Identification of Precision Sepsis Subphenotypes Using Vital Sign Trajectories

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.

Grant Number: 5K23GM144867-05
NIH Institute/Center: NIH
Principal Investigator: Sivasubramanium Bhavani