Integrated Preventive Cardiology Initiative

综合预防心脏病学倡议

• 批准号: 9761319
• 负责人: RODNEY A. HAYWARD
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9761319
• 关键词: Age; Antihypertensive Agents; Area; Back; Blood Pressure; Calibration; Cardiology; Caring; Cause of Death; Cerebrovascular Disorders; Cessation of life; Chronic; Chronic Care; Chronic Disease; Clinical; Clinical Data; Code; Data; Data Set; Decision Making; Diagnosis; Discrimination; Disease Outcome; Electroconvulsive Therapy; Elements; Event; Goals; Guidelines; Health Expenditures; Heart Diseases; Individual; Integrated Learning System; Kidney Diseases; Lipids; Markov Chains; Mathematics; Measurement; Metabolic; Methodology; Methods; Modeling; Morbidity - disease rate; Myocardial Infarction; National Heart, Lung, and Blood Institute; Nature; Outcome; Patient risk; Patients; Pattern; Policies; Polypharmacy; Population; Premature Mortality; Prevention; Preventive; Preventive treatment; Primary Prevention; Process; Quality of life; Risk; Risk Factors; Risk Reduction; Risk stratification; Science; Sensitivity and Specificity; Series; Stroke; Testing; Time; Uncertainty; United States; Veterans; Work; alternative treatment; base; blood pressure medication; cardiovascular disorder prevention; design; disorder prevention; disorder risk; evidence base; health record; improved; indexing; longitudinal analysis; mortality; mortality disparity; outcome prediction; randomized trial; shared decision making; study population; tool; treatment choice; treatment effect; treatment guidelines; treatment strategy

Summary page In addition to their profound impact of quality of life, seven of the top ten leading causes of death in the United States in 2010 were chronic conditions, and 86% of health care expenditures were for patients with one or more chronic diseases. A common feature of most chronic disease care is that decision-making is not just a matter of whether to intervene, but when the optimal time to intervene is and which of the available treatments should be tried first. This task becomes even more difficult when there are multiple competing treatments directed at multiple different target outcomes. The VA is reaching a critical point in its ability to develop integrated learning systems into the care of chronic conditions. Clinically-detailed data now dates back almost 15 years and the computing power to use it clinically is now available. This proposal describes the work for the Integrated Preventive Cardiology Initiative (IPCI), which seeks to improve care for the prevention of cardio-cerebrovascular disease (CVD) with an underlying goal of making theoretical and methodological advancing models for integrated chronic disease treatment strategies. CVD is an ideal model for this goal. CVD is not just important in its own right (the leading cause of both morbidity and mortality in VA, the nation and now, worldwide, and a leading cause of ethnic and SES mortality disparities), but CVD has excellent evidence for benefit from multiple treatments which influence multiple target conditions. Further, the risk factors for the different target conditions (heart attacks, stroke, CHF, renal disease) and treatment effects on these outcomes vary substantially. Yet guidelines remain fairly simplistic, without integration of blood pressure (BP), lipid and ASA guidelines. To examine these issues, we developed a multi- faceted study with 3 Specific Aims. Aim1: Examine the degree to which longitudinal baseline patient data improves prediction of overall CVD risk—the key determinant of statin’s and BP medication’s absolute risk reduction. Aim2: Develop and validate methods for adjusting estimates of effect sizes, model calibration, and model discrimination for measurement error in EHR-derived predictor and outcome variables. Aim3: Estimate how the timing, order, and intensity of treatment impact CVD absolute risk reduction within an integrated CVD prevention framework. This 4-year study is designed to substantively improve primary CVD treatment choices, by dramatically advancing how we use existing historical clinical data and integrating the alternative treatment options by analyzing their strengths, weaknesses, and their differential impact on various CVD outcomes. In Aim 1 we will analyze 13-years of longitudinal EHR data on Veterans age 45 to 80 using data from national VA datasets, the National Death Index, CMS data and focused chart reviews. We will test a series of hypotheses trying to understand the relationships of risk factors to different CVD risks and to improve patients’ risk stratification, a key factor for estimating absolute risk reduction. Aim 2 will test the validity and possibility for improvement of the findings of Aim 1. Extensive chart reviews will help estimate the sensitivity and specificity of using EHR diagnosis codes for identifying hard CVD events, and be check the calibration of the risk prediction tool. In Aim 3 we will build a Markov Decision Process model to evaluate an integrated optimal approach to considering anti-hypertensive, lipid-lowering and anti-platelet therapy simultaneously based on expected absolute risk reduction from treatment. We will model the progression of metabolic factors using a Markov Chain model, using a multi-way probabilistic sensitivity analysis to evaluate the effects of uncertainty in model input parameters. Our fully developed model of this Integrated Preventive Cardiology Initiative (IPCI) will be able to examine numerous clinically important questions and hypotheses, informing current policies, shared decision-making and areas important for future research.

