Modeling and Forecasting Atherosclerotic Risk: A Complex Systems Approach

项目总结/摘要 与美国人的社会经济地位（SES）有关的健康结果的不平等正在上升： 布鲁金斯研究所发现，职业收入最高的10%的男性和女性的预期寿命超过10年 比最底层的10%的人要多。心血管疾病-仍然是美国人死亡的主要原因-值得研究 对于这些发现。 需要更多关于SES如何影响动脉粥样硬化风险的研究。我们项目的目标是开发先进的 动脉粥样硬化性心血管疾病（ASCVD）相关事件的预测算法-基线和 纵向-使用基于系统的建模方法，该方法结合了患者 社会经济和环境特征。这代表了一种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， 美国心脏病学会基金会和美国心脏协会（ACCF/AHA）仅基于 生理风险因素。我们认为ASCVD风险模型的预测性能可以显著提高 通过纳入社会经济和环境风险得到改善，特别是在一个改善初级和 二级预防和社会经济不平等的加剧导致了老年人中的复杂现象， 美国人ASCVD风险。 我们的初步工作表明，在主要ASCVD事件中， （心肌梗死、中风或心血管死亡），低SES社区与事件发生率相关， 是高社会经济地位社区的三倍。此外，社区SES解释了四倍的数量， ASCVD事件发生率的社区水平变异大于ACCF/AHA汇总队列方程解释的变异 风险模型。因此，我们建议的项目将为医疗保健系统提供一个重要的风险建模平台 重点是优化在社会经济和文化方面高度异质的人口的健康， 环境特征 这些模型将在基于团队的环境中开发，包括来自通用内部的翻译科学家。 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 和人口健康科学。为这些模型提供信息的将是一个新成立的、前沿的区域研究登记处， 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大都会健康中心的电子健康数据。 最终，这项研究有望产生新的机理见解和假设，更准确的预测模型 为心血管结局提供信息，并为多个战略和计划层面的决策提供依据。

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.