# Characterizing patients at risk for sepsis through Big Data

> **NIH NIH K23** · EMORY UNIVERSITY · 2023 · $158,530

## Abstract

Characterizing Patients at Risk for Sepsis Through Big Data
SUMMARY
The goal of this KL2 research proposal is create an extension of an existing data-driven sepsis algorithm, the
artificial intelligence sepsis expert (AISE), by forecasting the type and sequence of sepsis-specific organ failure
using clinical data from the electronic medical record, then identifying the incremental benefit received by
adding high-resolution data derived from cardiovascular waveforms (arterial waveform and
electrocardiographic waves), and small molecule metabolite data over time to provide some mechanistic
context. The most important data (features) used in real-time from AISE will be used as inputs for a fuzzy k-
means clustering algorithm (“Saving Organs from Sepsis”, or SOS) using retrospectively-collected data,
designed to better characterize patients at risk for sepsis by their organ failure. I have selected three organs in
particular: shock, acute respiratory failure, and acute kidney injury (AKI). Principal component analyses (PCA)
data from 2,375 ICU patients with sepsis will be projected onto a novel visual representation for patient
phenotyping based on risk of (trajectory toward) different types of organ failure. I will identify if this SOS
algorithm can accurately forecast new organ failure within 12 hours based on SOS.
To better understand the impact of specific features on organ failure, I will test the ability of each high-
resolution features, and metabolomics data to forecast septic shock. Among those who develop septic shock, I
will measure all nine high-resolution features from the beginning of the ICU stay up to shock onset and
compare those changes to those who develop sepsis but not septic shock, and those who do not develop
sepsis. I will then see if the collective addition of high-resolution data improves performance of septic shock
forecasting. Finally, I will conduct a prospective observational study to collect metabolomic information in a 60-
patient study to identify the incremental improvement of adding metabolomics data to SOS for predicting septic
shock, over SOS with just EMR and waveform data. The results of this work will provide preliminary data for
further career development and NIH-funding. The long-term goal would be to build a model that optimizes the
timing of appropriate therapy, thus decreasing the incidence of sepsis and associated organ failure.
As a K23 candidate, I will use this award to acquire formal didactic training and more hands-on experience in
machine learning, signal processing, metabolomics analysis. I will seek focused training that will complement
my experience as a clinical trialist so that I can design high-quality studies to contribute to Big Data analytics in
critical care research and practice. My overarching career goal is to become a leader in the application of Big
Data analysis of critically ill patients to predict progression of disease, specifically sepsis. The Emory
environment is an ideal place ...

## Key facts

- **NIH application ID:** 10668998
- **Project number:** 5K23GM137182-04
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Andre L Holder
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $158,530
- **Award type:** 5
- **Project period:** 2020-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10668998

## Citation

> US National Institutes of Health, RePORTER application 10668998, Characterizing patients at risk for sepsis through Big Data (5K23GM137182-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10668998. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*