摘要页面 除了对生活质量的深刻影响外， 美国在2010年的慢性病，86%的医疗保健支出是为患者一个 或更多的慢性疾病。大多数慢性病护理的一个共同特点是，决策不仅仅是一个 是否进行干预的问题，但何时进行干预的最佳时机以及可用的治疗方法 应该先审判。当存在多种竞争性治疗时，这项任务变得更加困难 针对多个不同的目标结果。退伍军人事务部的发展能力正处于一个关键点， 将学习系统整合到慢性病的护理中。临床详细数据现在几乎可以追溯到 15年了，现在可以在临床上使用它的计算能力。 该提案描述了综合预防心脏病倡议（IPCI）的工作，该倡议旨在 改善预防心脑血管疾病的护理，其根本目标是使 综合慢性病治疗策略的理论和方法学先进模型。CVD是 这是实现这一目标的理想模式。心血管疾病不仅仅是其本身的重要性（发病率和死亡率的主要原因）。 弗吉尼亚州的死亡率，国家和现在，世界范围内，和种族和社会经济地位死亡率差异的主要原因）， 但是CVD有很好的证据表明从影响多个目标条件的多种治疗中获益。 此外，不同目标病症（心脏病发作、中风、CHF、肾病）的风险因素和 对这些结果的治疗效果差异很大。然而，指导方针仍然相当简单， 血压（BP）、血脂和阿萨指南的整合。为了研究这些问题，我们开发了一个多- 研究有三个具体目标。 目的1：检查纵向基线患者数据改善总体CVD预测的程度 风险-他汀类药物和BP药物绝对风险降低的关键决定因素。 目标2：开发和验证调整效应量估计值、模型校准和模型的方法 EHR衍生的预测变量和结果变量的测量误差的歧视。 目的3：估计治疗的时间、顺序和强度如何影响CVD绝对风险降低， 一个综合的CVD预防框架。 这项为期4年的研究旨在通过显着改善主要CVD治疗选择， 推进我们如何使用现有的历史临床数据，并整合替代治疗方案， 分析他们的优势，弱点，以及他们对各种CVD结果的不同影响。 在目标1中，我们将使用以下数据分析45至80岁退伍军人13年的纵向EHR数据： 国家退伍军人管理局数据集、国家死亡指数、CMS数据和重点图表审查。我们将测试一系列 这些假设试图了解危险因素与不同CVD风险的关系，并改善患者的 风险分层，估计绝对风险降低的关键因素。目标2将检验其有效性和可行性 改进目标1的结果。广泛的图表审查将有助于估计灵敏度和 使用EHR诊断代码识别硬CVD事件的特异性，并检查 风险预测工具。在目标3中，我们将建立一个马尔可夫决策过程模型来评估一个综合的最优方案。 同时考虑降压、降脂和抗血小板治疗的方法， 预期治疗的绝对风险降低。我们将使用一个 马尔可夫链模型，使用多路概率敏感性分析来评估不确定性的影响， 模型输入参数我们全面开发的综合预防心脏病倡议（IPCI）模型将 能够检查许多临床上重要的问题和假设，为当前的政策提供信息，共享 决策和未来研究的重要领域。