Toward Accurate Cardiovascular Disease Prediction in Hispanics/Latinos: Modeling Risk and Resilience Factors

项目摘要/摘要 现有的心脏病和中风预测模型(例如，Framingham)往往高估了 西班牙裔/拉丁裔(H/L)S。这种不准确具有重大的经济和公共卫生影响，与 不准确的监测、干预目标和医疗管理。模型的不准确性可能源于 在模型开发和验证工作中普遍存在H/L代表不足的情况。因此，传统的 心血管疾病(CVD)的危险因素可能是特定于其衍生人群的， 并且不能推广到H/L。此外，可能存在唯一的H/L疾病决定因素 未经检验的或未合并的导致预测错误的。重要的是，韧性因素，如文化上- 适度的社会资本可能对了解这一人群中的风险至关重要。解决这些差距将 有助于更好地了解心血管疾病风险，并对针对性干预战略产生相应影响。 这份K99/R00马赛克提案将使用二级数据为当前的10年期心血管疾病风险模型提供信息， 理论和数据驱动方法，以提高H/L中CVD预测模型的精度。拟议中的培训 PLAN为研究H/L心血管疾病风险和复原力因素的职业奠定了坚实的基础。培训 Plans利用亚利桑那大学的大量资源和一支由高级内容组成的指导团队 专家。应聘者将获得以下技能：1)在H/L CVD差异方面的专业知识；2)在 动脉粥样硬化性疾病的心血管疾病流行病学、风险、病因学和病理生理学，3)应用机器 风险预测模型的学习、交叉验证和选择，以及4)文化因素和社会资本 影响H/L心血管疾病。研究建议有三个目标，重点是评估和告知现有的10个- 以H/L表示的年CVD预测。使用西班牙裔社区健康研究/研究的二手数据 拉丁裔(HCHS/SOL)，候选人将(目标1-K99)评估当前10年的预测准确性 使用具有显著不同H/L代表性的大H/L样本(HCHs/SOL)的心血管疾病风险模型。(目标2 -R00)候选人将使用可用的数据来确定一组改善风险预测的目标风险因素 在H/L中。(AIM 3-R00)候选人将测试是否在心血管疾病风险中增加社会韧性因素 模型将提高对这一群体的预测精度。机器学习将被用于识别有效的 拉丁裔10年心血管疾病的预测因素。社会复原力组件将捕获 使用数据简化方法的社会环境(例如，配偶、家庭、邻居)。拟议的研究 建议采用心血管健康的整体观点来阐明风险和复原力因素 不断壮大的民族群体。

PROJECT SUMMARY/ABSTRACT Existing heart disease and stroke prediction models (e.g., Framingham) tend to overestimate risk for Hispanics/Latinxs (H/L)s. This inaccuracy has significant economic and public health impacts associated with inaccurate surveillance, intervention targeting, and medical management. Model inaccuracies likely stem from pervasive underrepresentation of H/Ls in model development and validation efforts. Consequently, traditional risk factors for cardiovascular disease (CVD) may be specific to the populations upon whom they were derived, and not generalizable to H/Ls. In addition, there may be unique disease determinants for H/Ls that remain untested or unincorporated leading to error in prediction. Importantly, resilience factors such as culturally- moderated social capital may be critical to understanding risk in this population. Addressing these gaps will lead to better understanding of CVD risk with corresponding implications for targeted intervention strategies. This K99/R00 MOSAIC proposal will use secondary data to inform current 10-year CVD risk models using theory and data-driven methods to increase CVD prediction model accuracy in H/Ls. The proposed training plan establishes a solid foundation for a career investigating H/L CVD risk and resilience factors. The training plan leverages substantial resources at The University of Arizona and a mentoring team of senior content experts. The candidate will gain the following, 1) expertise in H/L CVD disparities, 2) advanced knowledge in CVD epidemiology, risk, and etiology and pathophysiology of atherosclerotic disease, 3) applied machine learning, cross-validation, and selection of risk prediction models, and 4) cultural factors and social capital influencing H/L CVD. The research proposal has three aims focused on evaluating and informing existing 10- year CVD prediction in H/Ls. Using secondary data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), the candidate will (Aim 1 – K99) evaluate the prediction accuracy of current 10-year CVD risk models using a large H/L sample with significant representation of diverse H/Ls (HCHS/SOL). (Aim 2 – R00) the candidate will use available data to identify a group of target risk factors that improve risk prediction in H/Ls. (Aim 3 – R00) the candidate will test whether adding a social resilience component to CVD risk models will improve their prediction accuracy for this group. Machine learning will be used to identify valid predictors of 10-year CVD in Latinos. The social resilience component will capture the multi-dimensionality of social environments (e.g. spouse, family, neighborhood) using data reduction methods. The proposed research proposal adopts a holistic view of cardiovascular health to elucidate both risk and resilience factors in this growing ethnic group.