Preventing Sudden Cardiac Death: Harnessing the Power of Decision Analysis, Baye

描述(由申请人提供)：心脏性猝死(SCD)，通常由室性快速心律失常(快速异常心跳)引起，是美国最常见的死亡原因，每年高达350,000人死亡。最近对有SCD风险的患者进行的临床试验表明，植入型心律转复除颤器(ICD)是目前可用的最有效的治疗方法。尽管ICD治疗对总体死亡率的好处是显而易见的，但ICD治疗在临床定义的亚组中的有效程度尚不清楚。在临床上，有关ICD和SCD的预防有许多悬而未决的问题。其中许多问题希望通过使用国家ICD注册来探索；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，比较当前和新疗法的有效性，并合成证据以帮助临床医生和政策制定者进行决策。贝叶斯统计方法已被提出，作为一种方法，它使决策者能够利用可用的临床证据来源的力量，以严格的方法探索试验内和试验之间的亚组效应，并评估临床试验结果的不确定性。此外，这些方法还可以并入正式的决策策略。我们的长期目标是加强卫生保健研究和质量机构(AHRQ)的能力，为预防SCD的援助提供者和政策制定者提供循证决策工具。为了实现这一总体目标，我们有四个具体目标：(1)开发预防SCD的通用决策建模框架；(2)使用贝叶斯统计技术设计预测患者和人群健康和经济结果的模型；(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；以及(4)开发一个基于网络的传播系统，使提供者和政策制定者能够与决策建模框架互动，并随着证据的演变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SCD预防方面的长期研究基础上。我们将与11个现有的SCD一级和二级预防试验的主要研究人员合作，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。在一个非常重视用严格的证据来捍卫临床实践的时代，我们的研究将决策分析方法、贝叶斯统计技术、临床试验数据的强度和医学信息学工具结合在一起，为决策者的决策提供强有力的方法。

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.