# Solving Sepsis: Early Identification and Prompt Management Using Machine Learning

> **NIH NIH R42** · COHERE-MED, INC. · 2024 · $925,681

## Abstract

Abstract
This fast-track STTR application proposes to enhance, validate, and scale Sepsis Watch, a deep
learning sepsis detection and management system built using data from the Emergency
Department (ED) Duke University Hospital (DUH). The proposal will extend and enhance
Sepsis Watch to EDs, general inpatient wards, and intensive care unit (ICU) settings across
multiple health systems in the United States. While early diagnosis and prompt treatment of
sepsis can improve mortality and morbidity, early detection has remained elusive. The Sepsis
Watch integration in the DUH ED improved compliance with the 3-hour sepsis bundle by 12%
and the 6-hour sepsis bundle by 18%. The system reduced mortality for severe sepsis by 15%
and mortality for septic shock by 22%. This proposal seeks to transform Sepsis Watch into a
scalable solution to replicate such results at other health systems and in settings beyond the ED.
In Phase I, we propose external validation through a retrospective analysis of data from two
separate health systems. Phase 1 will let us automate data quality checks and ingestion
processes at scale from different health systems as we curate data from at least 200,000
encounters over a 2-year period. We will present model predictions to clinicians from each
hospital to analyze potential impact of integrating Sepsis Watch into clinical care. In Phase II,
we propose conducting temporal validation at each hospital from Phase I. This will allow us to
design real-time ingestion of data records into Sepsis Watch in a manner that is agnostic to
electronic health record (EHR) vendor systems. We will optimize the machine learning model
using Phase 1 findings to improve performance at each location while assessing federated and
centralized learning approaches that incorporate data from different hospitals. Models
variations that utilize different sets of inputs will also be assessed and models will be built to
three gold-standard sepsis definitions, including Sepsis-3, CMS SEP-1 sepsis, and CDC Adult
Sepsis Event. During the 6-month temporal validation we will also generalize the Sepsis Watch
user-interface and workflow by seeking feedback from clinicians at each hospital as it is run in
silent mode. This will allow Sepsis Watch to be configurable to various clinical workflows.
The optimized model and user-interface in Phase 2 should allow Sepsis Watch to be seamlessly
integrated into routine clinical care in each hospital and then into other hospitals within each of
the two health systems and eventually to any health system in the US.

## Key facts

- **NIH application ID:** 10746843
- **Project number:** 5R42GM144999-03
- **Recipient organization:** COHERE-MED, INC.
- **Principal Investigator:** Manesh R Patel
- **Activity code:** R42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $925,681
- **Award type:** 5
- **Project period:** 2022-06-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10746843

## Citation

> US National Institutes of Health, RePORTER application 10746843, Solving Sepsis: Early Identification and Prompt Management Using Machine Learning (5R42GM144999-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10746843. Licensed CC0.

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