You are here

Solving Sepsis: Early Identification and Prompt Management Using Machine Learning

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 4R42GM144999-02
Agency Tracking Number: R42GM144999
Amount: $912,755.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: 300
Solicitation Number: PA20-265
Timeline
Solicitation Year: 2020
Award Year: 2023
Award Start Date (Proposal Award Date): 2022-12-01
Award End Date (Contract End Date): 2024-11-30
Small Business Information
1204 CABRILLO AVE
Burlingame, CA 94010-4805
United States
DUNS: 117750052
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 MANESH PATEL
 (919) 681-4441
 manesh.patel@duke.edu
Business Contact
 SRIKANTH MUTHYA
Phone: (919) 668-8917
Email: srikanth.muthya@cohere-med.com
Research Institution
 DUKE UNIVERSITY
 
2200 W MAIN ST, SUITE 1000
DURHAM, NC 27705-4673
United States

 Nonprofit College or University
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.

* Information listed above is at the time of submission. *

US Flag An Official Website of the United States Government