Modeling and Forecasting Atherosclerotic Risk: A Complex Systems Approach

项目摘要/摘要 与美国人的社会经济地位(SES)相关的健康结果不平等正在加剧：The 布鲁金斯学会发现，职业收入前10%的男性和女性的预期寿命都在10年以上 比排名靠后10%的人要高。心血管疾病-仍然是美国人的主要死亡原因-值得研究 关于这些发现。 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-在基线和 纵向-使用基于系统的建模方法，其中包括患者的概率表示 社会经济和环境特征。这代表着一种范式的转变，超越了 指导动脉粥样硬化性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 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而得到改善，特别是在一个主要和 二级预防和日益加剧的社会经济不平等导致了老年人中的复杂现象 美国人对ASCVD的风险。 我们的初步工作表明，在主要的ASCVD事件中，邻里水平的可变性程度很大 (心肌梗死、中风或心血管死亡)，与事件发生率相关的低SES社区超过 是高社保社区的三倍。此外，邻居SES解释的数量是 社区水平的ASCVD事件发生率的变化比ACCF/AHA合并队列方程所解释的 风险模型。因此，我们提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社会经济方面高度不同的人群的健康 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。通知模型的将是一个新建立的、尖端的区域研究登记机构， 基于俄亥俄州东北部两个最大的医疗系统克利夫兰诊所和MetroHealth的电子健康数据。 最终，这项研究有望产生新的机械性见解和假设，更准确的预测模型 对于心血管结果，以及在多个战略和方案层面上为决策提供信息的基础。

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.