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 - 建议预防末期肾脏疾病（ESKD）和心血管疾病（CVD）的疗法 CKD患者的事件。但是，在实际实践中，ACEI/ARB经常被停止 急性肾脏损伤（AKI）的提供者，肾功能迅速下降或晚期CKD。 ACEI/ARB的过早停用可能导致CKD患者的不良临床结果。 但是，这些代理的停用可能适合某些处于较高风险的CKD亚组 AKI的及其可能从这些代理人中获得的好处比试验人群少。虽然指南支持 在CKD患者中使用ACEIS/ARB，他们还允许对围绕其决定的决策个性化 使用。但是，尚不清楚如何在CKD进步以优化肾脏时最好地实现这些决定 和心血管结局。 该提案的总体目的是根据每种患者对CKD患者的ACEI/ARB治疗个性化 个人的ESKD和CVD的风险因素。为此，我们将使用慢性肾脏的数据 不足群体（CRIC）研究。我们将首先确定驱动acceis/arbs中断的因素 （目标1）。接下来，我们将建立两个临床工具，以模拟ACEI/ARB中断之间的关系 以及CKD患者中ESKD（AIM 2）或CVD事件（AIM 3）的风险。我们的长期目标是减少 肾脏疾病的负担并通过在CKD中个性化药物使用来改善患者预后。我们期望 这些目标的完成将导致开发两个可以进一步完善的预测工具 并在现实世界中实施，作为职业发展奖应用程序的一部分。 拟议的目标已整合到包括硕士学位的全面培训计划中 临床研究和实践指导的经验。通过这个培训计划，陈博士将拥有 1）学习和应用知识的先进生物统计和流行病学方法，包括 边缘结构建模； 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）完善科学写作和呈现方面的技能 研究，以及5）进入职业发展奖和作为临床研究者的独立性。