Phenotyping Heart Failure through Analysis of Secondary Data

通过二手数据分析对心力衰竭进行表型分析

• 批准号: 10581057
• 负责人: David Peter Kao
• 金额: $ 11.61万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10581057
• 关键词: Artificial Intelligence; Biological Markers; Biological Models; Cardiac; Cessation of life; Characteristics; Clinical; Clinical Data; Clinical Research; Clinical Trials; Colorado; Complex; Coupled; Data; Data Set; Data Sources; Development; Diagnosis; Disease; Electronic Health Record; Event; Framingham Heart Study; Functional disorder; Future; Goals; Guidelines; Health system; Heart failure; Hospitalization; Individual; Industry; Intervention; Knowledge; Left Ventricular Ejection Fraction; Linear Models; Machine Learning; Medical; Meta-Analysis; Metadata; Methods; Modeling; Observational Study; Outcome; Patient Selection; Patients; Patterns of Care; Performance; Phenotype; Physiological; Population; Predictive Value; Prognosis; Regression Analysis; Resources; Risk Factors; Survival Analysis; Testing; Universities; Validation; clinical application; clinical data warehouse; clinical practice; clinical risk; cohort; comorbidity; data harmonization; data resource; design; diverse data; epidemiology study; health disparity; improved; individual patient; insight; machine learning method; machine learning model; novel; patient population; point of care; predict clinical outcome; prediction algorithm; predictive modeling; randomized, clinical trials; secondary analysis; study population; supervised learning; survival prediction; targeted treatment; treatment response; unsupervised learning; virtual

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.

项目摘要 该项目的主要目标是利用大型，协调的数据资源，包括广泛的范围 通过使用机器学习（ML）开发和测试复杂模型以预测心力衰竭（HF）的患者（HF） 临床结果并确定基于病理生理学的HF表型，这些表型在临床上很重要， 预后和治疗反应。我们将通过对25次临床试验的二次分析，6 大型流行病学研究和电子健康记录数据总计约130,000名HF患者。这些> 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 EHR。通过利用有关人口，设计，时间范围和数据源的各种研究，我们设想 我们的表型将是a）更反映现实世界中临床遇到的患者 练习和b）能够通过常规收集的临床数据更稳定地识别。改进 根据HF表型的结局表征反过来促进HF的个性化 在治疗和治疗目标方面的管理。我们假设这种预测性和表型 使用这些资源生成的模型将胜过各种数据源的现有模型， 临床人群。该提案的主要重叠目的是： 1。在25项完成的临床试验中使用74,308名患者的数据来表征生存和 根据简单特征，预测模型和复杂的治疗反应 表型。我们将监督和无监督的ML方法应用于此数据集之一 迄今为止，HF临床试验数据的最大个人患者数据荟萃分析。然后，我们将比较 这些模型对使用常规回归和的建立模型的预测价值 生存分析。 2。从AIM 1验证模型，探索新表型并描述相关的临床 HF诊断之前的特征在观察队列的9,734例出现HF患者中。 使用来自弗雷明汉心脏研究等6个大型研究的数据，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 试图确定在特定HF表型发展之前的临床风险因素。 3。使用45,763例HF患者验证表型特征，关联和结果 来自科罗拉多大学临床数据的回顾性电子健康记录（EHR）数据 仓库。我们将使用这些测试AIM 1和2中得出的所有预测性和患者表型模型 在其他数据集中，统一的现实世界数据并再次识别表型未得到很好的代表。 由于临床实践中已知的健康差异，我们将根据患者描述护理模式 可能影响结果的表型。