Mayo Clinic HeartShare Clinical Center

梅奥诊所 HeartShare 临床中心

• 批准号: 10679096
• 负责人: Barry A. Borlaug
• 金额: $ 28.1万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10679096
• 关键词: Acceleration; Adipose tissue; Algorithms; Artificial Intelligence; Automobile Driving; Belief; Biological; Businesses; Cardiac; Cardiovascular Diseases; Clinic; Clinical; Clinical Data; Clinical Investigator; Clinical Research; Clinical Trials; Collaborations; Communities; Complex; Consensus; Data; Data Collection; Data Science; Data Scientist; Data Set; Dedications; Diagnosis; Disease; EFRAC; Enrollment; Etiology; Functional disorder; Future; Generations; Goals; Heart; Heart failure; Immersion; Industry; Internships; Intervention Trial; Machine Learning; Medical; Methods; Mission; Modeling; Muscle; Myocardial; Organ; Patient Recruitments; Patients; Pattern; Phenotype; Physiological; Process; Productivity; Program Development; Proteins; Proteomics; Protocols documentation; Public Health; Research; Research Personnel; Resources; Science; Skeletal Muscle; Specific qualifier value; Systemic disease; Technology; Testing; Therapeutic; Translations; United States National Institutes of Health; aptamer; circulating biomarkers; clinical center; clinical practice; complex data; effective therapy; experience; hemodynamics; large datasets; machine learning model; novel diagnostics; novel marker; novel therapeutic intervention; patient retention; preservation; programs; senescence; skill acquisition; success; supervised learning; targeted agent; targeted treatment; unsupervised learning

PROJECT SUMMARY/ABSTRACT This is Mayo Clinic’s application to participate in the NIH HeartShare Research Consortium as a HeartShare Clinical Center (CC). Our goal is to collaborate with the other HeartShare Investigators to elucidate the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF) and discover novel diagnostic and therapeutic approaches. Multiple pathophysiologic processes may ultimately lead to different HFpEF phenotypes, though the specific mechanisms remain largely undefined. It is also not known whether standard clinical information can identify patients with different mechanistic etiologies, which is necessary to provide targeted therapies in clinical trials and eventually in clinical practice. Our proposal outlines four specific aims. In Specific Aim 1: We document that Mayo Clinic has the resources and the Mayo HeartShare Team has the expertise and track record of productivity in HFpEF and relevant related diseases, clinical research, patient recruitment and retention, data science, and collaborative team science to help drive the success of HeartShare Network. In Specific Aim 2: We propose a broad mechanistic phenotyping protocol providing quantitative variables reflective of senescence, systemic disease processes, and multi-organ integrity (L2 data), which are used as input variables in unsupervised machine learning (ML) models. We hypothesize that this approach will allow identification of unique HFpEF pathophysiologic phenogroups (clusters). We also propose invasive hemodynamic signatures, trans-cardiac gradients of circulating biomarkers and myocardial, adipose and skeletal muscle tissue characterization (L3 data) be obtained in a subset within each HFpEF pathophysiologic phenogroup. We hypothesize these L3 data will enhance identification of targeted therapeutic strategies. Lastly, we outline supervised ML using EHR data to develop automatable algorithms to accurately identify the HeartShare HFpEF pathophysiological phenogroups derived using L2 data. We hypothesize that if successful, this approach will enhance translation of HeartShare findings by allowing automated identification of patients in the different HFpEF phenogroups for enrollment in clinical trials of agents targeting their specific pathophysiology. In Specific Aim 3: We propose that use of circulating proteins alone (n=5000; defined by the SOMAScanTM Aptamer based platform) as input variables for unsupervised ML models will identify unique HFpEF pathophysiologic phenotypes (clusters). In Specific Aim 4: We outline the Mayo HeartShare Research Skills Development Program. Providing HFpEF clinical investigators a short-term intensive immersion experience by collaboration with a data scientist intern in the Mayo Cardiovascular Disease AI Internship or a long term dedicated program in data science as a Mayo Kern Center Scholar in Data Science will equip a new generation of HFpEF investigators with a robust data science toolbox to drive future discovery.

项目概要/摘要 这是梅奥诊所作为 HeartShare 参与 NIH HeartShare 研究联盟的申请 我们的目标是与其他 HeartShare 研究人员合作阐明临床中心 (CC)。 射血分数保留的心力衰竭（HF）的病理生理学（HFpEF）并发现新的诊断方法 多种病理生理过程可能最终导致不同的 HFpEF。 表型，但具体机制在很大程度上仍不确定。是否标准也不清楚。 临床信息可以识别具有不同机制病因的患者，这对于提供 我们的建议概述了临床试验中的靶向治疗以及最终的临床实践的四个具体目标。 具体目标 1：我们记录梅奥诊所拥有资源，梅奥 HeartShare 团队拥有 HFpEF 和相关疾病、临床研究、患者的专业知识和生产力跟踪记录 招聘和保留、数据科学和协作团队科学，以帮助推动成功 HeartShare 网络。具体目标 2：我们提出了一个广泛的机械表型分析协议，提供 反映衰老、全身疾病过程和多器官完整性的定量变量（L2 数据），用作无监督机器学习（ML）模型中的输入变量。 这种方法将允许识别独特的 HFpEF 病理生理表型组（簇）。 提出侵入性血流动力学特征、循环生物标志物和心肌的跨心脏梯度， 在每个 HFpEF 的子集中获得脂肪和骨骼肌组织特征（L3 数据） 我们探索这些 L3 数据将增强靶向治疗的识别。 最后，我们概述了使用 EHR 数据的监督机器学习来开发自动化算法，以准确地实现目标。 我们捕获了使用 L2 数据得出的 HeartShare HFpEF 病理生理表型组。 成功后，这种方法将通过允许自动识别来增强 HeartShare 研究结果的转化 不同 HFpEF 表型组的患者参加针对其特定药物的临床试验 在具体目标 3 中：我们建议单独使用循环蛋白（n=5000；由 基于 SOMAScanTM Aptamer 的平台）作为无监督 ML 模型的输入变量将识别唯一的 HFpEF 病理生理学表型（簇）。具体目标 4：我们概述了 Mayo HeartShare 研究。 为 HFpEF 临床研究人员提供短期强化沉浸式技能发展计划。 通过与 Mayo 心血管疾病 AI 实习中的数据科学家实习生合作获得经验，或 作为梅奥·克恩中心数据科学学者，数据科学领域的长期专门项目将配备 新一代 HFpEF 研究人员拥有强大的数据科学工具箱来推动未来的发现。