Optimizing stroke prevention for older adults with atrial fibrillation: Towards rigorous evaluation and judicious application of a new device

项目摘要/摘要 房颤(AF)影响着大约10%的老年人，在中风中所占的比例越来越大。 终生口服抗凝，使用华法林或非维生素K拮抗剂(NOAC)，是 推荐用于大多数房颤患者的中风预防。然而，这些药物增加了出血和 对终身药物治疗的依从性很差，导致许多患者得不到治疗。最近批准的 Watchman设备为预防房颤中风的终身药物治疗提供了一个有吸引力的替代方案。 然而，该设备只在两项临床试验中进行了研究，这两项试验都将其与华法林相比较。小才是 了解Watchman与当前主流疗法NOAC的比较，或Watchman如何 相比之下，服用抗凝药物有困难的患者不接受治疗。此外，对于 预防性治疗具有较高的前期成本和一些程序风险，需要采取个性化的方法来 将Watchman目标对准最有可能受益的患者，而对那些获益甚微的患者则避免。 因此，该项目的总体目标是解决这些证据差距并开发新的预测 优化使用Watchman预防房颤中风的工具。在目标1中，我们将进行比较 使用包含保险的大型国家行政数据库(OptomLabs)进行的有效性研究 来自所有50个州的200多万不同年龄和种族的房颤患者的声明，这些患者的EHR相关 子集。这些发现将提供及时的证据，以解决在 目前的实践指南，并将促进未来临床试验的设计、分析和解释。在……里面 目标2，我们将开发和验证机器学习模型，以预测Watchman与 非侵入性治疗。我们将使用OptomLabs数据开发模型，并在 两个随机对照试验和两个大型卫生系统的EHR，从而在临床试验和常规中验证模型 练习设置。新的预测模型将提供对收益和危害的个性化估计， 因此，让患者做出与他们的偏好一致的明智选择，并缓解临床医生的 认知负担。在目标3中，我们将评估守望者如何在当代实践中做出决定 同意新预测模型的建议。我们将使用机器学习方法来 确定与不一致的决定相关的患者和提供者的特征。这些发现将突出显示 可能特别受益于决策支持的患者和提供者群体，从而为未来提供信息 实施和翻译工作。我们组建了一支临床和研究互补的团队 专业知识、成功协作的坚实记录以及在成果研究和 预测建模。我们还开发了一个基于Web的决策辅助工具，可以随时将 预测模型，以减少护理提供、患者结局和医疗成本方面的不必要差异。

PROJECT SUMMARY/ABSTRACT Atrial fibrillation (AF) affects about 10% of older adults and accounts for a growing proportion of strokes. Lifelong oral anticoagulation, with warfarin or a non-vitamin K antagonist oral anticoagulant (NOAC), is recommended for stroke prevention in most AF patients. However, the drugs increase the risk of bleeding and the adherence to the lifelong drug therapy is poor, leaving many patients under-treated. The recently-approved Watchman device offers an attractive alternative to lifelong drug therapy for AF stroke prevention. However, the device has been studied in only two clinical trials, both of which compared it to warfarin. Little is known about how Watchman compares with the current mainstay therapy, NOACs, or how Watchman compares with no treatment in patients who have difficulties taking anticoagulation drugs. Furthermore, for preventive treatment with a high upfront cost and some procedural risks, a personalized approach is needed to target Watchman to patients who are most likely to benefit and avoid it in those who have little to gain. Therefore, the overall objective of this project is to address these evidence gaps and develop new prediction tools to optimize the use of Watchman for AF stroke prevention. In Aim 1, we will conduct comparative effectiveness studies using a large national administrative database (OptumLabs) that contains insurance claims for over two million patients with AF of all ages and races from all 50 states with linked EHRs in a subset. The findings will provide timely evidence to address the key unanswered questions highlighted in current practice guidelines and will facilitate the design, analysis, and interpretation of future clinical trials. In Aim 2, we will develop and validate machine-learning models to predict how Watchman compares with non-invasive therapies. We will develop the models using the OptumLabs data, and validate the models in two RCTs and two large health systems’ EHRs, thereby validating models in both clinical trial and routine practice settings. The new prediction models will provide personalized estimates for the benefits and harms, and thus, engage patients in making informed choices consistent with their preferences and ease clinicians’ cognitive burden. In Aim 3, we will assess how the Watchman decisions made in contemporary practice agree with those suggested by the new prediction models. We will use machine-learning methods to identify patient and provider characteristics associated with incongruent decisions. Such findings will highlight patient and provider groups who may particularly benefit from the decision support, thereby informing future implementation and translation efforts. We have assembled a team with complementary clinical and research expertise, a solid record of successful collaboration, and extensive experience in outcomes research and prediction modeling. We also have developed a web-based decision aid that is ready to translate the prediction models to reduce unwarranted variation in care delivery, patient outcomes, and medical costs.