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

基本信息

批准号：
10225340
负责人：
Aaron S Eisman
金额：
$ 5.35万
依托单位：
BROWN UNIVERSITY
依托单位国家：
美国
项目类别：
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/10225340
关键词：
Address Age Atherosclerosis Bayesian Modeling Bayesian Network Blood Pressure Calibration Cardiovascular Diseases Caring Cause of Death Centers for Disease Control and Prevention (U.S.)Clinical Clinical Research Data Development Diabetes Mellitus Disease Progression Effectiveness Electronic Health Record 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 Randomized Clinical Trials 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 System Telephone Testing Time Training United States Woman Work adjudication artificial neural network base biomedical informatics black patient cardiovascular disorder epidemiology cardiovascular disorder risk career clinical practice cohort computer science data exchange data mining data repository disability-adjusted life years genomic data health disparity improved markov model mathematical sciences mortality open source patient subsets polygenic risk score population based portability recruit risk prediction social health determinants success tool

项目摘要

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。动脉粥样硬化性心血管疾病（ASVD）是 CVD 发生的主要机制，疾病控制和预防中心基本上认为可以预防。降脂疗法是当前 ASCVD 预防性治疗的支柱和药物治疗指南依赖于 2013 年用于估计 10 年风险的合并队列方程 (PCE)。虽然这些方程已在在人群水平上，它们存在影响现实世界患者水平有效性的重大缺陷。这些包括实施（即临床医生将患者数据输入电话或网络的时间和精力）计算器），治疗改变对高度可变输入的敏感性（例如单时间点血压），对某些患者亚组的矛盾风险估计是线性模型的产物（例如女性吸烟者）、对种族的生硬对待（即针对黑人患者单独推导的方程）以及对种族的校准不佳现代队列（即导致风险高估）。该项目将尝试解决这些问题缺点。首先，罗德岛卫生局发现的用于分析电子健康记录的便携式工具将开发信息交换（HIE）来提取 PCE 风险因素，从而实现自动化 ASCVD 风险的计算。 PCE 风险因素提取排列（例如最后血压与平均血压）将是优化并且方程将针对总体进行校准。接下来，EHR 系统不可知工具提取医疗记录中可用的其他风险因素，包括症状发展、社会将制定健康的决定因素和家族史。 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，得到 ASCVD 的单核苷酸多态性 (SNP) 基因型数据遗传风险评分将与 HIE 衍生的风险因素相结合，以证明潜在的临床风险实施组学整合学习医疗保健系统的影响。该项目将作为为首席研究员提供基础培训，以实现该领域的医师科学家职业生涯生物医学信息学。假设：动脉粥样硬化性心血管疾病风险评估是当前降脂治疗的核心指导方针。该项目将检验以下假设：人口层面的数据驱动方法将改善风险计算器的准确性。目标 1：确定从纵向 HIE 数据导出的 PCE 风险因素的预测性能目标 2：定义基于人群的 ASCVD 风险表型簇目标 3：通过直接面向消费者的测序展示 HIE-Omics 集成学习医疗保健系统