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，具有高死亡率（5年时约50%）和住院率。 再入院（30天时约25%）。血管紧张素转换酶抑制剂和血管紧张素受体 受体阻滞剂（ARB）可改善射血分数降低（HFrEF）的HF患者的两种结局。然而，在这方面， 这些药物还对肾功能产生不利影响，并可能增加急性肾损伤（阿基）的风险， 慢性肾脏疾病（CKD）进展和偶发性肾衰竭，导致终末期肾脏疾病 （ESRD）需要肾脏替代治疗。所有这些风险在患有CKD的HFrEF患者中较高， 大剂量服用这些药物。我们已经证明ACEI或ARB可以降低 HFrEF伴CKD（PMC3324926）。我们的研究结果还表明，ACEI或ARB的临床获益 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在HFrEF伴CKD患者中安全且有益。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获益的个体HF患者， 独特的射血分数、肾功能和其他基线特征。这些目标将通过以下方式实现： 使用VA的全国数据（超过100万HF患者）和美国心脏协会的Get With The 指南（GWTG）HF数据（超过150万HF患者）链接至美国肾脏数据系统 （USRDS）数据。将使用自动机器学习算法裁定HF。有源比较器 将使用具有倾向评分匹配和敏感性分析的新用户设计来比较临床和 接受低剂量与高剂量ACEI或ARB治疗患者的肾脏结局。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获益，并最大限度地降低个体患者的肾脏损害。 调查小组由关键内容领域的国家专家组成，具有集体经验， 专业知识，及时完成项目。近一半的I类建议（受益于 大于风险）是基于C级证据（主要是专家意见）， 需要扩大临床实践指南的证据基础。Findings from the 拟议的项目将提供证据，将有助于临床医生在使用ACEI时使用个性化方法 和ARB，以便优化潜在风险和获益。

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.