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.

项目概要 心力衰竭 (HF) 是一个重大的公共卫生问题，死亡率很高（5 年时约为 50%），需要住院治疗 再入院（30 天时约 25%）。血管紧张素转换酶抑制剂（ACEIs）和血管紧张素受体 阻滞剂（ARB）可改善射血分数降低的心力衰竭（HFrEF）患者的两种结局。然而， 这些药物还会对肾功能产生不利影响，并可能增加急性肾损伤（AKI）的风险， 慢性肾病 (CKD) 进展和肾衰竭，导致终末期肾病 (ESRD) 需要肾脏替代治疗。所有这些风险在患有 CKD 的 HFrEF 患者和那些 高剂量接受这些药物。我们已经证明 ACEI 或 ARB 可以降低死亡率 带 CKD 的 HFrEF (PMC3324926)。我们的工作结果还表明 ACEI 或 ARB 的临床益处 在低剂量和高剂量下可能相似。拟议研究的目的是检验假设 低剂量 ACEI 和 ARB 对 CKD 患者的 HFrEF 是安全且有益的。然后我们将开发一个 机器学习算法可识别可能从这些药物中受益的个别心力衰竭患者 独特的射血分数、肾功能和其他基线特征。这些目标将通过以下方式实现 使用 VA 的全国数据（超过 100 万心力衰竭患者）和美国心脏协会的 Get With The 与美国肾脏数据系统关联的指南 (GWTG) 心力衰竭数据（超过 150 万心力衰竭患者） （USRDS）数据。 HF 将使用自动机器学习算法进行裁决。有源比较器 具有倾向评分匹配和敏感性分析的新用户设计将用于比较临床和 接受低剂量与高剂量 ACEI 或 ARB 患者的肾脏结局。机器学习将用于 开发风险预测模型，以最大限度地提高临床效益并最大限度地减少个体患者的肾脏损害。 调查团队由国家重点内容领域专家组成，具有集体经验和 专业知识，及时完成项目。近一半的 I 类建议（好处 国家心力衰竭指南中的“大于风险”）基于 C 级证据（主要是专家意见），并且 需要扩大临床实践指南的证据基础。调查结果来自 拟议的项目将提供证据，帮助临床医生在使用 ACEI 时采用个性化方法 和 ARB 治疗 HFrEF 患者，从而优化潜在风险和益处。