Improving Outcomes in Veterans with Heart Failure and Chronic Kidney Disease

项目摘要 心力衰竭(HF)是一个主要的公共卫生问题，死亡率高(5年时约50%)和医院 再次入院(30天时约为25%)。血管紧张素转换酶抑制剂与血管紧张素受体 受体阻滞剂(ARB)可改善射血分数(HFrEF)降低的心衰患者的这两种结局。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。 慢性肾脏疾病(CKD)进展和偶发肾衰竭，导致终末期肾脏疾病 (终末期肾病)需要肾脏替代疗法。所有这些风险在患有CKD的HFrEF患者和 接受高剂量的这些药物。我们已经证明，ACEI或ARB可以降低 HFrEF与CKD(PMC3324926)。我们的研究结果还表明，ACEI或ARB的临床益处 在低剂量和高剂量下可能是相似的。拟议研究的目标是检验假设。 小剂量ACEIs和ARB对CKD患者的HFrEF是安全和有益的。然后，我们将开发一种 机器学习算法识别可能从这些药物中受益的个别心衰患者 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 使用退伍军人事务部的全国数据(100多万心衰患者)和美国心脏协会的Get With the 指南(GWTG)心力衰竭数据(150多万心力衰竭患者)与美国肾脏数据系统相连 (USRDS)数据。HF将使用自动机器学习算法进行评判。有源比较器 将使用带有倾向得分匹配和敏感度分析的新用户设计来比较临床和 接受低剂量与大剂量血管紧张素转换酶抑制剂或ARB治疗的患者的肾脏结局。机器学习将被用于 开发风险预测模型，以最大限度地提高临床效益，最大限度地减少对个别患者的肾脏损害。 调查小组由关键内容领域的国家专家组成，具有集体经验和 及时完成项目的专业知识。近一半的I类建议(受益 (大于风险)是基于C级证据(主要是专家意见)，并且存在 有必要扩大临床实践指南所依据的证据基础。研究结果： 拟议的项目将提供证据，帮助临床医生在ACEI的使用中使用个性化的方法 和ARB在HFrEF患者中的应用，从而优化潜在的风险和收益。

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.