Dynamic and personalized prediction of complex cardiovascular events.

复杂心血管事件的动态和个性化预测。

• 批准号: 10360163
• 负责人: Yifei Sun
• 金额: $ 12.15万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-03 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10360163
• 关键词: Address; Cardiovascular Diseases; Cardiovascular Models; Cardiovascular system; Clinical; Cohort Studies; Complex; Data; Data Analyses; Dependence; Disease; Event; Future; Incidence; Individual; Intervention; Measures; Medical Research; Methodology; Methods; Modeling; National Heart, Lung, and Blood Institute; Outcome; Prevention strategy; Recording of previous events; Recurrence; Research; Risk; Risk Factors; Specific qualifier value; Structure; Testing; Time; Validation; Visit; cohort; flexibility; follow-up; forest; high risk; learning strategy; novel; personalized predictions; personalized risk prediction; random forest; risk prediction; statistical learning; tool; treatment strategy

PROJECT SUMMARY Statistical learning methods such as random forests have proven useful in medical research. With the availability of massive biomedical and event history data collected during the course of diseases, dynamic and personalized risk prediction of future clinical events can provide valuable information to identify high-risk individuals and initiate timely treatments or interventions. Our application is motivated by the NHLBI Pooled Cohorts Study, where risk factors were measured intermittently at follow-up visits, and multiple cardiovascular disease (CVD) events could occur during the follow-up period. Existing statistical learning methods usually focus on time to the first event with baseline predictors; methods that can handle the second and subsequent clinical events or repeatedly measured time-dependent risk factors are lacking. We develop flexible random forest methods for multiple event data, where the complex event history information is fully utilized without pre-specifying the dependence structure of different events. The proposed methods can deal with the case where events are of different degrees of clinical importance and competing risks exist. The methodology will be applied to the pooled cohorts to build accurate risk prediction tools and to identify important risk factors for both CVD incidence and recurrence. We will conduct validation analysis to test whether novel statistical learning methods can outperform existing methods such as Cox-type models; we will also use forest models to provide guidance in building Cox-type models for CVD recurrence. The proposed research has the potential to advance dynamic and personalized risk prediction and to facilitate more effective prevention and treatment strategies for CVD recurrence.

项目摘要 随机森林等统计学习方法已被证明在医学研究中很有用。与可用性 在疾病过程中收集的大量生物医学和事件历史数据，动态和个性化 未来临床事件的风险预测可以提供有价值的信息，以识别高风险个体并启动 及时治疗或干预。我们的申请是由NHLBI汇总队列研究的动机，其中风险 在随访时间歇性测量因素，多个心血管疾病（CVD）事件可能 发生在随访期间。现有的统计学习方法通常侧重于第一个事件的时间， 基线预测因子;可以处理第二次和后续临床事件或重复测量的方法 缺乏与时间相关的风险因素。我们为多事件数据开发了灵活的随机森林方法，其中 充分利用了复杂事件历史信息，而无需预先指定不同事件的依赖结构， 事件所提出的方法可以处理事件具有不同程度的临床重要性的情况 竞争的风险存在。该方法将应用于合并队列，以建立准确的风险预测 工具，并确定CVD发病率和复发的重要风险因素。我们将进行验证 分析，以测试新的统计学习方法是否可以优于现有的方法，如Cox型 模型;我们还将使用森林模型为CVD复发建立Cox型模型提供指导。的 拟议的研究有可能推进动态和个性化的风险预测， 有效预防和治疗CVD复发的策略。