Personalizing Angiotensin-Converting Enzyme Inhibitor and Angiotensin Receptor Blocker Therapy in Chronic Kidney Disease

项目概要/摘要 血管紧张素转换酶抑制剂（ACEIs）和血管紧张素受体阻滞剂（ARB）是指南- 预防终末期肾病 (ESKD) 和心血管疾病 (CVD) 的推荐疗法 CKD 患者的事件。然而，在现实世界的实践中，ACEI/ARB 经常因以下原因而停药： 急性肾损伤 (AKI)、肾功能快速下降或晚期 CKD 情况下的医疗服务提供者。 过早停用 ACEI/ARB 可能会导致 CKD 患者出现不良临床结果。 然而，对于某些风险较高的 CKD 亚组，停用这些药物可能是合适的 AKI 患者以及从这些药物中获得的益处可能比试验人群少。尽管指南支持 在 CKD 患者中使用 ACEI/ARB 时，它们还允许围绕其治疗进行个体化决策 使用。然而，随着 CKD 的进展以优化肾脏，目前尚不清楚如何最好地个性化这些决定 和心血管结局。 该提案的总体目标是根据每个因素为 CKD 患者提供个性化的 ACEI/ARB 治疗 个人的 ESKD 和 CVD 危险因素。为了实现这一目标，我们将使用来自慢性肾病的数据 不足队列 (CRIC) 研究。我们将首先确定导致 ACEI/ARB 停用的因素 （目标 1）。接下来，我们将构建两个临床工具来模拟 ACEI/ARB 停药之间的关系 CKD 患者发生 ESKD（目标 2）或 CVD 事件（目标 3）的风险。我们的长期目标是减少 通过个体化治疗 CKD 药物，减轻肾脏疾病负担并改善患者预后。我们期望 完成这些目标将导致开发出两种可以进一步完善的预测工具 并作为职业发展奖申请的一部分在现实临床实践中实施。 拟议的目标被纳入一个全面的培训计划，其中包括硕士学位 临床研究和实践指导经验。通过本次培训计划，陈博士将拥有 1）学习和应用先进生物统计和流行病学方法的知识，包括 边际结构建模； 2) 熟悉 CRIC 研究数据库，3) 应用机器学习 开发临床预测工具的算法，4) 提高科学写作和演示的技能 研究，以及 5) 争取职业发展奖和作为临床研究者的独立性。

PROJECT SUMMARY/ABSTRACT Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are guideline- recommended therapies for preventing end-stage kidney disease (ESKD) and cardiovascular disease (CVD) events in patients with CKD. However, in real-world practice, ACEIs/ARBs are frequently discontinued by providers in the setting of acute kidney injury (AKI), rapid declines in kidney function, or advanced CKD. Premature discontinuation of ACEIs/ARBs can lead to adverse clinical outcomes among patients with CKD. However, discontinuation of these agents may be appropriate for certain CKD subgroups who are at higher risk of AKI and who may derive less benefit from these agents than trial populations. Although guidelines support the use of ACEIs/ARBs in patients with CKD, they also allow for individualization of decisions surrounding their use. However, it is unclear how to best individualize these decisions as CKD progresses to optimize kidney and cardiovascular outcomes. The overall objective of this proposal is to personalize ACEI/ARB therapy for patients with CKD based on each individual's risk factors for ESKD and CVD. To accomplish this, we will use data from the Chronic Renal Insufficiency Cohort (CRIC) Study. We will first identify factors that drive the discontinuation of ACEIs/ARBs (Aim 1). Next, we will build two clinical tools that model the relationship between ACEI/ARB discontinuation and risk of ESKD (Aim 2) or CVD events (Aim 3) in individuals with CKD. Our long-term goal is to reduce the burden of kidney disease and improve patient outcomes by individualizing medication use in CKD. We expect that completion of these aims will result in the development of two prediction tools that can be further refined and implemented in real-world clinical practice as part of a career development award application. The proposed aims are integrated into a comprehensive training plan that includes a Master's Degree in Clinical Research and practical mentored experiences. Through this training plan, Dr. Chen will have the opportunity to 1) learn and apply knowledge in advanced biostatistical and epidemiological methods, including marginal structural modeling; 2) gain familiarity with the CRIC Study database, 3) apply machine learning algorithms to the development of clinical prediction tools, 4) refine skills in scientific writing and presenting research, and 5) advance towards a career development award and independence as a clinical investigator.