Implanted Cardiac Defibrillators for Heart Failure Patients with Kidney Disease

描述（由申请人提供）：认识到在具有多种合并症的复杂患者中优化循证干预的挑战，我们建议评价植入式心脏除颤器（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HF和CKD的患病率日益增加和共同共存，以及缺乏临床试验和实践指南的数据来专门指导此类复杂患者的治疗，拟议的项目与美国医疗保健系统具有关键相关性。特别重要的是CKD与竞争性死亡模式（心源性猝死（SCD）除外）风险增加之间的相关性，这可能会改变ICD在这两种慢性疾病患者中的总体有效性。因此，本建模研究的目的有三个方面。首先，我们将使用入组左心室功能障碍研究（SOLVD）试验的未植入ICD的HF人群的主要数据，以估计HF和CKD严重程度的变化如何影响病因特异性死亡率（SCD、非心源性心源性死亡和非心源性死亡）的风险，并估计CKD和HF疾病进展的发生率。这将通过使用竞争风险比例风险回归模型对HF/CKD严重程度和年龄的几个类别中的三种死因特异性死亡模式进行建模，并使用比例风险回归模型估计HF/CKD进展来实现。其次，我们将基于系统性文献综述/数据综合，验证并扩展我们的主要数据模型估计值，重点关注ICD治疗的风险/获益、不同年龄患者的病因特异性死亡率和CKD/HF疾病进展率以及HF/CKD严重程度。第三，基于从原始数据建模和文献综述中获得的转移概率，我们将使用马尔可夫模型估计ICD治疗在不同年龄、HF/CKD严重程度患者中的终身受益。这将通过初步构建马尔可夫模型来实现，该模型将估计未植入ICD的患者的结局，并使用SOLVD数据和ICD试验（多中心自动除颤器试验，MADIT-II）的对照组校准该非ICD模型。然后，我们将ICD治疗的影响纳入马尔可夫模型，并校准完整的ICD与无ICD模型的MADIT-II数据。然后将使用最终和校准的马尔可夫模型来估计ICD治疗在几种HF/CKD严重程度和年龄类别中的预期寿命获益，包括在ICD试验中代表性不足的重度CKD/HF患者中。

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.