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: 9977674
• 负责人: Koen Nieman
• 金额: $ 80.58万
• 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/9977674
• 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; Diagnostic Imaging; Diagnostic Procedure; Disease; Echocardiography; Event; Family member; Foundations; Fractals; Genetic; Genetic Structures; 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 Intervention; Preventive care; Preventive measure; Preventive therapy; Public Health; Research; Resolution; Risk; Risk Factors; Risk stratification; 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; disability; embolic stroke; genetic pedigree; heart imaging; high risk; imaging genetics; implantation; improved; improved outcome; innovation; multimodality; novel; outcome prediction; patient subsets; predictive modeling; prognostic value; prospective; radiomics; research clinical testing; risk prediction model; 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.

总结 致密化不全性心肌病（NCCM）是一种异质性的、知之甚少的疾病， 疏松心肌组织的突出内层。NCCM患者发生心力衰竭、中风 严重的心律失常和死亡目前的成像技术无法区分病理性和良性 过度小梁形成此外，目前基于超声/MRI的NCCM标准导致NCCM的过度诊断， 不提供预后价值，并导致植入除颤器（ICD）和使用抗凝治疗， 对许多其他方面健康的非致密心肌个体有积极的益处。也日渐增加 诊断为NCCM的人群迫切需要更好的风险分层，以适当分配（或 这些有效的预防措施。我们的国际财团结合了专业知识， 心肌疾病，心脏成像和先进的计算机分析，来自5个中心，致力于 心肌病患者，并有最终的目标，以改善非压实患者的护理 心肌病我们假设，综合分析临床、遗传、结构和功能 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 用于鉴别致密化不全的病理性和良性模式。在一个大型的成年患者队列中， 疑似NCCM，我们将进行深入的表型分析，包括临床信息，谱系数据，遗传学， 超声心动图和MRI，并随访患者长达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检查， 心肌壁的表征。将开发新的分析方法来表征3D 基于病理生理学假设的结构复杂性，但也使用无假设的原始分析 使用深度学习技术的图像。此外，我们还将研究心肌梗死与心肌梗死之间的关系。 结构和区域收缩功能，如通过回声和MRI评估的。本提案的目的是确定 与病理性非压实相关的结构特征，并改善开发风险预测 模型这个致力于NCCM的国际财团将是第一个收集遗传和多模态 成像数据。通过创新的成像技术发现病理结构特征， 对心肌收缩力的影响，将促进我们对NCCM的理解。临床上迫切需要 诊断技术，检测病理性致密化不全，并确定患者的破坏性风险， 并发症这项研究是临床相关的，因为开发的预测模型可能允许更多 有效的，个性化的预防性护理在越来越多的人诊断为非压实 心肌病