# Implementation of Continuum of Care Sepsis Phenotyping and Risk Stratification

> **NIH NIH K23** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $179,541

## Abstract

PROJECT SUMMARY/ABSTRACT
This proposal outlines a 5-year research and career development plan for Dr. Gabriel Wardi, an emergency
medicine intensivist and assistant professor at UCSD. The major objective of his research is the effective
implementation of deep-learning algorithms to clinical practice to improve care of sepsis patients. This K23
proposal outlines and provides support for his career development plan, specifically focusing on (1) the ability
to design meaningful sepsis studies and necessary statistical training, (2) strong understanding of machine-
learning approaches, and (3) a focus on implementation science to improve care of sepsis patients with novel
deep-learning algorithms. Dr. Wardi has assembled a diverse team of collaborative experts to support his
career development and mentor him consisting of Dr. Atul Malhotra, an internationally recognized expert in
critical care physiology and respiratory failure along with Dr. Shamim Nemati, a machine-learning expert with a
strong focus in prediction of sepsis in real-time. Additionally, his training team includes experts in
implementation science from the Dissemination and Implementation Science Center (DISC) at UCSD as well
as an expert in clinical trial design and biostatistics (Dr. Sonia Jain). Despite decades of research, sepsis
remains a major public health challenge. Current approaches to sepsis care emphasize “one-size fits all”
bundles that may result in patient harm in certain subgroups. Newer approaches to data analysis, using
multiple layers of non-linear arithmetic operations now allow for clustering of sepsis patients into novel clinical
phenotypes that may provide for more personalized care. The PI will evaluate potential phenotypes of sepsis
not present on admission (NPOA) in Aim 1. Prior investigations into phenotyping have been developed and
validated in patients present in the emergency department. Patients with sepsis NPOA have high mortality and
better quantification of phenotypes may help improve care by identifying novel groups. Dr. Wardi seeks to
evaluate 2 inter-related hypotheses in this aim: one is that phenotypes may represent disease trajectories that
are modifiable by accepted therapies (e.g. time to, and quantity of fluid resuscitation). The second is that novel
phenotypes exist in the inpatient setting. In his second aim, Dr. Wardi seeks to determine clinical mechanisms
of 30-day readmissions in sepsis patients through a variety of approaches, including identification of novel
clusters of sepsis patients at discharge and use of natural language processing of a large data set to identify
actionable reasons for readmissions. Finally, he seeks to determine if the application of a wearable patch to
sepsis patients discharged to a long-term acute care hospital when combined with a machine-learning
algorithm may reduce unanticipated 30-day sepsis readmissions. This research and career development plan
affords Dr. Wardi an impressive foundation to develop into...

## Key facts

- **NIH application ID:** 10833136
- **Project number:** 5K23GM146092-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Gabriel Wardi
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $179,541
- **Award type:** 5
- **Project period:** 2022-05-01 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10833136

## Citation

> US National Institutes of Health, RePORTER application 10833136, Implementation of Continuum of Care Sepsis Phenotyping and Risk Stratification (5K23GM146092-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10833136. Licensed CC0.

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