Use of Registries, Claims and Health System Data to Enhance the Evaluation of Cardiovascular Therapies in Clinical Trials

项目概要/摘要 及时有效地评估新的医疗策略、疗法和设备至关重要， 改善公共卫生。在患者的常规护理中收集的无处不在的数据，包括登记、健康 系统和支付者，仍然是未充分利用的信息来源，可用于支持临床试验 新的医疗干预措施的评价，但需要验证。通过与史密斯的合作， 贝斯以色列女执事医疗中心，哈佛临床研究所， 哈佛大学Chan公共卫生学院，美国心脏病学院（ACC），Premier，Inc.， Symphony Health Solutions和美敦力，我们将联合收割机从两个里程碑式的临床试验项目中获得数据- 双重抗血小板治疗（DAPT）研究和美国CoreValve试验-综合国家数据 从登记处，卫生系统和付款人，以测试几个假设的核心，以未来的评估， 心血管治疗在目标1中，我们将检验试验结局来自关联登记研究的假设， 医院和索赔数据将等同于临床试验中获得的结果，这表明这些 在某些情况下，替代数据源可以取代传统的临床试验数据。在目标2中，我们将测试 假设通过登记处、医院和索赔收集的信息 原始临床试验，如先前非心脏病住院治疗和虚弱标记，可以增加试验数据， 通过改进风险分层和识别异质性治疗效果。在目标3中，我们 利用试验与基于人群的数据集的联系， 评估总体试验普遍性和对研究不足亚组的适用性的适用指标。的 这些不同资源的联系将创造一个重要的机会，正式评估的优势， 使用非临床试验数据源支持和改善医疗器械临床试验评估的局限性 治疗所学到的信息有可能为今后开展务实和 自然主义的临床试验，同时提供新的见解的疗效和安全性的疗法，以治疗冠状动脉 和心脏瓣膜病

PROJECT SUMMARY/ABSTRACT The timely and efficient evaluation of new medical treatment strategies, therapies and devices is critical to improving public health. Ubiquitous data collected in the routine care of patients, including registries, health systems and payers, remain underutilized sources of information that could be used to support clinical trial evaluations of novel medical interventions, but require validation. Through a collaboration involving the Smith Center for Outcomes Research at Beth Israel Deaconess Medical Center, Harvard Clinical Research Institute, the Harvard T.H. Chan School of Public Health, the American College of Cardiology (ACC), Premier, Inc., Symphony Health Solutions and Medtronic, we will combine data from two landmark clinical trial programs - the Dual Antiplatelet Therapy (DAPT) Study and the US CoreValve Trials - with comprehensive national data from registries, health systems and payers, to test several hypotheses central to the future evaluation of cardiovascular therapies. In Aim 1, we will test the hypothesis that trial outcomes derived from linked registry, hospital, and claims data will be equivalent to outcomes obtained in clinical trials, suggesting that these alternative data sources can, in some cases, replace traditional clinical trial data. In Aim 2, we will test the hypothesis that information collected through registries, hospitals, and claims that was not collected in the original clinical trial, such as prior non-cardiac hospitalizations and markers of frailty, can augment trial data, through improved risk stratification and identification of heterogeneous treatment effects. In Aim 3, we will leverage the linkage of trials with population-based datasets to enable the creation of novel and broadly applicable metrics to assess overall trial generalizability and applicability to understudied subgroups. The linkage of these diverse resources will create an important opportunity to formally evaluate the strengths and limitations of using non-clinical trial data sources to support and improve clinical trial assessments of medical therapies. The information learned has the potential to greatly inform the future conduct of pragmatic and naturalistic clinical trials, while offering novel insights into the efficacy and safety of therapies to treat coronary and valvular heart disease.