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由广泛的 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 临床结果并基于病理生理学鉴定可能具有临床重要性的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目标， 大型流行病学研究和总计约130，000例HF患者的电子健康记录数据。其中> 其中40，270份来自21个BioLINCC数据集，43，536份来自行业赞助的研究，45，763份来自 电子病历通过利用关于人口，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a）更能反映真实的临床中遇到的患者谱， 实践和B）能够与常规收集的临床数据更一致地识别。改进 根据HF表型的结果表征反过来可以促进HF的个性化 在治疗方法和治疗目标方面的管理。我们假设预测和表型分析 使用这些资源生成的模型将在一系列数据源中优于现有模型， 临床人群。这项建议的主要目标是： 1.使用25项已完成临床试验中74，308例患者的数据来描述生存率， 根据简单特征、预测模型和复杂 表型我们将监督和无监督ML方法应用于其中一个数据集， 迄今为止最大的HF临床试验数据的个体患者数据荟萃分析。然后我们将比较 这些模型对使用常规回归得出的已建立模型的预测值， 生存分析 2.研究目标1中的模型，探索新的表型，并描述相关的临床 在来自观察性队列的9，734例发生HF的患者中进行HF诊断前的特征分析。 我们将使用来自6项大型研究的数据，如心脏病研究，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中没有很好代表的主要表型， 试图确定发生特定HF表型之前的临床风险因素。 3. 45，763例HF患者的表型特征、相关性和结局， 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方法测试目标1和目标2中得出的所有预测和患者表型模型。 协调现实世界的数据，并再次识别在其他数据集中没有得到充分代表的表型。 由于临床实践中已知的健康差异，我们将根据患者描述护理模式 可能影响结果的表型。