Sepsis Subphenotypes: Development, Validation, and Implementation of a Precision Medicine Approach (CE Session)
Includes a Live Web Event on 09/25/2025 at 11:10 AM (PDT)
Description:
This session is part of Sepsis Alliance Summit 2025.
Sepsis is a highly heterogeneous condition, often eluding a one-size-fits-all treatment approach. This session focuses on the cutting-edge development of AI algorithms to uncover unique sepsis subphenotypes, enabling a precision medicine strategy that precisely matches treatments to individual patient profiles. The presenter will explore the methods used to develop and validate these algorithms, discuss the key challenges of implementing them in real-world clinical settings, and examine how clinical trials can be leveraged to evaluate their impact on patient outcomes. Attendees will gain a comprehensive understanding of the journey from algorithm development to bedside application and the pivotal role of AI in advancing personalized sepsis management.
Learning Objectives:
At the end of this session, the learner should be able to
- Evaluate the methodological approaches used in developing and validating AI algorithms for sepsis subphenotyping;
- Analyze the key challenges and barriers to implementing AI algorithms for sepsis management in real-world clinical settings;
- Apply appropriate clinical trial designs for assessing the effectiveness of AI algorithms in personalized sepsis management.
Target Audience:
Nurses, advanced practice providers, physicians, emergency responders, pharmacists, medical technologists, respiratory therapists, physical/occupational therapists, infection prevention specialists, data/quality specialists, and more.
Sivasubramanium (Siva) Bhavani, MD, MS
Assistant Professor of Medicine
Emory School of Medicine
Siva Bhavani, MD, MS, is a physician-scientist and an Assistant Professor of Medicine at Emory University in the Division of Pulmonary and Critical Care Medicine. He completed his internal medicine residency at Baylor College of Medicine, and Pulmonary and Critical Care fellowship at University of Chicago, where he completed a T32 research fellowship and a Master of Science in Public Health. Dr. Bhavani’s research centers on identifying treatment-responsive sepsis subphenotypes by applying machine learning algorithms to routine bedside vital signs that are available even in low-resource settings.
Dr. Bhavani’s work has received support from the NIH, NSF, ATS, and Kaiser Permanente. Through his NIH career development award, Dr. Bhavani developed the vitals trajectory algorithm, one of the first precision medicine tools for fluid resuscitation in patients with sepsis. The algorithm has been implemented as a clinical decision support tool in the electronic medical record across the Emory Healthcare system as part of the PRECISE trial. Dr. Bhavani’s vision is to translate the machine learning algorithms developed in his lab from paper to practice, and to evaluate the implementation-effectiveness of these algorithms in guiding precision treatment and improving the outcomes of critically ill patients.