Secondary Analyses to Support the Rational Design of Clinical Trials in Chronic Lung Allograft Dysfunction

支持慢性同种异体肺功能障碍临床试验合理设计的二次分析

基本信息

批准号：
10586182
负责人：
Jamie Lynn Todd
金额：
$ 24.15万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-16 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10586182
关键词：
Address Adult American Bayesian Method Behavior Cessation of life Characteristics Chronic Clinical Clinical Data Clinical Trials Clinical Trials Design Collaborations Communities Complement Complication Cox Proportional Hazards Models Data Data Set Diagnosis Disease Disease Progression Eligibility Determination Enrollment Ethics Event Exhibits Functional disorder Funding Future Goals Heterogeneity Immune Immune response Industry Intervention Intervention Trial Investigation Literature Lung Lung Transplantation Lung diseases Measures Mediating Methods Modeling Modernization National Heart, Lung, and Blood Institute National Institute of Allergy and Infectious Disease Natural History Organ Transplantation Outcome Participant Patient Selection Patients Pattern Phenotype Population Progressive Disease Publishing Pulmonary function tests Randomized Rare Diseases Research Risk Factors Sample Size Source Statistical Methods Statistical Models Surrogate Endpoint Time Transplant Recipients Transplantation Transplanted Lung Complication United States National Institutes of Health Work arm cohort data integration design follow-up graft dysfunction high risk improved information gathering innovation interest longitudinal analysis lung allograft mortality risk multidisciplinary placebo controlled study pre-clinical pulmonary function pulmonary function decline rational design response secondary analysis simulation standard of care statistics success transplant centers treatment effect treatment trial trial design

项目摘要

Long-term outcomes for lung transplant recipients remain inadequate. Chronic lung allograft dysfunction (CLAD) is the most important immune-mediated complication of lung transplantation, representing the leading cause of late death among lung recipients. CLAD is diagnosed upon a sustained decline in lung function measures, yet the clinical course after diagnosis is highly variable. Despite the burden of CLAD, there remain no approved therapies. In part, this relates to difficulties in the design of CLAD trials due to a paucity of precise, generalizable information on CLAD progression under standard-of-care (SOC) conditions and lack of established surrogate endpoints for use in CLAD treatment studies. An additional barrier to advancing CLAD trials is the large number of participants needed and ethical concerns regarding enrollment in placebo-controlled studies for a progressive disease with substantial mortality risk. To address these gaps this proposal will create the largest multicenter cohort of lung recipients with CLAD available in the published literature and perform analyses that will inform the rational design of future CLAD trials. Specifically, to be responsive to this Notice of Special Interest (NOSI) this proposal will integrate longitudinal clinical data collected on adult lung recipients through a prior NIAID-funded Clinical Trials in Organ Transplantation (CTOT) study, CTOT-20, and through the NHLBI-funded Lung Transplant Outcomes Group (LTOG) study. Using these rich data, Aim 1 will precisely define the natural history of lung function progression after CLAD and employ latent class analyses of longitudinal lung function measures to discover subphenotypes of CLAD progression, incorporating other clinical variables that may influence disease behavior into the analytical framework. Plausible surrogate lung function endpoints for CLAD trials will be identified by assessing the association between each identified class and graft loss/death using landmarked Cox proportional hazards models. Aim 2 will evaluate the practical use of these real-world lung transplant datasets as a source of external SOC controls to complement future CLAD trials. Key eligibility criteria and endpoints assimilated from ongoing CLAD trials will be applied to evaluate how many patients from CTOT-20 or LTOG could potentially be included as real-world external controls for each trial. Simulations will then be conducted to assess operating characteristics when the external controls are used in hypothetical CLAD trials. This research is innovative because it represents a substantive departure from the existing state of CLAD investigation, namely in the application of modern statistical methods, such as latent class methods, to a large multicenter CLAD dataset and in the examination of innovative concepts, such as the use of external controls from historical data, a model of increasing interest to the FDA and industry. This research is significant because it will establish the natural history of CLAD and resolve the heterogeneity in CLAD progression in a multicenter real-world cohort; informing the design of future interventional CLAD trials in a way that is generalizable across center practices. Thus, aligned with the goals of this NOSI, these data are expected to improve the success of future CLAD trials.

肺移植受者的长期结果仍然不足。慢性同种异体肺移植功能障碍（CLAD）是肺移植最重要的免疫介导并发症，是导致肺移植失败的主要原因肺移植受者的晚期死亡。 CLAD 是根据肺功能指标持续下降而诊断的，但诊断后的临床病程变化很大。尽管有 CLAD 的负担，但仍然没有获得批准疗法。在某种程度上，这与 CLAD 试验设计中的困难有关，因为缺乏精确的、可推广的有关标准护理 (SOC) 条件下 CLAD 进展以及缺乏既定替代方案的信息用于 CLAD 治疗研究的终点。推进 CLAD 试验的另一个障碍是数量庞大需要的参与者数量以及关于参加安慰剂对照研究的伦理问题，以实现渐进的研究具有重大死亡风险的疾病。为了解决这些差距，该提案将创建最大的多中心已发表文献中提供的患有 CLAD 的肺接受者队列，并进行分析以告知未来 CLAD 试验的合理设计。具体来说，为了响应本特别利益通知 (NOSI)，该提案将整合通过先前由 NIAID 资助的项目收集的成年肺受者的纵向临床数据器官移植临床试验 (CTOT) 研究 CTOT-20 以及通过 NHLBI 资助的肺移植结果组 (LTOG) 研究。利用这些丰富的数据，目标 1 将精确定义肺的自然史 CLAD 后的功能进展并采用纵向肺功能测量的潜在类别分析发现 CLAD 进展的亚表型，结合可能影响疾病的其他临床变量行为纳入分析框架。 CLAD 试验的合理替代肺功能终点将是通过使用地标 Cox 评估每个确定的类别与移植物丢失/死亡之间的关联来确定比例风险模型。目标 2 将评估这些真实肺移植数据集的实际用途作为外部 SOC 控制的来源，以补充未来的 CLAD 试验。关键资格标准和终点从正在进行的 CLAD 试验中吸收的数据将用于评估有多少患者来自 CTOT-20 或 LTOG 可能会被纳入每次试验的现实世界外部对照中。然后将进行模拟评估在假设的 CLAD 试验中使用外部对照时的操作特征。这项研究之所以具有创新性，是因为它代表了与 CLAD 调查现有状态的实质性背离，即现代统计方法（例如潜在类方法）在大型多中心 CLAD 中的应用数据集和创新概念的检验，例如使用历史数据的外部控制， FDA 和行业越来越感兴趣的模型。这项研究意义重大，因为它将建立 CLAD 的自然史并解决多中心现实世界队列中 CLAD 进展的异质性；以可在中心实践中推广的方式为未来的介入性 CLAD 试验的设计提供信息。因此，根据 NOSI 的目标，这些数据预计将提高未来 CLAD 试验的成功率。