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

项目总结/摘要 现有的心脏病和中风预测模型（例如，（1）倾向于高估风险 西班牙裔/拉丁裔（H/L）这种不准确性具有重大的经济和公共卫生影响， 不准确的监测，干预目标和医疗管理。模型不准确可能源于 模型开发和验证工作中H/L的普遍代表性不足。因此，传统 心血管疾病（CVD）的危险因素可能对它们所来源的人群是特异性的， 并且不能推广到H/L。此外，可能存在H/L的独特疾病决定因素， 未经测试或未合并，导致预测错误。重要的是，复原力因素，如文化- 适度的社会资本对于理解这一人群的风险可能至关重要。解决这些差距将 从而更好地了解心血管疾病的风险与相应的有针对性的干预策略的影响。 本K99/R 00 MOSAIC提案将使用二级数据为当前的10年CVD风险模型提供信息， 理论和数据驱动的方法，以提高高/低CVD预测模型的准确性。拟议的培训 该计划为研究H/L CVD风险和弹性因素的职业生涯奠定了坚实的基础。培训 该计划利用亚利桑那大学的大量资源和高级内容指导团队 专家候选人将获得以下内容，1）H/L CVD差异的专业知识，2）高级知识， 心血管疾病的流行病学、危险性、动脉粥样硬化疾病的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; 4）文化因素和社会资本 影响H/L CVD。该研究提案有三个目标，重点是评估和通报现有的10- 年CVD预测（H/L）。使用西班牙裔社区健康研究/研究的次要数据， 拉丁美洲人（HCHS/SOL），候选人将（目标1 - K99）评估当前10年的预测准确性 CVD风险模型使用大量H/L样本，具有不同H/L的显著代表性（HCHS/SOL）。(Aim 2 - R 00）候选人将使用可用数据来确定一组可改善风险预测的目标风险因素 在H/L。(Aim 3 -R 00）候选人将测试是否将社会弹性成分添加到CVD风险中 模型将提高对该群体的预测准确性。机器学习将用于识别有效的 拉丁美洲人10年心血管疾病的预测因子。社会复原力部分将涵盖以下多层面问题： 社会环境（如配偶，家庭，邻里）使用数据简化方法。拟议研究 该提案采用了心血管健康的整体观点，以阐明心血管疾病的风险和弹性因素， 日益壮大的民族。

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.