in major ASCVD events (myocardial infarction, stroke or cardiovascular death), with low-SES neighborhoods associated with event rates over three times that of high-SES neighborhoods. Moreover, neighborhood SES explained four times the amount of neighborhood-level variation in ASCVD event rates than that explained by the ACCF/AHA Pooled Cohort Equations Risk Model. Our proposed project will therefore provide an essential risk modeling platform to health care systems focused on optimizing the health of populations that are highly heterogeneous with respect to socioeconomic and environmental characteristics. These models will be developed in a team-based environment, including translational scientists from general internal medicine, cardiology, social work, spatial epidemiology, urban poverty, community development, immunology, and data and population health sciences. Informing the models will be a newly-established, cutting-edge regional research registry, based on electronic health data from Northeast Ohio's two largest health systems, Cleveland Clinic and MetroHealth. Ultimately, this research is anticipated to yield new mechanistic insights and hypotheses, more accurate prediction models for cardiovascular outcomes, and a basis for informing decisions at multiple strategic and programmatic levels.

项目概要/摘要 与美国人社会地位 (SES) 相关的健康结果的不平等正在加剧：一项最近的研究 布鲁金斯学会发现，职业收入最高 10% 的男性和女性的预期寿命超过 10 年 心血管疾病仍然是美国人的主要死因，值得研究。 关于这些发现。 需要对 SES 如何影响动脉粥样硬化风险进行更多研究。我们项目的目标是开发先进的技术。 动脉粥样硬化性心血管疾病 (ASCVD) 相关事件的预测算法 - 无论是在基线还是在 纵向——使用基于系统的建模方法，其中结合了患者的概率表示 这代表了超越现有模型的范式转变。 指导动脉粥样硬化疾病的一级和二级预防；特别是由 美国心脏病学会基金会和美国心脏协会 (ACCF/AHA) 仅基于 我们认为 ASCVD 风险模型的预测性能可以显着提高。 通过纳入社会经济和环境风险来改善，特别是在初级和环境改善的时代 二级预防和社会经济不平等的加剧导致老年人出现复杂的现象 美国人对于 ASCVD 风险的看法。 我们的初步工作表明，主要 ASCVD 事件存在显着的邻里层面变异性 （心肌梗塞、中风或心血管死亡），SES 较低的社区与事件发生率相关 是高 SES 社区的三倍，而且，社区 SES 解释的数量是高 SES 社区的四倍。 ASCVD 事件发生率的邻里水平变化高于 ACCF/AHA 合并队列方程所解释的变化 因此，我们提出的项目将为医疗保健系统提供一个重要的风险建模平台。 专注于优化社会经济和社会方面高度异质的人群的健康 环境特征。 这些模型将在基于团队的环境中开发，包括来自一般内部的转化科学家 医学、心脏病学、社会工作、空间流行病学、城市贫困、社区发展、免疫学和数据 为模型提供信息的将是一个新建立的尖端区域研究登记处， 基于俄亥俄州东北部两个最大的卫生系统克利夫兰诊所和 MetroHealth 的电子健康数据。 最终，这项研究预计将产生新的机制见解和假设，以及更准确的预测模型 心血管结局，以及在多个战略和计划层面提供决策依据的基础。