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.

项目总结/摘要 这是马约诊所作为HeartShare参与NIH HeartShare研究联盟的申请 临床中心（CC）。我们的目标是与其他HeartShare研究人员合作， 射血分数保留性心力衰竭（HF）的病理生理学，并发现新的诊断方法 和治疗方法。多种病理生理过程可能最终导致不同的HFpEF 表型，但具体机制仍在很大程度上不明确。也不知道标准是否 临床信息可以识别具有不同机械病因的患者，这对于提供 靶向治疗在临床试验中，并最终在临床实践中。我们的提案概述了四个具体目标。在 具体目标1：我们记录了马约诊所拥有资源，马约心连心团队拥有 在HFpEF和相关疾病、临床研究、患者 招聘和保留，数据科学和协作团队科学，以帮助推动成功 HeartShare Network.在具体目标2：我们提出了一个广泛的机制表型方案， 反映衰老、全身性疾病过程和多器官完整性的定量变量（L2 数据），它们被用作无监督机器学习（ML）模型的输入变量。我们假设 该方法将允许鉴定独特的HFpEF病理生理表型组（簇）。我们也 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内的子集中获得脂肪和骨骼肌组织表征（L3数据） 病理生理表型群我们假设这些L3数据将增强靶向治疗药物的识别， 战略布局最后，我们使用EHR数据概述了监督ML，以开发可自动化的算法， 鉴定使用L2数据导出的HeartShare HFpEF病理生理表型组。我们假设如果 如果成功，这种方法将通过允许自动识别来增强HeartShare发现的翻译 不同HFpEF表型组中的患者招募入靶向其特异性的药物的临床试验， 病理生理学在具体目标3中：我们提出单独使用循环蛋白（n=5000;由 基于SOMAScanTM适体的平台）作为无监督ML模型的输入变量将识别独特的 HFpEF病理生理表型（簇）。在具体目标4中：我们概述了马约心分享研究 技能发展方案。为HFpEF临床研究者提供短期的强化沉浸 通过与马约心血管疾病AI实习的数据科学家实习生合作获得经验，或 一个长期的数据科学专门计划，作为马约克恩中心学者在数据科学将装备一个 新一代HFpEF研究人员拥有强大的数据科学工具箱，以推动未来的发现。