Phenotyping Heart Failure through Analysis of Secondary Data

项目摘要 该项目的主要目标是利用范围广泛的大型协调数据资源 通过使用机器学习(ML)开发和测试复杂模型来预测心力衰竭(HF)患者 临床结果，并根据病理生理学确定可能在临床上重要的HF表型。 预后和治疗反应。我们将通过对25项临床试验的二次分析来实现这一点，6 大型流行病学研究和总计约130,000名心力衰竭患者的电子健康记录数据。其中&gt； 40,270个来自21个BioLINCC数据集，43,536个来自行业赞助的研究，45,763个来自 啊哈。通过利用关于人口、设计、时间框架和数据来源的各种研究，我们设想 我们的表型将更多地反映现实世界临床中遇到的患者的谱系 B)能够与常规收集的临床数据更一致地进行识别。改进 根据心力衰竭表型对结果进行表征可能反过来促进心力衰竭的个性化 在治疗和治疗目标方面的管理。我们假设预测性和表型 使用这些资源生成的模型将在一系列数据源和 临床人群。这项建议的主要重叠目标是： 1.使用来自25个已完成临床试验的74,308名患者的数据来表征存活率和 根据简单特征、预测模型和复杂情况进行治疗反应 表型。我们将监督和非监督最大似然方法应用于该数据集 迄今为止对心力衰竭临床试验数据的最大个体患者数据荟萃分析。然后我们将比较 这些模型对已建立的模型的预测价值使用常规回归和 生存分析。 2.验证Aim 1的模型，探索新的表型，并描述相关的临床 来自观察队列的9,734例心力衰竭患者诊断前的特征。 使用6项大型研究的数据，如弗雷明翰心脏研究，我们将验证已建立的模型 和AIM 1的模型。我们还将确定在临床试验和 试图确定在发生特定的心力衰竭表型之前的临床危险因素。 3.验证45,763名心力衰竭患者的表型特征、相关性和预后 来自科罗拉多大学临床数据的追溯电子健康记录(EHR)数据 仓库。我们将使用这些测试来测试在AIMS 1和2中导出的所有预测性和患者表型模型 协调真实世界的数据，并再次确定在其他数据集中没有很好代表的表型。 由于临床实践中已知的健康差异，我们将根据患者的情况描述护理模式 可能影响结果的表型。

PROJECT ABSTRACT The primary goal of this project is to leverage large, harmonized data resources comprised of a broad range of patients with heart failure (HF) by using machine learning (ML) to develop and test complex models to predict clinical outcomes and identify HF phenotypes that may be clinically important based on pathophysiology, prognosis, and treatment response. We will accomplish this through secondary analysis of 25 clinical trials, 6 large epidemiologic studies, and electronic health record data totaling ~ 130,000 patients with HF. Of these > 40,270 are derived from 21 BioLINCC datasets, 43,536 from industry-sponsored studies and 45,763 from the EHR. By utilizing a variety of studies with respect to population, design, timeframe, and data source, we envisage that our phenotypes will be a) more reflective of the spectrum of patients encountered in real world clinical practice and b) able to be identified more consistently with routinely collected clinical data. Improved characterization of outcomes according to HF phenotype may in turn facilitate personalization of HF management both in terms of therapies and treatment goals. We hypothesize that predictive and phenotyping models generated using these resources will outperform existing models across a range of data sources and clinical populations. The primary overlapping Aims of this proposal are: 1. Use data from 74,308 patients in 25 completed clinical trials to characterize survival and treatment response according to simple characteristics, predictive models, and complex phenotypes. We apply both supervised and unsupervised ML methods to this dataset in one of the largest individual patient data meta-analyses of HF clinical trial data to date. We will then compare the predictive value of these models to established models derived using conventional regression and survival analysis. 2. Validate models from Aim 1, explore novel phenotypes, and describe associated clinical characteristics prior to HF diagnosis in 9,734 patients with incident HF from observational cohorts. Using data from 6 large studies such as the Framingham Heart Study, we will validate established models and models from Aim 1. We will also identify major phenotypes not well represented in clinical trials and attempt to identify clinical risk factors that precede development of specific HF phenotypes. 3. Validate phenotype characteristics, associations, and outcomes in 45,763 patients with HF using retrospective electronic health record (EHR) data from the University of Colorado's clinical data warehouse. We will test all predictive and patient phenotype models derived in Aims 1 and 2 using these harmonized real-world data and again identify phenotypes not well-represented in other the datasets. Because of known health disparities in clinical practice, we will describe care patterns according to patient phenotype that may impact outcomes.