Parametric Response Mapping (PRM) for the detection of chronic lung injury in hematopoietic cell transplant recipients

用于检测造血细胞移植受者慢性肺损伤的参数响应图 (PRM)

• 批准号: 10683080
• 负责人: Craig J Galban
• 金额: $ 78.2万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10683080
• 关键词: Accounting; Adult; Age; Airway Disease; Allogenic; Biological Markers; Bone Marrow Transplantation; Bronchiolitis Obliterans; Cessation of life; Child; Childhood; Chronic; Chronic Lung Injury; Chronic Obstructive Pulmonary Disease; Clinical; Complication; Data; Detection; Development; Diagnosis; Diagnostic; Disease; Disease Progression; Dyspnea; Early Diagnosis; Functional disorder; High Resolution Computed Tomography; Image; Intervention; Lung; Lung diseases; Machine Learning; Maps; Measurement; Measures; Medicine; Methodology; Michigan; Modeling; Morbidity - disease rate; Multicenter Trials; Observational Study; Outcome; Patient Care; Patients; Phase; Population; Prospective Studies; Pulmonary Pathology; Pulmonary function tests; Quality of life; Reporting; Research Personnel; Risk; Scanning; Serology; Serum; Structure of parenchyma of lung; Survival Rate; Syndrome; Techniques; Technology; Testing; Time; Training; Transplant Recipients; Transplantation; United States National Institutes of Health; Universities; X-Ray Computed Tomography; cell injury; chronic graft versus host disease; clinical development; data repository; design; diagnostic value; disorder subtype; functional decline; graft vs host disease; hematopoietic cell transplantation; high risk; improved; learning strategy; lung allograft; lung development; lung injury; machine learning model; mortality; novel; patient stratification; pediatric patients; pneumonitis and fibrosis; post-transplant; precision medicine; prevent; primary endpoint; prospective; pulmonary function; pulmonary function decline; recurrent infection; response; small airways disease; Accounting; Adult; Age; Airway Disease; Allogenic; Biological Markers; Bone Marrow Transplantation; Bronchiolitis Obliterans; Cessation of life; Child; Childhood; Chronic; Chronic Lung Injury; Chronic Obstructive Pulmonary Disease; Clinical; Complication; Data; Detection; Development; Diagnosis; Diagnostic; Disease; Disease Progression; Dyspnea; Early Diagnosis; Functional disorder; High Resolution Computed Tomography; Image; Intervention; Lung; Lung diseases; Machine Learning; Maps; Measurement; Measures; Medicine; Methodology; Michigan; Modeling; Morbidity - disease rate; Multicenter Trials; Observational Study; Outcome; Patient Care; Patients; Phase; Population; Prospective Studies; Pulmonary Pathology; Pulmonary function tests; Quality of life; Reporting; Research Personnel; Risk; Scanning; Serology; Serum; Structure of parenchyma of lung; Survival Rate; Syndrome; Techniques; Technology; Testing; Time; Training; Transplant Recipients; Transplantation; United States National Institutes of Health; Universities; X-Ray Computed Tomography; cell injury; chronic graft versus host disease; clinical development; data repository; design; diagnostic value; disorder subtype; functional decline; graft vs host disease; hematopoietic cell transplantation; high risk; improved; learning strategy; lung allograft; lung development; lung injury; machine learning model; mortality; novel; patient stratification; pediatric patients; pneumonitis and fibrosis; post-transplant; precision medicine; prevent; primary endpoint; prospective; pulmonary function; pulmonary function decline; recurrent infection; response; small airways disease

