Improving the Accuracy of ASCVD Risk Estimation Using Population-Level EHR and Genetic Data

使用人群水平 EHR 和遗传数据提高 ASCVD 风险评估的准确性

• 批准号: 10431891
• 负责人: Aaron S Eisman
• 金额: $ 5.29万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-10-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10431891
• 关键词: Address; Age; Applied Research; Atherosclerosis; Bayesian Modeling; Bayesian Network; Blood Pressure; Calibration; Cardiovascular Diseases; Caring; Cause of Death; Centers for Disease Control and Prevention (U.S.); Clinical; Data; Development; Diabetes Mellitus; Disease Progression; Effectiveness; Electronic Health Record; Epidemiology; Equation; Family; Foundations; Future; Genetic; Genetic Risk; Goals; Guidelines; Health; Health system; Healthcare Systems; Hyperlipidemia; Hypertension; Infrastructure; Learning; Linear Models; Link; Lipids; Logistic Regressions; Maps; Medical Records; Mentors; Meta-Analysis; Methods; Modeling; Modernization; Morbidity - disease rate; Morphologic artifacts; Natural Language Processing; Online Systems; Patient-Focused Outcomes; Patients; Performance; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Physicians; Population; Population Heterogeneity; Preventive therapy; Preventive treatment; Principal Investigator; Probability; Race; Recommendation; Recording of previous events; Research; Rhode Island; Risk; Risk Estimate; Risk Factors; SNP genotyping; Sampling; Scientist; Series; Single Nucleotide Polymorphism; Smoker; Smoking Status; Symptoms; Telephone; Testing; Time; Training; United States; Woman; Work; adjudication; artificial neural network; biomedical informatics; black patient; cardiovascular disorder risk; career; clinical practice; cohort; computer science; data exchange; data mining; data repository; disability-adjusted life years; electronic health record system; genomic data; health disparity; improved; markov model; mathematical sciences; mortality; open source; patient subsets; polygenic risk score; population based; portability; randomized, clinical trials; recruit; risk prediction; self organization; social health determinants; success; tool

SUMMARY Cardiovascular diseases (CVD) are the leading causes of morbidity and mortality in the United States. Atherosclerotic cardiovascular disease (ASVD) is the primary mechanism for the development of CVD and is largely considered preventable by the Center for Disease Control and Prevention. Lipid-lowering therapy is the current mainstay of preventative treatment for ASCVD and guidelines for pharmacotherapy rely on the 2013 Pooled Cohort Equations (PCE) for estimating 10-year risk. While these equations have been validated at a population level they have significant shortcomings that impact real-world patient-level effectiveness. These include implementation (i.e. time and effort for clinicians to enter patient data into a phone or web-based calculator), therapy changing sensitivity to highly variable inputs (e.g single time point blood pressure), paradoxical risk estimation for some patient subgroups that are an artifact of linear modeling (e.g. women smokers), blunt treatment of race (i.e. separately derived equations for black patients), and poor calibration for modern cohorts (i.e. resulting in the overestimation of risk). This project will attempt to address these shortcomings. First, portable tools for analyzing electronic health records found within the Rhode Island Health Information Exchange (HIE) will be developed for the extraction of PCE risk factors to enable the automated calculation of ASCVD risk. PCE risk factor extraction permutations (e.g. last vs mean blood pressure) will be optimized and the equations will be calibrated for the population. Next, EHR-system agnostic tools for extracting additional risk factors available within the medical record including symptom development, social determinants of health, and family history will be developed. PCE and non-PCE risk factors will be used for artificial neural network and dynamic Bayesian network modeling of ASCVD risk phenotype clusters to augment PCE risk prediction. Finally, a single nucleotide polymorphism (SNP) genotype data derived ASCVD genetic risk score will be integrated with the HIE derived risk factors to demonstrate the potential clinical implications of implementing an omics-integrated learning healthcare system. The project will serve as foundational training for the principal investigator towards pursuing a career as a physician-scientist in the field of biomedical informatics. Hypothesis: Atherosclerotic cardiovascular disease risk estimation is central to current lipid-lowering therapy guidelines. This project will test the hypothesis that population-level data-driven methods will improve the accuracy of risk calculators. Aim 1: Determine the Predictive Performance of PCE Risk Factors Derived from Longitudinal HIE Data Aim 2: Define Population-Based ASCVD Risk Phenotype Clusters Aim 3: Demonstrate HIE-Omics-Integrated Learning Healthcare System with Direct-to-Consumer Sequencing

概括 心血管疾病（CVD）是美国发病率和死亡率的主要原因。 动脉粥样硬化心血管疾病（ASVD）是CVD发展的主要机制，IS 在很大程度上被疾病控制与预防中心可以预防。降脂疗法是 当前针对ASCVD预防治疗的主要支柱和药物治疗指南依赖于2013年 汇总队列方程（PCE）用于估计10年风险。尽管这些方程已在 人口水平，他们存在重大缺陷，影响现实世界中的患者水平效果。这些 包括实施（即临床医生将患者数据输入电话或基于Web的时间和精力 计算器），治疗改变对高度可变输入的敏感性（例如，单个时间点血压）， 对于某些是线性建模伪像的患者亚组的矛盾风险估计（例如 吸烟者），种族的直率治疗（即黑人患者的分别得出的方程式），校准较差 现代人群（即导致风险高估）。该项目将尝试解决这些问题 缺点。首先，用于分析罗德岛健康中的电子健康记录的便携式工具 信息交换（HIE）将开发用于提取PCE风险因素以实现自动化的风险因素 ASCVD风险的计算。 PCE风险因素提取排列（例如，最后与平均血压）将是 优化并将为人群校准方程。接下来，EHR-System不可知论工具 提取病历中可用的其他风险因素，包括症状发展，社会 将开发健康和家族史的决定因素。 PCE和非PCE风险因素将用于 ASCVD风险表型簇的人工神经网络和动态贝叶斯网络建模 增强PCE风险预测。最后，单个核苷酸多态性（SNP）基因型数据得出的ASCVD 遗传风险评分将与HIE得出的风险因素整合在一起，以证明潜在的临床 实施OMICS集成的学习医疗系统的含义。该项目将作为 首席研究人员的基础培训，致力于从事该领域的医师科学家职业 生物医学信息学。 假设：动脉粥样硬化心血管疾病风险估计对于当前降脂疗法至关重要 指南。该项目将检验以下假设，即人群级数据驱动的方法将改善 风险计算器的准确性。 目标1：确定纵向HIE数据得出的PCE风险因素的预测性能 AIM 2：定义基于人群的ASCVD风险表型群集 AIM 3：通过直接到消费者测序演示HIE-OMICS集成的学习医疗系统