Implanted Cardiac Defibrillators for Heart Failure Patients with Kidney Disease

描述(由申请者提供)：认识到在复杂的多种并存患者中优化循证干预的挑战，我们建议评估植入式心脏除颤器(ICD)在心力衰竭(HF)和慢性肾脏疾病(CKD)患者中的最佳利用。考虑到心力衰竭和慢性肾脏病日益流行和普遍共存，以及缺乏来自临床试验和实践指南的数据来具体指导这种复杂患者的治疗，拟议的项目与美国医疗体系具有关键的相关性。尤其重要的是，慢性肾脏病与除心脏性猝死(SCD)以外的竞争性死亡方式的风险增加有关，这可能会改变这两种慢性病患者ICD的整体疗效。因此，本模型研究的目的有三个方面。首先，我们将使用参加左心功能障碍研究(SOLVD)试验的没有ICD的心力衰竭人群的主要数据来估计心力衰竭和慢性肾脏病严重程度的变化如何影响原因特定死亡(心源性心脏病、非心律失常性心源性死亡和非心源性死亡)的风险，并估计慢性心绞痛和心力衰竭疾病的进展率。这将使用竞争风险比例风险回归来模拟几种类别的HF/CKD严重程度和年龄的三种特定原因的死亡模式，并使用比例风险回归模型来估计HF/CKD的进展。其次，我们将基于系统的文献回顾/数据综合，验证和扩展我们从主要数据建模中的估计，重点关注ICD治疗的风险/好处，以及不同年龄和心力衰竭/心力衰竭严重程度患者的病因特定死亡率和CKD/HF疾病进展率。第三，基于从原始数据建模和文献综述中获得的转移概率，我们将使用马尔科夫模型估计ICD治疗对不同年龄、心衰/慢性肾脏病严重程度患者的终生益处。这将通过以下方式实现：首先构建一个马尔可夫模型，该模型将估计没有ICD的患者的结果，并使用SOLVD数据和ICD试验(多中心自动除颤器试验，MADIT-II)的控制臂来校准这个非ICD模型。然后，我们将把ICD治疗的影响纳入马尔可夫模型，并在MADIT-II数据上校准完整的ICD和非ICD模型。最终和校准的马尔可夫模型将被用来估计ICD治疗在几个类别的心力衰竭/慢性肾脏病严重程度和年龄方面的预期寿命效益，包括在ICD试验中代表性不足的重度CKD/心力衰竭患者。

DESCRIPTION (provided by applicant): Recognizing the challenge in optimizing evidence-based interventions in complex patients with multiple comorbidities, we propose to evaluate optimal utilization of the implantable cardiac defibrillator (ICD) in individuals with heart failure (HF) and chronic kidney disease (CKD). The proposed project is of critical relevance to the US healthcare system given the increasing prevalence, and common coexistence, of HF and CKD, and the paucity of data from clinical trials and practice guidelines to specifically guide therapy in such complex patients. Of particular importance is the association between CKD and increased risk of competing modes of death, other than sudden cardiac death (SCD), which can alter the overall effectiveness of the ICD in patients with these two chronic conditions. Therefore, the aims of this modeling study are three-fold. First, we will use primary data from a HF population without an ICD enrolled in the Study of Left Ventricular Dysfunction (SOLVD) trials to estimate how variation in severity of HF and CKD affect the risk of cause-specific mortality (SCD, non-arrhythmic cardiac death, and non-cardiac mortality), and estimate the rates of CKD and HF disease progression. This will be achieved using competing risks proportional hazards regressions to model the three cause-specific modes of death across several categories of HF/CKD severity and age, and proportional hazards regression models to estimate HF/CKD progression. Second, we will validate and expand on our estimates from the primary data modeling based on a systematic literature review/data synthesis focusing on risks/benefits of ICD therapy, and rates of cause-specific mortality and CKD/HF disease progression in patients of varying age, and HF/CKD severity. Third, based on the transition probabilities obtained from the primary data modeling and literature review, we will estimate the lifetime benefits of ICD therapy in patients of varying age, HF/CKD severity using a Markov model. This will be accomplished by initially constructing a Markov model that will estimate the outcomes in patients without ICDs, and calibrating this non-ICD model with the SOLVD data and the control arm of an ICD trial (the Multicenter Automatic Defibrillator Trial, MADIT-II). We will then incorporate the effect of ICD treatment into the Markov model, and calibrate the full ICD versus no-ICD model on the MADIT-II data. The final and calibrated Markov model will then be used to estimate life-expectancy benefit of ICD therapy across several categories of HF/CKD severity and age, including in patients with severe CKD/HF who were under-represented in ICD trials.