Preventing Sudden Cardiac Death: Harnessing the Power of Decision Analysis, Baye
预防心源性猝死:利用决策分析的力量,Baye
基本信息
- 批准号:7785845
- 负责人:
- 金额:$ 49.69万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2009
- 资助国家:美国
- 起止时间:2009-09-30 至 2012-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
DESCRIPTION (provided by the applicant): Sudden cardiac death (SCD), usually due to a ventricular tachyarrhythmia (rapid abnormal heart beat), is the most common cause of death in the United States accounting for up to 350,000 deaths per year. Recent clinical trials of patients considered at risk for SCD have demonstrated that the implantable cardioverter defibrillator (ICD) is the most effective therapy currently available. Although the overall mortality benefit from ICD therapy is evident, the magnitude of effectiveness of ICD therapy in clinically defined subgroups is unclear. Clinically, there are numerous unanswered questions related to ICDs and the prevention of SCD. Many of these questions are hoped to be explored through the use of the National ICD registry; others will require new clinical trials, and others may be evaluated through the combination of existing data sources. Formal methods for combining existing data, determining the value of obtaining additional information, comparing the effectiveness of current and novel treatments, and synthesizing evidence to aid clinicians and policymakers in their decision making are needed. Bayesian statistical approaches have been put forward as a method which enables policymakers to harness the power of the available sources of clinical evidence, explore subgroup effects within a trial and across trials in a methodologically rigorous manner, and to assess the uncertainty in clinical trial findings. In addition, these approaches can be incorporated into formalized decision making strategies. Our long-term goal is to enhance the ability of the Agency for Healthcare Research and Quality (AHRQ) to provide evidence-based decision making tools to aid providers and policymakers in the prevention of SCD. To achieve this overall goal, we have four specific aims: (1) To develop a generalizable decision modeling framework for the prevention of SCD; (2) To use Bayesian statistical techniques to devise a model for predicting patient and population health and economic outcomes; (3) To use the framework from Specific Aim 1, the Bayesian model from Specific Aim 2, and patient level data from existing clinical trials, to explore timely clinical and policy questions; and (4) To develop a web-based dissemination system to allow providers and policy makers to interact with the decision modeling framework and to explore clinical and policy questions as evidence evolves. Our research will build off our team's long-term research in chronic disease modeling, Bayesian statistical techniques in clinical trial design/ analysis, methods of disseminating evidence-based decision models to providers and policymakers, and the prevention of SCD. We will collaborate with principal investigators from 11 existing primary and secondary prevention of SCD trials to harness the power of patient level data from over two decades of clinical trials representing 8,200 patients. In an era in which great importance is placed on defending clinical practice with rigorous supporting evidence, our research brings together decision analytic methods, Bayesian statistical techniques, the strength of clinical trial data, and medical informatics tools to provide powerful methods to aid policy makers in their decision making.
描述(由申请人提供):心源性猝死(SCD),通常由室性快速性心律失常(快速异常心跳)引起,是美国最常见的死亡原因,每年死亡人数高达350,000人。最近的临床试验表明,植入式心律转复除颤器(ICD)是目前最有效的治疗方法。虽然ICD治疗的总体死亡率获益是明显的,但ICD治疗在临床定义的亚组中的有效性程度尚不清楚。在临床上,有许多与ICD和SCD预防相关的未回答的问题。这些问题中的许多问题希望通过使用国家ICD登记处进行探索;其他问题将需要新的临床试验,其他问题可能通过结合现有数据来源进行评估。需要正式的方法来结合现有的数据,确定获得额外信息的价值,比较当前和新的治疗方法的有效性,并综合证据以帮助临床医生和决策者做出决策。贝叶斯统计方法已被提出作为一种方法,使决策者能够利用现有的临床证据来源的力量,以严格的方法论方式探索试验内和试验间的亚组效应,并评估临床试验结果的不确定性。此外,这些方法可以纳入正式的决策策略。我们的长期目标是提高医疗保健研究和质量机构(AHRQ)提供循证决策工具的能力,以帮助提供者和政策制定者预防SCD。为了实现这一总体目标,我们有四个具体目标:(1)开发一个可推广的决策模型框架,以预防SCD;(2)使用贝叶斯统计技术,设计一个模型,预测病人和人口的健康和经济结果;(3)使用特定目标1的框架、特定目标2的贝叶斯模型和现有临床试验的患者水平数据,探索及时的临床和政策问题;(4)开发一个基于网络的传播系统,使供应商和政策制定者能够与决策建模框架进行互动,并随着证据的发展探索临床和政策问题。我们的研究将建立在我们团队在慢性病建模,临床试验设计/分析中的贝叶斯统计技术,向提供者和政策制定者传播循证决策模型的方法以及预防SCD方面的长期研究基础上。我们将与来自11个现有的SCD一级和二级预防试验的主要研究者合作,利用来自代表8,200名患者的20多年临床试验的患者水平数据的力量。在一个非常重视以严格的支持证据捍卫临床实践的时代,我们的研究汇集了决策分析方法,贝叶斯统计技术,临床试验数据的强度和医学信息学工具,以提供强大的方法来帮助决策者进行决策。
项目成果
期刊论文数量(0)
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{{ truncateString('GILLIAN D SANDERS SCHMIDLER', 18)}}的其他基金
Preventing Sudden Cardiac Death: Harnessing the Power of Decision Analysis, Baye
预防心源性猝死:利用决策分析的力量,Baye
- 批准号:
8118459 - 财政年份:2009
- 资助金额:
$ 49.69万 - 项目类别:
Preventing Sudden Cardiac Death: Harnessing the Power of Decision Analysis, Baye
预防心源性猝死:利用决策分析的力量,Baye
- 批准号:
7940953 - 财政年份:2009
- 资助金额:
$ 49.69万 - 项目类别:
COMPUTER BASED GUDIELINES TO PREVENT SUDDEN CARDIAC DEAT
基于计算机的预防心脏猝死的指南
- 批准号:
6391145 - 财政年份:2000
- 资助金额:
$ 49.69万 - 项目类别:
COMPUTER BASED GUIDELINES TO PREVENT SUDDEN CARDIAC DEAT
基于计算机的预防心源性猝死指南
- 批准号:
6942509 - 财政年份:2000
- 资助金额:
$ 49.69万 - 项目类别:
COMPUTER BASED GUIDELINES TO PREVENT SUDDEN CARDIAC DEAT
基于计算机的预防心源性猝死指南
- 批准号:
6283526 - 财政年份:2000
- 资助金额:
$ 49.69万 - 项目类别:
COMPUTER BASED GUDIELINES TO PREVENT SUDDEN CARDIAC DEAT
基于计算机的预防心脏猝死的指南
- 批准号:
6528220 - 财政年份:2000
- 资助金额:
$ 49.69万 - 项目类别:
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