ABSTRACT Chronic lung dysfunction (CLD) is a potentially severe complication of bone marrow transplantation (BMT), particularly prevalent in patients who develop chronic graft versus host disease (GVHD) post-BMT. The development of CLD is associated with a 20%-40% long term survival rate, with the majority of patients diagnosed with CLD only after they have developed significant (and potentially irreversible) lung pathology. There is a critical need to identify CLD earlier in its clinical course, at a stage when treatment may prove beneficial to patients. Parametric response mapping (PRM) is a novel computed tomography (CT) based methodology that when applied to standard CT scans is capable of identifying and quantifying a variety of lung disease subtypes in patients. This technology was initially developed at Michigan Medicine over 10 years ago for patients with chronic obstructive pulmonary disease (COPD). In both single center and multicenter trials, PRM has demonstrated efficacy for the identification of CLD in adult BMT patients. Our central hypothesis is that PRM can identify patients with chronic GVHD who are at high risk for the development of CLD. We propose an observational study to examine PRM in adult and pediatric subjects (≥ 3 years in age) with chronic GVHD post- BMT. To test our hypothesis, we have established 3 specific aims. In the first two, we will examine PRM values at two main time points: at the onset of chronic GVHD (Aim 1) and at the onset of CLD (Aim 2), with CLD defined by standard NIH criteria. In Aim 3, we will develop a machine learning model that will incorporate serologic and pulmonary function test (PFT) biomarkers with PRM values to develop a composite biomarker strategy. Upon completion of our current proposal, we will establish PRM as a predictor of CLD in adult BMT patients. In addition, the PRM, PFT, and serologic data obtained from pediatric BMT patients will provide a data bank for future research in this population. Historically, the ability to identify CLD in pediatric patients has been PFT-dependent, with PFTs technically challenging to conduct in young children. The use of PRM as a diagnostic indicator of CLD in pediatric BMT patients will be a major advance of this proposal. Finally, our proposal takes our PRM methodology forward in adults, allowing us to study the trajectory of lung disease in adult BMT recipients with chronic GVHD, and following the onset of CLD.

抽象的 慢性肺功能障碍（CLD）是骨髓移植（BMT）的潜在严重并发症， BMT后发生慢性移植与宿主疾病（GVHD）的患者特别普遍。这 CLD的发展与20％-40％的长期存活率有关，大多数患者 仅在CLD患有明显（且可能不可逆的）肺病理学之后才被诊断出患有CLD。 在临床过程中，迫切需要识别CLD，在治疗可能证明有益的阶段 给患者。参数响应映射（PRM）是一种基于新型计算机断层扫描（CT）的方法论 当应用于标准CT扫描时，能够识别和量化各种肺部疾病亚型 在患者中。这项技术最初是在10年前在密歇根医学开发的 慢性阻塞性肺疾病（COPD）。在单中心和多中心试验中，PRM具有 在成年BMT患者中鉴定CLD的效率。我们的中心假设是PRM可以 识别患有CLD发展风险高风险的慢性GVHD患者。我们提出了一个 观察性研究以检查成人和儿科受试者的PRM（年龄≥3岁）患有慢性GVHD。 BMT。为了检验我们的假设，我们已经建立了3个具体目标。在前两个中，我们将检查PRM值 在两个主要时间点：在慢性GVHD（AIM 1）和CLD的发作（AIM 2）开始，CLD定义 按标准NIH标准。在AIM 3中，我们将开发一种机器学习模型，该模型将结合血清学和 具有PRM值的肺功能测试（PFT）生物标志物，以制定复合生物标志物策略。之上 完成当前建议的完成，我们将在成年BMT患者中建立PRM作为CLD的预测指标。此外， 从小儿BMT患者获得的PRM，PFT和血清学数据将为将来提供数据库 研究这个人群。从历史上看，鉴定小儿患者CLD的能力一直依赖于PFT， 随着PFT在技术上挑战，要对幼儿进行。使用PRM作为CLD的诊断指标 在小儿BMT中，患者将是该提案的重大进步。最后，我们的建议将我们的PRM带到 成年人的方法论，使我们能够研究成人BMT接受者的肺疾病轨迹 慢性GVHD，遵循CLD的发作。