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

项目摘要/摘要 及时和有效地评估新的医疗治疗策略、疗法和设备对于 改善公共卫生。在患者常规护理中收集的无处不在的数据，包括登记表、健康状况 系统和付款人，仍然没有充分利用可用于支持临床试验的信息来源 对新的医疗干预措施的评估，但需要验证。通过史密斯夫妇的合作 贝丝以色列女执事医学中心结果研究中心，哈佛临床研究所， 哈佛大学公共卫生学院、美国心脏病学会(ACC)、Premier，Inc. 我们将结合来自两个具有里程碑意义的临床试验项目的数据- 双重抗血小板治疗(DAPT)研究和美国CoreValve试验--综合国家数据 来自登记机构、卫生系统和付款人，以检验几个对未来评估 心血管治疗。在目标1中，我们将检验试验结果来自关联登记的假设， 医院，并声称数据将等同于在临床试验中获得的结果，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不是在 最初的临床试验，例如以前的非心脏住院和虚弱的标志，可以增加试验数据， 通过改进风险分层和确定异质治疗效果。在《目标3》中，我们将 利用试验与基于人口的数据集的联系，能够创建新的和广泛的 评估总体试验的概括性和对未被研究的亚组的适用性的适用指标。这个 这些不同资源的联系将创造一个重要机会，以正式评估优势和 使用非临床试验数据来源支持和改进医学临床试验评估的局限性 治疗。所学到的信息有可能极大地指导未来的务实和 自然主义临床试验，同时为治疗冠心病的有效性和安全性提供了新的见解 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.