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 这个K23的科学目标是应用尖端的数据科学方法来识别新的亚表型 异质性脓毒症综合征此K23应用程序提出了一个为期5年的培训计划，以推动 博士Sivasubramanium Bhavani走向他的职业生涯作为一个独立的物理学家，科学家。Bhavani博士的职业生涯 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特征。 将电子健康记录（EHR）中的数据转化为重症患者的个性化治疗计划。Bhavani博士 我将通过完成3个短期目标来实现这个职业目标：1）获得无人监督机器方面的专业知识 2）获得深度学习神经网络的专业知识，3）获得临床信息学的专业知识 将模型应用于真实世界数据的原则。Bhavani博士概述了教学法的综合方案， 研讨会，会议，并与专家导师保持沟通，以提供必要的职业生涯 发展Bhavani博士的导师是重症监护协会前主席克雷格库珀史密斯博士 长期从事NIH资助的脓毒症研究的医学博士和著名的机器专家May Wang博士 学习此外，Bhavani博士的顾问是John Hanfelt博士，Annette Ehrman博士，Matthew Semler博士和Matthew 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。与 在K23的支持下，Bhavani博士将为败血症的精准医学方法的发展做出贡献。 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， 每年在美国造成超过27万人死亡几十年的临床试验都未能找到 持续有益于脓毒症患者。一刀切的治疗方法并没有奏效， 需要鉴定可能对治疗有不同反应的脓毒症亚表型。迄今为止，大多数研究 已经使用实验室和生命体征的静态测量确定了脓毒症的亚表型。然而，败血症是一种 动态过程，生物和生理反应在几分钟到几小时内演变。的目标 该建议是使用动态数据，特别是纵向生命体征来鉴定新的脓毒症亚表型。 在Aim 1中，Bhavani博士将把尖端的机器学习算法应用于纵向生命体征， 并验证新的生命轨迹亚表型。在目标2中，Bhavani博士将研究 这些subphenotypes。在目标3中，Bhavani博士将研究亚表型对一种最常见的 败血症的常见干预措施-静脉输液。鉴定对不同药物应答的亚表型 液体可以将脓毒症管理从一刀切的方法转变为精确的医学方法- 脓毒症亚表型的最终目的。通过K23的培训，Bhavani博士将为以下方面做好准备： R 01水平的工作：a）通过结合动态临床和免疫学数据来细化亚表型，和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。