Comparative Effectiveness of Imaging Strategies in Acute Ischemic Stroke Based on Patient Profiles - Resubmission - 1

项目摘要/摘要 中风对健康有重大影响，对人群的健康构成持续的挑战 以及医疗保健资源的可用性。影像在诊断和治疗中都起着至关重要的作用。 急性缺血性卒中的决策。然而，有关高级成像的最佳策略 根据国家指南，血管造影术和使用CT(CTAP)或MR(MRAP)的灌注仍然不确定。 台词。因此，低效的成像做法随着显著增加的利用率而演变 先进的成像技术。在临床实践中，卒中成像也存在很大的差异，大多数默认为 医生的偏好和制度因素，没有清楚地了解涉及的好处和风险 中风护理。主要的担忧是，低效的成像会导致治疗的时间延误，并有可能 更糟糕的结果。尽管CTAP和MRAP在急性卒中中各有其独特的益处和风险 在这种情况下，这种风险-收益权衡对健康结果和资源利用的影响尚不清楚。这个 问题是这种基于结果的证据的缺乏导致了巨大的差异和低效的成像实践 在卒中护理中，直接影响资源和成本的利用，但更重要的是影响治疗 决策、时间延误和健康后果。不幸的是，到目前为止，没有任何努力在 超过这一点作为现状，因为在理解如何不同方面存在显著的知识差距 成像策略会影响中风的结果，因为高级成像与 急性卒中时为CTAP或MRAP。这项建议的总体目标是比较 应用决策科学方法评估急性缺血性卒中影像策略的有效性 在健康结果、资源利用(成像和治疗)和成本方面的风险-收益权衡。我们的 中心假设是健康结果将得到改善，医疗资源将得到最有效的利用 根据特定患者配置文件(由定义)选择高级成像(CTAP或MRAP 已知的影响中风预后的特征，如年龄、临床中风严重程度和症状出现时间。 我们将实现以下具体目标：(1)更新中风健康结局、质量指标、 利用资源和成本，(2)根据患者档案开发新的成像建议 以及(3)量化新的成像建议对健康的潜在影响 与当前实际情况相比，理想的基于模型的方案中的结果、资源利用率和成本 世界惯例。我们的交付内容是创新的成像建议，专为急性发作的患者量身定做 使用全面的模型评估对健康结局的潜在影响和缺血性中风 利用资源，在未来实施之前提供关键的安全性、可行性和有效性数据 临床试验。重要的是，决策模型可以灵活地进行修改，并可以根据新的证据或 技术不断发展，从而为未来的临床和政策决策提供了持久的宝贵资源。

Project Summary/Abstract Stroke has a significant health impact and presents an ongoing challenge for the health of the population and the availability of healthcare resources. Imaging plays a critical role in both diagnosis and treatment decisions in acute ischemic stroke. However, optimal strategies regarding advanced imaging with angiography and perfusion using either CT (CTAP) or MR (MRAP) remain uncertain according to national guide- lines. Consequently, inefficient imaging practices have evolved with marked increased utilization of advanced imaging. Wide variations in stroke imaging also exist in clinical practice, mostly defaulted to physician preferences and institutional factors, without a clear understanding of the benefits and risks involved in stroke care. The main concern is that inefficient imaging leads to time-delays to treatment and potentially worse outcomes. Although CTAP and MRAP each have unique benefits and risks in the acute stroke setting, the effect of this risk-benefit tradeoff on health outcomes and utilization of resources is unknown. The problem is this lack of outcomes-based evidence results in wide variations and inefficient imaging practices in stroke care, directly affecting utilization of resources and costs, but more importantly affecting treatment decisions, time-delays and health outcomes. Unfortunately, no efforts to-date have been effective in moving beyond this as the status-quo because significant knowledge gaps exist in understanding how different imaging strategies affect stroke outcomes given the unique risk-benefit tradeoffs of advanced imaging with either CTAP or MRAP in the acute stroke setting. The overall objective of this proposal is to compare the effectiveness of imaging strategies in acute ischemic stroke employing decision science methods to assess the risk-benefit tradeoffs on health outcomes, utilization of resources (imaging and treatment) and costs. Our central hypothesis is that health outcomes will be improved and healthcare resources used most efficiently when advanced imaging (CTAP or MRAP) is selected according to specific patient profiles, defined by characteristics known to impact stroke outcomes such as age, clinical stroke severity and symptom onset time. We will perform the following specific aims: (1) Update trends in stroke health outcomes, quality metrics, utilization of resources and costs, (2) Develop new imaging recommendations tailored to patient profiles based on stroke outcomes, and (3) Quantify the potential impact of the new imaging recommendations on health outcomes, utilization of resources and costs in an ideal model-based scenario compared to current real- world practice. Our deliverables are innovative imaging recommendations tailored to patient profiles in acute ischemic stroke with comprehensive model evaluation of the potential impact on health outcomes and utilization of resources, providing critical safety, feasibility and efficacy data prior to implementation in future clinical trials. Importantly, the decision model is flexible to revisions and can be adjusted as new evidence or technology develops, thus producing an enduring valuable resource for future clinical and policy decisions.