权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Methods to Improve Personalized Cardiovascular Disease Prevention Across the Life Course

改善整个生命周期个性化心血管疾病预防的方法

基本信息

批准号：
9903441
负责人：
Lihui Zhao
金额：
$ 39.5万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9903441
关键词：
Algorithms Blood Pressure Cardiovascular Diseases Cessation of life Cholesterol Clinical Collaborations Communication Communities Complex Computerized Medical Record Data Data Pooling Data Sources Decision Making Event Heart failure Individual Joints Life Cycle Stages Life Expectancy Life Style Modification Mean Survival Times Measures Methods Modeling Morbidity - disease rate Myocardial Infarction Observational Study Online Systems Patient risk Patient-Focused Outcomes Patients Persons Pharmaceutical Preparations Play Prevention strategy Probability Process Randomized Controlled Trials Recording of previous events Relative Risks Reporting Research Personnel Risk Risk Factors Risk Reduction Risk-Benefit Assessment Role Stroke System Time Translating Vital Status Work adjudication base blood pressure reduction burden of illness cardiovascular disorder prevention cardiovascular disorder risk clinical practice cohort follow-up improved individualized prevention insight lifetime risk mortality novel patient oriented personalized medicine personalized predictions personalized risk prediction precision medicine prediction algorithm predictive modeling prevent risk prediction model shared decision making treatment adherence treatment effect

项目摘要

Title: Methods to Improve Personalized Cardiovascular Disease Prevention Across the Life Course Abstract: Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality. The overall objective of this proposal is to develop methods for improving personalized CVD prevention across the life course. CVD risk prediction plays a central role in clinical CVD prevention strategies, by aiding decision making for lifestyle modification and/or to match the intensity of therapy to the absolute risk of a given patient. The current risk prediction algorithms are generally based on the risk factors measured at a single time. Recently, we and others have shown that cumulative burden and trajectories of CV risk factors are independently associated with incident CVD. As risk factors like blood pressure are regularly collected in clinical practice, we propose to develop dynamic personalized prediction models for (1) short-term (e.g., 10-year) and lifetime risk of CVD and (2) life expectancy lived free of CVD and life expectancy lived with different subtypes of CVD across the life course using the history of time-varying CV risk factors. In addition, we will develop robust methods to improve the prediction of personalized blood pressure-lowering and cholesterol-lowering benefit with respect to CVD risk reduction as well as life expectancy lived free of CVD and life expectancy lived with CVD across the life course. The investigator team of this proposal has pooled the data from 20 community-based CVD cohorts through the Lifetime Risk Pooling Project (LRPP), which now has in excess of 25 years of follow-up data with repeated measured CVD risk factors, detailed information about medication use (including blood pressure- lowering and cholesterol-lowering therapy), nearly 100% follow-up for vital status, and detailed CVD event adjudication. Therefore, the LRPP provides a unique data source for our objective. We will validate the estimates for short-term personalized blood pressure-lowering and cholesterol-lowering treatment effects using the data from RCTs through our collaborations with the Blood Pressure Lowering Treatment Trialists' Collaboration and the Cholesterol Treatment Trialists' Collaboration, respectively. The consistency of the results would suggest adequate confounding adjustment and support the long-term personalized treatment effect estimates from LRPP which cannot otherwise be derived from RCTs data due to relatively short follow up.

标题：提高心脑血管疾病全生命周期个性化预防的方法摘要：心血管疾病(CVD)仍然是发病率和死亡率的主要原因。总的目标是这项建议是为了开发方法，以改善整个生命过程中的个性化心血管疾病预防。CVD 风险预测通过辅助生活方式的决策，在临床心血管疾病预防策略中发挥着核心作用。修改和/或使治疗强度与给定患者的绝对风险相匹配。当前的风险预测算法通常基于一次测量的风险因素。最近，我们和其他人则表明，累积负担和心血管风险因素的发展轨迹是独立相关的与突发心血管疾病有关。由于临床实践中经常收集血压等危险因素，我们建议为(1)心血管疾病的短期(例如，10年)和终生风险开发动态个性化预测模型，以及 (2)无心血管疾病和不同亚型心血管疾病患者的预期寿命课程使用了历史上时变的CV风险因素。此外，我们将开发稳健的方法来改进心血管疾病患者个性化降压降胆固醇效益的预测风险降低和无心血管疾病时的预期寿命以及一生中有心血管疾病时的预期寿命当然了。这项提案的研究团队汇集了20个社区心血管疾病队列的数据通过终身风险共享项目(LRPP)，该项目现在拥有超过25年的跟踪数据重复测量的心血管疾病危险因素，有关用药的详细信息(包括血压- 降低和降胆固醇治疗)，对生命状态进行近100%的随访，以及详细的心血管事件裁决。因此，LRPP为我们的目标提供了一个独特的数据源。我们将验证短期个性化降血压和降胆固醇治疗效果的评估通过我们与降压治疗试验者的合作来自随机对照试验的数据合作和胆固醇治疗试验者的合作。这些数据的一致性结果将建议进行适当的混杂调整，并支持长期的个性化治疗 LRPP的影响估计，由于跟踪时间相对较短，因此无法从RCT数据中得出向上。