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

项目总结/摘要 房颤（AF）影响约10%的老年人，并占中风的比例越来越大。 终身口服抗凝剂，华法林或非维生素K拮抗剂口服抗凝剂（NOAC）， 建议用于大多数AF患者的中风预防。然而，这些药物会增加出血的风险， 终身药物治疗的坚持性差，使许多患者治疗不足。最近批准的 Watchman器械为预防房颤卒中提供了终身药物治疗的一种有吸引力的替代方案。 然而，该装置仅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进行了比较。之甚少 了解Watchman与当前主流疗法NOAC的比较，或者Watchman如何 与服用抗凝药物有困难的患者没有治疗相比。此外，对于 预防性治疗具有较高的前期费用和一些程序风险，需要个性化的方法， 将Watchman瞄准那些最有可能受益的患者，并避免那些几乎没有收益的患者。 因此，本项目的总体目标是解决这些证据缺口并开发新的预测 优化Watchman用于AF中风预防的工具。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（OptumLabs）进行有效性研究 来自所有50个州的所有年龄和种族的200多万AF患者的EHR相关索赔， 子集调查结果将提供及时的证据，以解决 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 目标2，我们将开发和验证机器学习模型，以预测Watchman与 非侵入性疗法。我们将使用OptumLabs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。新的预测模型将为益处和危害提供个性化的估计， 因此，让患者根据自己的偏好做出明智的选择， 认知负担在目标3中，我们将评估守望者在当代实践中如何做出决策 这与新的预测模型的预测结果一致。我们将使用机器学习方法， 识别与不一致决策相关的患者和提供者特征。这些发现将突出 患者和提供者群体可能特别受益于决策支持，从而告知未来 执行和翻译工作。我们组建了一支具有互补临床和研究的团队 专业知识，成功合作的坚实记录，以及在成果研究和 预测建模我们还开发了一个基于网络的决策辅助工具， 预测模型，以减少护理交付、患者结果和医疗成本中的不必要变化。

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.