Improving Outcomes in Veterans with Heart Failure and Chronic Kidney Disease

改善患有心力衰竭和慢性肾脏病的退伍军人的预后

• 批准号: 10186538
• 负责人: ALI AHMED
• 金额: --
• 依托单位: U.S. DEPT/VETS AFFAIRS MEDICAL CENTER
• 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 2024-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10186538
• 关键词: Accounting; Acute Renal Failure with Renal Papillary Necrosis; Address; Adverse drug effect; Adverse effects; Affect; American; American Heart Association; Angiotensin Receptor; Angiotensin-Converting Enzyme Inhibitors; Area; Benefits and Risks; Blinded; Characteristics; Chronic Kidney Failure; Clinical; Clinical Practice Guideline; Data; Disease Progression; Dose; Drug usage; EFRAC; End stage renal failure; Expert Opinion; Fracture; Functional disorder; Future; Glomerular Filtration Rate; Guidelines; Harm Reduction; Heart failure; Hospital Costs; Hospitalization; Hypotension; Individual; Information Systems; Kidney; Kidney Failure; Left Ventricular Ejection Fraction; Link; Machine Learning; Modeling; Natural Language Processing; Outcome; Patients; Pharmaceutical Preparations; Phenotype; Population; Public Health; Randomized Controlled Trials; Recommendation; Renal Replacement Therapy; Renal function; Renin-Angiotensin System; Risk; Risk Factors; Testing; Therapeutic; Time; United States; United States Department of Veterans Affairs; Veterans; Work; active comparator; adjudicate; adverse outcome; automated algorithm; base; cohort; deep learning; design; evidence base; experience; falls; high risk; hospital readmission; hyperkalemia; improved; improved outcome; individual patient; individualized medicine; machine learning algorithm; mortality; mortality risk; personalized approach; precision medicine; preservation; prognostic; risk prediction model; trial comparing

Project Summary Heart failure (HF) is a major public health problem with high mortality (~50% at 5 years) and hospital readmission (~25% at 30 days). Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) improve both outcomes in patients with HF with reduced ejection fraction (HFrEF). However, these drugs also adversely affect kidney function, and may increase the risk of acute kidney injury (AKI), chronic kidney disease (CKD) progression, and incident kidney failure, leading to end-stage renal disease (ESRD) requiring renal replacement therapy. All these risks are higher in HFrEF patients with CKD and those receiving these drugs in high doses. We have demonstrated that ACEIs or ARBs may reduce mortality in HFrEF with CKD (PMC3324926). Findings from our work also suggest that clinical benefits of ACEIs or ARBs might be similar at both low and high doses. The objectives of the proposed study are to test the hypotheses that low-dose ACEIs and ARBs are safe and beneficial in patients HFrEF with CKD. We will then develop a machine-learning algorithm to identify individual HF patients who might benefit from these drugs given their unique ejection fraction, kidney function, and other baseline characteristics. These aims will be achieved by using VA's national data (over 1 million HF patients) and the American Heart Association's Get With The Guideline (GWTG) HF data (over 1.5 million HF patients) linked to the United States Renal Data System (USRDS) data. HF will be adjudicated using an automated machine-learning algorithm. An active-comparator new-user design with propensity score matching and sensitivity analysis will be used to compare clinical and renal outcomes in patients receiving low-dose vs. high-dose ACEIs or ARBs. Machine learning will be used to develop a risk prediction model to maximize clinical benefit and minimize renal harm for individual patients. The investigative team consists of national experts in key content areas and has the collective experience and expertise to complete the project in a timely manner. Nearly half of the Class-I recommendations (benefit greater than risk) in national HF guideline are based on Level-C evidence (mostly expert opinion) and there is a need to expand the evidence base from which clinical practice guidelines are derived. Findings from the proposed project will provide evidence that will help clinicians use a personalized approach in the use of ACEIs and ARBs in patients with HFrEF so that potential risks and benefits are optimized.

项目摘要 心力衰竭（HF）是高死亡率（5年时约50％）和医院的主要公共卫生问题 再入院（30天时约25％）。血管紧张素转换酶抑制剂（ACEI）和血管紧张素受体 阻滞剂（ARB）改善了射血分数降低（HFREF）的HF患者的两种结局。然而， 这些药物还对肾脏功能产生不利影响，并可能增加急性肾脏损伤的风险（AKI）， 慢性肾脏疾病（CKD）进展和事件肾衰竭，导致终末期肾脏疾病 （ESRD）需要肾脏替代疗法。 HFREF患者的所有这些风险都更高 高剂量接受这些药物。我们已经证明了ACEI或ARB可能会降低死亡率 HFREF与CKD（PMC3324926）。我们工作中的发现还表明，ACEI或ARB的临床益处 低剂量和高剂量可能相似。拟议研究的目标是检验假设 低剂量的ACEI和ARB对患有CKD的HFREF患者是安全且有益的。然后，我们将开发一个 机器学习算法以识别可能从这些药物中受益的个别HF患者 独特的射血分数，肾功能和其他基线特征。这些目标将通过 使用VA的国家数据（超过100万HF患者）和美国心脏协会与 与美国肾脏数据系统相关的指南（GWTG）HF数据（超过150万HF患者） （USRDS）数据。 HF将使用自动化的机器学习算法进行裁决。一个有源启动器 具有倾向得分匹配和灵敏度分析的新用户设计将用于比较临床和 接受低剂量的患者与高剂量ACEI或ARB的肾脏结局。机器学习将用于 开发风险预测模型，以最大程度地提高临床益处并最大程度地减少对个别患者的肾脏伤害。 调查团队由关键内容领域的国家专家组成，并具有集体经验和 及时完成项目的专业知识。 I类建议的近一半（受益 在国家HF指南中，大于风险）是基于级别的证据（主要是专家意见），并且有 需要扩大从中得出临床实践指南的证据基础。来自 拟议的项目将提供证据，这些证据将帮助临床医生在使用ACEIS时使用个性化方法 和HFREF患者的ARB，以便优化潜在的风险和益处。