An Observational Acute Stroke Model for Decision Support and Comparing Outcomes

描述（由申请人提供）：据估计，每年有超过795，000名美国人经历中风。中风仍然是美国的主要死亡原因，仅次于心血管疾病和癌症;并且是长期残疾的主要原因，只有25%的成年人从这种状况恢复到正常健康。由于急性中风导致的神经损伤的严重程度通过早期恢复受影响区域的血流而减轻;现在更多的人通过溶栓剂和介入凝块取出装置的早期干预而在中风中幸存下来。不幸的是，该领域新药和器械的快速发展使得难以为特定患者提供治疗指导，并且缺乏用于比较治疗组之间结果的指标。该提案的重点是创建一个观察数据库，随后用于支持急性卒中治疗的影响图。该数据库基于卒中及其治疗的统一信息模型的规范：迄今为止，尚未建立使用标准化表示和/或受控词汇纳入这些变量的综合框架。我们利用我们机构的当前数据;以及通过美国心脏协会（AHA）即将发布的国家数据集来实例化该数据库。从该数据库中，使用关于患者表现、病史、成像和可用治疗药物/设备的信息来建立影响图，以计算最佳治疗决策，从而最大化结果和其他考虑因素（例如，生活质量）。通过插补和倾向分数处理缺失数据的方法用于帮助计算影响图所需的条件概率表。在这一努力过程中，将考虑一系列效用函数，以探索健康结果和成本之间的权衡。使用数据库和影响图，支持基于病例的相似性检索，从而能够发现相关的既往病例，以审查治疗决策和结局;并作为定义和比较亚组（及其结局）的方法。将开发一个图形用户界面（GUI），用于查询影响图和执行基于案例的检索。评价的重点是评估影响图相对于已知结果的性能，并与其他常规统计模型（例如，逻辑回归、决策树）;以及系统对影响决策的总体影响。该R 01利用了加州大学洛杉矶分校中风中心在急性中风治疗方面的国家认可的临床专业知识;以及一个长期的医学信息学研究小组。这项工作的最终结果将是一套信息驱动的建模工具和一个急性中风治疗数据库。

DESCRIPTION (provided by applicant): Annually, it is estimated that more than 795,000 Americans experience a stroke. Stroke remains a major cause of death in the United States, behind only cardiovascular disease and cancer; and is the leading cause of long- term disability, with only 25% of adults recovering to normal health from this condition. The severity of neurological damage due to an acute stroke is mitigated by the early restoration of blood flow to the affected area; and more people are now surviving strokes through earlier intervention with thrombolytic agents and interventional clot retrieval devices. Unfortunately, the rapid development of new drugs and devices in this area has made it difficult to provide treatment guidance for a given patient, and metrics for comparing outcomes between treatment groups are lacking. This proposal focuses on the creation of an observational database that is subsequently used to support an influence diagram for acute stroke treatment. The database is predicated on the specification of a unified in- formation model for stroke and its treatment: thus far, no comprehensive framework has been established to incorporate these variables using standardized representations and/or controlled vocabularies. We take ad- vantage of current data at our institution; and a forthcoming national dataset through the American Heart Association (AHA) to instantiate this database. From this database, an influence diagram is established, using in- formation on patient presentation, medical history, imaging, and available treatment drugs/devices to compute an optimal treatment decision maximizing outcomes and other considerations (e.g., quality of life). Methods to handle missing data via imputation and propensity scores are used to help compute the required conditional probability tables underlying the influence diagram. A spectrum of utility functions will be considered over the course of this effort to explore the trade-offs between health outcomes and cost. Using the database and influence diagram, case-based similarity retrieval is supported, enabling discovery of related past cases to review treatment decisions and outcomes; and as a means to define and compare subgroups (and their outcomes). A graphical user interface (GUI) for querying the influence diagram and performing case-based retrieval will be developed. Evaluation focuses on assessing the performance of the influence diagram relative to known out- comes and compared to other conventional statistical models (e.g., logistic regression, decision trees); and the overall impact of the system to impact decision-making. This R01 leverages nationally-recognized clinical expertise in acute stroke treatment from the UCLA Stroke Center; as well as a longstanding medical informatics research group. The ultimate result of this effort will be a set of informatics-driven modeling tools and a database for acute stroke treatment.