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

慢性肾脏病的个体化血管紧张素转换酶抑制剂和血管紧张素受体阻滞剂治疗

• 批准号: 10313873
• 负责人: Debbie Catherine Chen
• 金额: $ 8.86万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10313873
• 关键词: Acute; Acute Renal Failure with Renal Papillary Necrosis; Adverse effects; Age; Angiotensin Receptor; Angiotensin-Converting Enzyme Inhibitors; Antihypertensive Agents; Biometry; Biostatistical Methods; California; Cardiovascular Diseases; Cardiovascular system; Chronic Kidney Failure; Chronic Kidney Insufficiency; Clinical; Clinical Investigator; Clinical Research; Cohort Studies; Data; Databases; Development; Diagnosis; Disease Outcome; Effectiveness; Elderly; Electronic Health Record; End stage renal failure; Enrollment; Epidemiologic Methods; Epidemiology; Event; Familiarity; Funding; Glomerular Filtration Rate; Goals; Guidelines; Heart failure; Heterogeneity; Hypertension; Hypotension; Individual; K-Series Research Career Programs; Kidney; Kidney Diseases; Knowledge; Lead; Learning; Longterm Follow-up; Machine Learning; Master' s Degree; Mediating; Mentors; Modeling; Myocardial Infarction; Outcome; Participant; Patient risk; Patient-Focused Outcomes; Patients; Peripheral arterial disease; Pharmaceutical Preparations; Population; Proteinuria; Provider; Quality of life; Recommendation; Renal function; Reporting; Research; Risk; Risk Factors; San Francisco; Solid; Stroke; Structural Models; System; Time; Training; Universities; Update; Visit; Writing; base; clinical development; clinical practice; disorder subtype; experience; follow-up; functional status; high risk; hyperkalemia; implementation science; improved; machine learning algorithm; mortality; personalized care; personalized decision; predictive modeling; premature; prevent; programs; skills; tool

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.

项目摘要/摘要 血管紧张素转换酶抑制剂（ACEI）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患者中发生的事件。然而，在现实世界的实践中，ACEI/ARB经常被停用， 在急性肾损伤（阿基）、肾功能快速下降或晚期CKD的情况下提供者。 提前停用ACEI/ARB可导致CKD患者出现不良临床结局。 然而，对于某些CKD亚组，这些药物的停药可能是适当的， 阿基的患者，这些药物的获益可能低于试验人群。虽然指南支持 在CKD患者中使用ACEI/ARB，也允许围绕其治疗方案的个体化决策。 使用.然而，目前尚不清楚如何最好地个性化这些决定，作为慢性肾脏病的进展，以优化肾脏 和心血管结局。 该提案的总体目标是根据CKD患者的每种ACEI/ARB治疗方案， ESKD和CVD的个体风险因素。为了实现这一目标，我们将使用来自慢性肾脏病患者的数据。 不充分队列研究（CRIC）。我们将首先确定导致ACEI/ARB停药的因素 (Aim 1）。接下来，我们将建立两个临床工具，模拟ACEI/ARB停药之间的关系， 以及CKD患者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CKD患者的个体化用药，减轻肾脏疾病负担并改善患者结局。我们预计 这些目标的完成将导致两种预测工具的开发，这两种工具可以进一步完善 并作为职业发展奖申请的一部分在现实世界的临床实践中实施。 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， 临床研究和实际指导经验。通过这个培训计划，陈博士将拥有 机会1）学习和应用先进的生物统计学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 算法的临床预测工具的发展，4）完善科学写作和呈现技能 研究，和5）走向职业发展奖和独立的临床研究者。