Identification of Precision Sepsis Subphenotypes Using Vital Sign Trajectories

使用生命体征轨迹精确识别脓毒症亚表型

• 批准号: 10350208
• 负责人: Sivasubramanium Bhavani
• 金额: $ 17.79万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10350208
• 关键词: Algorithms; Anti-Inflammatory Agents; Bioinformatics; Biological; Biological Markers; Cardiovascular system; Cessation of life; Clinical; Clinical Informatics; Clinical Trials; Communication; Computer Assisted; Critical Care; Critical Illness; Data; Data Science; Data Set; Development; Diagnostic; Early identification; Electronic Health Record; Funding; Future; Goals; Healthcare Systems; Heart Rate; Heterogeneity; Hospital Costs; Hospitalization; Hour; IV Fluid; Immune; Immune response; Immunologic Markers; Immunologics; Immunomodulators; Immunophenotyping; Infection; Inpatients; Intervention; Kidney; Life; Liquid substance; Machine Learning; Maps; Measurement; Measures; Medicine; Mentors; Methods; Modeling; Normal saline; Outcome; Patients; Physicians; Physiological; Physiology; Plasma; Process; Publishing; Research; Resuscitation; Scientist; Sepsis; Sepsis Syndrome; Societies; Subgroup; Syndrome; Temperature; Training; Training Programs; United States National Institutes of Health; Validation; Work; autoencoder; base; biobank; career; career development; clinically relevant; cohort; comparative; crystalloid; cytokine; deep learning; deep neural network; innovation; machine learning algorithm; mortality; multiplex assay; novel; personalized medicine; precision medicine; predicting response; programs; response; septic patients; symposium; tool; treatment planning; treatment response; unsupervised learning

Project Abstract The scientific goal of this K23 is to apply cutting-edge data science approaches to identify novel subphenotypes within the heterogeneous sepsis syndrome. This K23 application proposes a 5-year training program to propel Dr. Sivasubramanium Bhavani towards his career as an independent physician-scientist. Dr. Bhavani’s career goal is to be an expert in developing computer-aided diagnostic tools to map the extensive clinical and biological data in the electronic health record (EHR) to personalized treatment plans for critically ill patients. Dr. Bhavani will accomplish this career goal by completing 3 short-term goals: 1) Gain expertise in unsupervised machine learning, 2) Gain expertise in deep learning neural networks, and 3) Gain expertise in clinical informatics principles for model application to real-world data. Dr. Bhavani has outlined an integrated program of didactics, seminars, conferences, and consistent communication with expert mentors to provide the necessary career development. Dr. Bhavani’s mentors are Dr. Craig Coopersmith, a past president of the Society of Critical Care Medicine with a long career of NIH-funded sepsis research, and Dr. May Wang, a renowned expert in machine learning. In addition, Dr. Bhavani’s advisors are Drs. John Hanfelt, Annette Esper, Matthew Semler and Matthew Churpek, with collective expertise in longitudinal clustering, sepsis biomarkers, and bioinformatics. With the support of the K23, Dr. Bhavani will contribute to the development of precision medicine approaches to sepsis. Sepsis is a severe and heterogeneous syndrome characterized by a dysregulated host response to infection that results in over 270,000 deaths in the U.S. annually. Decades of clinical trials have failed to identify therapies that consistently benefit patients with sepsis. The one-size-fits-all treatment approach has not worked, and there is a need to identify sepsis subphenotypes that may have different responses to treatment. To date, most studies have identified sepsis subphenotypes using static measurements of labs and vital signs. However, sepsis is a dynamic process with biological and physiological responses that evolve over minutes to hours. The objective of this proposal is to identify novel sepsis subphenotypes using dynamic data, specifically longitudinal vital signs. In Aim 1, Dr. Bhavani will apply cutting-edge machine learning algorithms to longitudinal vital signs to develop and validate novel vitals trajectory subphenotypes. In Aim 2, Dr. Bhavani will investigate the immune signatures of these subphenotypes. In Aim 3, Dr. Bhavani will study the responses of the subphenotypes to one of the most common interventions in sepsis – intravenous fluids. Identification of subphenotypes with responses to different fluids could shift sepsis management from a one-size-fits-all approach to a precision medicine approach – the ultimate objective of sepsis subphenotypes. Through the training in this K23, Dr. Bhavani will be prepared for R01-level work in a) refining subphenotypes by combining dynamic clinical and immunological data and b) studying the responses of subphenotypes to additional treatments by using data from other RCTs.

项目摘要 K23 的科学目标是应用尖端数据科学方法来识别新的亚表型 该 K23 应用提出了一个为期 5 年的培训计划来推动。 Sivasubramanium Bhavani 博士作为一名独立医师科学家的职业生涯。 目标是成为开发计算机辅助诊断工具的专家，以绘制广泛的临床和生物学图谱 Bhavani 博士将电子健康记录 (EHR) 中的数据用于为危重患者制定个性化治疗计划。 将通过完成 3 个短期目标来实现这一职业目标：1) 获得无人监督机器方面的专业知识 学习，2) 获得深度学习神经网络方面的专业知识，以及 3) 获得临床信息学方面的专业知识 Bhavani 博士概述了一个综合教学计划， 研讨会、会议以及与专家导师的持续沟通，以提供必要的职业生涯 Bhavani 博士的导师是重症监护协会前任主席 Craig Coopersmith 博士。 长期从事 NIH 资助的脓毒症研究的医学专家，著名机器专家 May Wang 博士 此外，Bhavani 博士的顾问是 John Hanfelt、Annette Esper、Matthew Semler 和 Matthew 博士。 Churpek 在纵向聚类、脓毒症生物标志物和生物信息学方面拥有集体专业知识。 在 K23 的支持下，Bhavani 博士将为脓毒症精准医学方法的开发做出贡献。 脓毒症是一种严重的异质性综合征，其特征是宿主对感染的反应失调， 每年导致美国超过 270,000 人死亡，数十年的临床试验未能找到有效的治疗方法。 始终使脓毒症患者受益的一刀切治疗方法尚未奏效，并且有一种方法。 迄今为止，大多数研究都需要确定可能对治疗有不同反应的脓毒症亚表型。 已经通过实验室和生命体征的静态测量确定了脓毒症亚表型。 生物和生理反应的动态过程在几分钟到几小时内演变。 该提案旨在使用动态数据，特别是纵向生命体征来识别新的脓毒症亚表型。 在目标 1 中，Bhavani 博士将尖端的机器学习算法应用于纵向生命体征，以开发 并验证新的生命体征轨迹亚表型 在目标 2 中，Bhavani 博士将研究免疫特征。 在目标 3 中，Bhavani 博士将研究这些亚表型对其中一种最显着的反应。 脓毒症的常见干预措施——静脉输液识别亚表型及其对不同反应的反应。 液体可以将脓毒症管理从一刀切的方法转变为精准医学方法—— 通过 K23 的培训，Bhavani 博士将为脓毒症亚表型的最终目标做好准备。 R01 级工作 a) 通过结合动态临床和免疫学数据来细化亚表型，b) 使用其他随机对照试验的数据研究亚表型对额外治疗的反应。