Mayo Clinic HeartShare Clinical Center

梅奥诊所 HeartShare 临床中心

• 批准号: 10323121
• 负责人: Barry A. Borlaug
• 金额: $ 28.08万
• 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/10323121
• 关键词: Address; 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; Diagnosis; Disease; EFRAC; Enrollment; Etiology; Functional disorder; Future; Generations; Goals; Heart; Heart failure; Immersion; Industry; Internships; Intervention Trial; Lead; Machine Learning; Medical; Methods; Mission; Modeling; Muscle; Myocardial; 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; base; circulating biomarkers; clinical center; clinical practice; complex data; effective therapy; experience; hemodynamics; large datasets; novel diagnostics; novel marker; novel therapeutic intervention; 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.

项目摘要/摘要 这是梅奥诊所（Mayo Clinic 临床中心（CC）。我们的目标是与其他心脏调查人员合作，以阐明 具有保留的射血分数（HFPEF）的心力衰竭的病理生理（HF）并发现新型诊断 和治疗方法。多个病理生理过程最终可能导致不同的HFPEF 表型，尽管具体机制在很大程度上不确定。也不知道是否标准 临床信息可以识别具有不同机理病因的患者，这是提供的 在临床试验中，最终在临床实践中采用靶向疗法。我们的建议概述了四个具体目标。在 特定目标1：我们记录了梅奥诊所拥有资源，而梅奥的心夏团队有 HFPEF和相关疾病，临床研究，患者的生产力的专业知识和记录 招聘和保留，数据科学和协作团队科学，以帮助推动 心莎威网络。在特定目的2中：我们提出了一个广泛的机械表型协议提供 定量变量反映了感应，全身性疾病过程和多器官完整性（L2） 数据），在无监督的机器学习（ML）模型中用作输入变量。我们假设这一点 这种方法将允许识别独特的HFPEF病理生理现象（簇）。我们也是 建议侵入性血液动力学特征，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） 病理生理现象。我们假设这些L3数据将增强对目标治疗的识别 策略。最后，我们使用EHR数据概述了监督ML，以开发自动算法以准确 识别使用L2数据得出的心脏夏形HFPEF病理生理现象。我们假设如果 成功，这种方法将通过允许自动识别来增强心脏病的翻译 不同HFPEF现象中的患者在针对其特定的药物的临床试验中入学 病理生理学。在特定目标3中：我们建议单独使用循环蛋白（n = 5000；由 基于somascantm apamer的平台）作为无监督ML模型的输入变量，将确定独特的 HFPEF病理生理表型（簇）。在特定目的4：我们概述了梅奥心脏研究 技能开发计划。提供HFPEF临床研究人员短期密集浸入 与蛋黄酱心血管疾病AI实习的数据科学家实习生合作的经验 作为数据科学的长期专门计划，作为数据科学的梅奥·克恩中心学者将装备 新一代HFPEF研究人员具有强大的数据科学工具箱，以推动未来的发现。