International Consortium for Multimodality Phenotyping in Adults with Non-compaction

国际非致密化成人多模态表型联盟

• 批准号: 10452602
• 负责人: Koen Nieman
• 金额: $ 72.35万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-16 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10452602
• 关键词: 3-Dimensional; Adult; Adverse event; Anticoagulation; Architecture; Arrhythmia; Artificial Intelligence; Awareness; Behavior; Benign; Cardiac; Cardiomyopathies; Cessation of life; Clinic; Clinical; Complex; Computers; DNA Sequence Alteration; Data; Data Analyses; Defibrillators; Detection; Diagnosis; Diagnostic Imaging; Diagnostic Procedure; Disease; Echocardiography; Event; Family member; Foundations; Fractals; Genetic; Goals; Guidelines; Health; Heart failure; Histologic; Image; Image Analysis; Imaging Techniques; Individual; International; Intervention; Joints; Knowledge; Lead; Left ventricular non-compaction; Life; Machine Learning; Magnetic Resonance Imaging; Measures; Medical Genetics; Mission; Modeling; Multimodal Imaging; Mutation; Myocardial; Myocardial tissue; Myocardium; Nature; Non-compaction cardiomyopathy; Organism; Outcome; Pathologic; Patient Care; Patients; Pattern; Pennsylvania; Performance; Phenotype; Physiological; Population; Predictive Value; Preventive care; Preventive measure; Preventive therapy; Public Health; Research; Resolution; Risk; Risk Factors; Savings; Stroke; Structure; Sudden Death; Techniques; United States National Institutes of Health; Universities; Ventricular Arrhythmia; accurate diagnosis; adjudicate; adjudication; adverse event risk; analytical method; base; clinically relevant; cohort; deep learning; density; diagnostic criteria; disability; embolic stroke; genetic pedigree; heart imaging; high risk; imaging genetics; implantation; improved; improved outcome; innovation; machine learning model; multimodality; novel; outcome prediction; patient subsets; predictive modeling; preventive intervention; prognostic value; prospective; radiomics; research clinical testing; risk prediction; risk prediction model; risk stratification; sudden cardiac death; thrombogenesis; young adult

Summary Non-compaction cardiomyopathy (NCCM) is a heterogeneous, poorly understood disorder characterized by a prominent inner layer of loose myocardial tissue. Patients with NCCM are at increased risk of heart failure, stroke, severe rhythm irregularities and death. Current imaging techniques cannot differentiate pathological from benign hypertrabeculation. In addition, current echo/MRI-based criteria of NCCM lead to over-diagnosis of NCCM, provide no prognostic value, and lead to implantation of defibrillators (ICD) and the use of anticoagulation without a positive benefit in many otherwise healthy individuals with non-compacted myocardium. For a growing population diagnosed with NCCM there is an urgent need for better risk stratification to appropriately allocate (or safely withhold) these impactful preventive measures. Our international consortium combines expertise in myocardial disease, cardiac imaging and advanced computer analytics from 5 centers dedicated to the care of patients with cardiomyopathies, and has the ultimate goal to improve care of patients with non-compaction cardiomyopathy. We hypothesize that comprehensive analysis of clinical, genetic, structural and functional information will improve risk stratification. In addition, we hypothesize that detailed structural analysis will allow for differentiation of pathological and benign patterns of non-compaction. In a large cohort of adult patients with suspected NCCM we will perform in-depth phenotyping, including clinical information, pedigree data, genetics, echocardiography and MRI, and follow patients for up to 3 years. We will apply machine-learning based analytics to develop predictive models and compare their performance to currently used models and treatment criteria. Secondly, in a subset of patients we will perform high-resolution cardiac CT for detailed structural characterization of the myocardial wall. Novel analytical methods will be developed to characterize the 3D architectural complexity based on pathophysiological hypothesis, but also using hypothesis-free analyses of raw images using deep learning techniques. In addition, we will investigate associations between myocardial structure and regional contractile function, as assessed by echo and MRI. The aim of this proposal is to identify a structural signature associated with pathological non-compaction and improve developed risk prediction models. This international consortium dedicated to NCCM will be the first to collect genetic and multi-modality imaging data. Discovery of pathological structural signatures through innovative imaging techniques, in relation to myocardial contractility, will advance our understanding of NCCM. There is an urgent clinical need for diagnostic techniques that detect pathological non-compaction and identify patients at risk of devastating complications. This study is clinically relevant because the developed predictive models may permit more effective, personalized preventive care in a growing number of individuals diagnosed with non-compaction cardiomyopathy.

概括 非兼容心肌病（NCCM）是一种异质性，知名度不足的疾病，其特征是 松散心肌组织的突出内层。 NCCM患者患心力衰竭，中风的风险增加， 严重的节奏不规则和死亡。当前的成像技术无法区分病理学与良性 超收抵免率。此外，当前基于回声/MRI的NCCM标准导致NCCM过度诊断， 不提供预后价值，并导致除颤器（ICD）的植入和使用抗凝作用 对于许多其他健康的非压缩心肌的人来说，积极的好处。为了成长 被诊断为NCCM的人群迫切需要更好的风险分层以适当分配（或 安全地扣留这些有影响力的预防措施。我们的国际财团结合了专业知识 来自5个专门护理的5个中心的心肌疾病，心脏成像和高级计算机分析 心肌病的患者，并且具有改善无菌患者护理的最终目标 心肌病。我们假设对临床，遗传，结构和功能的全面分析 信息将改善风险分层。此外，我们假设详细的结构分析将允许 用于区分不复杂的病理和良性模式。在大量的成年患者中 怀疑的NCCM我们将执行深入的表型，包括临床信息，谱系数据，遗传学， 超声心动图和MRI，并关注患者长达3年。我们将应用基于机器学习的分析 开发预测模型并将其性能与当前使用的模型和治疗标准进行比较。 其次，在一部分患者中，我们将进行高分辨率心脏CT以详细结构 心肌壁的特征。将开发新的分析方法来表征3D 基于病理生理假设的建筑复杂性，但也使用原始假设分析 使用深度学习技术的图像。此外，我们将研究心肌之间的关联 结构和区域收缩功能，如Echo和MRI评估。该提议的目的是确定 与病理不复杂并改善开发风险预测相关的结构签名 型号。这个致力于NCCM的国际财团将是第一个收集遗传和多模式的联盟 成像数据。通过创新成像技术发现病理结构签名 对于心肌收缩，将提高我们对NCCM的理解。紧急临床需要 诊断技术检测病理不复杂并确定有毁灭性风险的患者 并发症。这项研究在临床上是相关的，因为开发的预测模型可能允许更多 有效的个性化预防性护理，越来越多的人被诊断 心肌病。