Antiplatelet in Stroke: Safety and Effectiveness in Seniors (ASSESS)

抗血小板治疗中风：老年人的安全性和有效性（ASSESS）

• 批准号: 9897457
• 负责人: Ying Xian
• 金额: $ 51.52万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9897457
• 关键词: Address; Adoption; Age; Aging; American; American Heart Association; Antiplatelet Drugs; Aspirin; Benefits and Risks; Cardiovascular Diseases; Cardiovascular system; Cause of Death; Characteristics; Clinical; Clinical Trials; Clinical effectiveness; Coagulation Process; Community Practice; Comparative Effectiveness Research; Complication; Data; Databases; Decision Making; Development; Dose; Effectiveness; Equilibrium; European; Event; Evidence based treatment; Face; Failure; Fright; Goals; Guidelines; Health; Heart; Hemorrhage; Heterogeneity; Hospitalization; Hospitals; Individual; Individual Differences; Ischemia; Ischemic Stroke; Knowledge; Lead; Level of Evidence; Life; Link; Machine Learning; Medicare claim; Methods; Mission; Modeling; Observational Study; Outcome; Patient Preferences; Patient risk; Patients; Pharmaceutical Preparations; Population; Predisposition; Primary Prevention; Probability; Public Health; Publications; Recurrence; Regimen; Registries; Research; Risk; Safety; Sampling; Secondary Prevention; Selection Bias; Selection for Treatments; Seminal; Societies; Specific qualifier value; Stroke; Stroke prevention; Subgroup; Techniques; Testing; Translations; Treatment Failure; United States; United States Centers for Medicare and Medicaid Services; United States National Institutes of Health; Weight; Woman; base; burden of illness; cardiovascular disorder prevention; clinical practice; clopidogrel; comorbidity; comparative effectiveness; disability; evidence base; improved; improved outcome; individual patient; individualized medicine; innovation; novel; older patient; patient oriented; personalized medicine; preference; prevent; safety outcomes; sex; stroke patient; stroke survivor; therapy design; treatment effect; treatment response

The current U.S. and European guidelines recommend antiplatelet therapy such as aspirin, clopidogrel, or dual antiplatelet therapy to reduce the risk of recurrent ischemic events in patients with noncardioembolic ischemic stroke. However, selection of antiplatelet agents can be a difficult decision to make for older patients. Not only older individuals are at increased risk for recurrent strokes, they also face risk for bleeding complications. While it is known that drug selection should be individualized, it remains unknown how to tailor antiplatelet therapy according to patient unique characteristics, preferences, needs, and values. The long-term goal is to use com- parative effectiveness research to improve primary and secondary prevention in cardiovascular disease and stroke. Leveraging the American Heart Association (AHA) Get With The Guidelines Stroke (GWTG) registry and Medicare claims, the overall objective in this application is to develop patient-centered evidence-based strategies to improve antiplatelet treatment selection for older individuals presenting with noncardioembolic ischemic stroke. The central hypothesis is that individuals differ in many characteristics, such as age, sex, presence of comorbid- ities, or concurrent medications, which can influence the potential benefits and harms associated with the treat- ment. Once the variability in response to treatment is known, selection of an antiplatelet agent can be made based on patient risk profiles, preferences, and known efficacy/safety of the agents, making treatment both safer and more effective. Guided by strong preliminary data, this hypothesis will be tested by pursuing three specific aims: 1) determine the long-term clinical effectiveness of different antiplatelet regimens in patients with noncar- dioembolic ischemic stroke; 2) determine the long-term safety of different antiplatelet regimens; and 3) balance benefits and harms of each antiplatelet regimen while incorporating patient preferences into decision-making. The proposed research is innovative in four key ways: 1) a patient-centered approach is used to address a real- life decisional dilemma facing stroke survivors and clinicians; 2) it shifts focus from selected samples in clinical trials to nationwide representative stroke population, including traditionally underrepresented subgroups in com- munity practice; 3) novel propensity score inverse probability weighting method using generalized boosted mod- els (a machine learning technique) will be employed to mimic a trial-type multi-treatment design, uncover treat- ment heterogeneity, and minimize selection bias in observational data; 4) beyond traditional mechanisms of scientific publications, the evidence generated from this study will be disseminated to stroke survivors, clinicians, and relevant stakeholders through the AHA GWTG-led national quality initiatives and patient-led efforts to insure a rapid translation of seminal findings into clinical practice. The proposed research is significant, because it is expected to help guide personalized antiplatelet therapy in older ischemic stroke survivors that could best meet their needs and lead to better outcomes most meaningful to patients. Ultimately, such knowledge has the poten- tial to inform evidence-based treatment decisions in stroke that now afflicts more than 7.2 million Americans.

目前美国和欧洲的指南推荐抗血小板治疗，如阿司匹林、氯吡格雷或Dual 抗血小板治疗降低非心源性脑缺血患者再发缺血事件的风险 卒中。然而，对于老年患者来说，选择抗血小板药物可能是一个困难的决定。不仅 老年人中风复发的风险增加，他们也面临出血并发症的风险。而当 众所周知，药物选择应该是个体化的，但如何量身定做抗血小板治疗仍是未知之事 根据患者独特的特点、喜好、需求和价值观。长期目标是使用COM- 改善心血管疾病一级和二级预防的相对有效性研究 卒中。利用美国心脏协会(AHA)获得中风指南(GWTG)登记和 Medicare声称，该应用程序的总体目标是开发以患者为中心的循证策略 目的：改善老年非心源性缺血性卒中患者的抗血小板治疗选择。 中心假设是，个体在许多特征上不同，如年龄、性别、是否存在合并症-- 或同时服药，这可能会影响与治疗相关的潜在好处和危害- 门槛。一旦知道治疗反应的可变性，就可以选择抗血小板药物。 根据患者的风险概况、偏好和已知的药物有效性/安全性，使治疗既安全 而且更有效率。在强劲的初步数据的指导下，这一假说将通过追求三个具体的 目的：1)确定不同抗血小板方案治疗非心绞痛患者的远期临床疗效。 2)确定不同抗血小板方案的长期安全性；3)平衡 在将患者的喜好纳入决策的同时，每种抗血小板方案的利弊。 拟议的研究在四个关键方面具有创新性：1)使用以患者为中心的方法来解决真正的- 卒中幸存者和临床医生面临的生活决策困境；2)它转移了临床上选定的样本的重点 对全国具有代表性的中风人群的试验，包括传统上在COM中代表性较低的亚组- 社区实践；3)新的基于广义Boosted模型的倾向得分逆概率加权方法。 ELS(一种机器学习技术)将被用来模拟试验型多处理设计，揭示治疗- 消除异质性，最大限度地减少观测数据中的选择偏差；4)超越传统的 科学出版物，这项研究产生的证据将传播给中风幸存者，临床医生， 和相关利益攸关方通过AHA GWTG领导的国家质量倡议和患者主导的努力，确保 将开创性发现快速转化为临床实践。这项拟议的研究意义重大，因为它 有望帮助指导最符合条件的老年缺血性中风幸存者的个性化抗血小板治疗 他们的需求，并导致更好的结果，最有意义的患者。归根结底，这样的知识有潜力- TIAL将为中风的循证治疗决策提供信息。中风目前困扰着超过720万美国人。