Identification of Precision Sepsis Subphenotypes Using Vital Sign Trajectories

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型 异质性脓毒症综合征。这份K23申请提出了一项为期5年的培训计划，以推动 西瓦苏布拉曼尼姆·巴瓦尼博士作为一名独立的内科科学家的职业生涯。巴瓦尼博士的职业生涯 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图 将电子健康记录(EHR)中的数据转换为危重患者的个性化治疗计划。巴瓦尼博士 我将通过完成3个短期目标来实现这一职业目标：1)获得无人监督机器的专业知识 学习，2)获得深度学习神经网络的专业知识，以及3)获得临床信息学的专业知识 模型应用于真实世界数据的原则。巴瓦尼博士概述了一套完整的教学计划， 研讨会、会议和与专家导师的持续沟通，以提供必要的职业生涯 发展。巴瓦尼医生的导师是克雷格·库珀史密斯医生，他曾任重症监护学会主席 NIH资助的败血症研究的长期职业生涯的医学，以及著名的机械专家May Wang博士 学习。此外，巴瓦尼博士的顾问是约翰·汉费尔特博士、安妮特·埃斯珀博士、马修·塞姆勒博士和马修·马修博士 Churpek在纵向聚集、败血症生物标志物和生物信息学方面拥有共同的专业知识。与 在K23的支持下，Bhavani博士将为开发治疗脓毒症的精确医学方法做出贡献。 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确定治疗方法 持续受益于败血症患者。一刀切的治疗方法没有奏效，而且有一个 需要确定可能对治疗有不同反应的败血症亚型。到目前为止，大多数研究 已经通过实验室和生命体征的静态测量确定了败血症的亚型。然而，败血症是一种 生物和生理反应在几分钟到几小时内演变的动态过程。的目标是 这项建议是利用动态数据，特别是纵向生命体征来识别新的脓毒症亚型。 在目标1中，巴瓦尼博士将把尖端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型。在《目标2》中，巴瓦尼博士将研究免疫信号 在这些亚型中。在目标3中，Bhavani博士将研究亚型对最多的 脓毒症的常见干预措施-静脉输液。对不同品种不同反应的亚型鉴定 液体可能会将脓毒症的管理从一刀切的方法转变为精准的医学方法- 败血症亚型的最终目标。通过这款K23的培训，Bhavani博士将为 R01级工作：a)结合动态临床和免疫学数据提炼亚型；b) 通过使用来自其他随机对照试验的数据来研究亚型对额外处理的反应。

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.