Enhanced Data to Accelerate Complex Patient Comparative Effectiveness Research

描述（由申请人提供）：我们的前提是易于使用的数据产品将加速有意义的比较有效性研究（CER）。索赔数据非常难以使用，需要丰富的经验才能最恰当地将这些数据汇总到患者层面。还需要专门的专业知识来创建有意义的变量，如治疗、协变量和索赔数据的端点。我们建议开发和传播一套易于使用的分析文件和预编码算法，以研究复杂心血管疾病（CVD）患者二级预防策略的比较有效性。对心血管疾病实践指南的依从性随着年龄的增长而下降，这可能是由于对老年人和患有多种合并症的人的各种二级预防策略的有效性存在不确定性。爱荷华大学老年人治疗学教育和研究中心（爱荷华CERT）和Buccaneer计算系统和服务公司（Buccaneer）的合作提议使用医疗保健慢性病仓库（CCW）作为我们的主要输入来源，创建一个纵向队列，包括2007年收治的180万名AMI或中风/TIA医疗保险受益人，并随访至2008年的复发事件、并发症和死亡。大样本确保了测试二级预防策略在具有复杂情况的优先亚群（例如80岁以上的糖尿病患者）中的相对有效性的能力。至少2个SAS(r)分析数据文件将包含相同心血管疾病队列的不同级别的详细信息和汇总。从CCW的50多个原始索赔、药物事件和人口统计（登记）数据文件中，我们将对这些文件进行处理，以加入我们队列中每个受益人的信息，这些信息随时间推移和在所有护理环境中进行。分析文件将包括：1)患者级别的汇总数据文件，其中汇总了每个受益人所接受的护理设置，并从该文件中创建2)总结治疗和结果数据文件，以便快速查询和报告（例如，可行性分析和初步数据分析）。创建数据产品的代码和算法有望简化未来CER研究新临床人群中未来数据产品的开发。联邦CERT和CTSA网络是传播数据产品信息的天然通信资产。我们将在一个研究项目中测试数据产品，该研究项目关注他汀类药物在患有多种合并症的老年患者中的有效性和安全性的不确定性。

DESCRIPTION (provided by applicant): It is our premise that easy to use data products will accelerate meaningful comparative effectiveness research (CER). Claims data are extremely difficult to use, requiring extensive experience to most appropriately aggregate these data to the patient level. Specialized expertise is also required to create meaningful variables such as treatments, covariates, and endpoints from claims data. We propose to develop and disseminate a large easy to use suite of analytical files and pre-coded algorithms to study comparative effectiveness of secondary prevention strategies among complex patients with cardiovascular disease (CVD). Adherence to CVD practice guidelines declines with age and this may be explained by uncertainty over the effectiveness of various secondary prevention strategies for the oldest old and those with multiple comorbidities. This collaboration of the University of lowa Older Adults Center for Education and Research on Therapeutics (lowa CERT) and Buccaneer Computing Systems & Services, Inc (Buccaneer) proposes to use the Medicare Chronic Condition Warehouse (CCW) as our major input source to create a longitudinal cohort of 1.8 million Medicare beneficiaries admitted with AMI or stroke/TIA in 2007 and followed through 2008 for recurrent events, complications and death. The large sample ensures power to test comparative effectiveness of secondary prevention strategies in priority subgroups with complex conditions, e.g. people over 80 years old with diabetes. At least 2 SAS(r) analytic data files will contain different levels of detail and aggregation for the same CVD cohort. From the more than fifty raw claims, drug event, and demographic (enrollment) data files in CCW, we will process the files to join the information for each beneficiary in our cohort over time and across all care settings. The analytical files will consist of: 1) patient-level aggregated data file(s) that summarize across care settings the care received by each beneficiary, and from this file we will create 2) summary treatment and outcome data files, to allow for rapid querying and reporting (e.g., for feasibility analyses and preliminary data analysis).The code and algorithms to create the data product are expected to streamline the development of future data products in new clinical populations for future CER studies. The federal CERT and CTSA networks are natural communication assets to disseminate information about the data product. We will test the data product in a research project focused on the uncertainty around statin effectiveness and safety in the aging patient with multiple comorbidities. PUBLIC HEALTH RELEVANCE: Cardiovascular disease (CVD) is the leading cause of death and over 80% of this is in people age 565, yet evidence-based guidelines are not aggressively followed, with the effect of co-morbidities not known. We will develop an easy to use data product for comparative effectiveness studies of 1.8 million Medicare heart attack and stroke patients and support its use through a collaboration between the lowa Older Adults Center for Education and Research on Therapeutics and the Institute for Clinical and Translational Science.