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Solving Sepsis: Early Identification and Prompt Management Using Machine Learning

Award Number: R42GM144999
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 06/01/2022
PERIOD OF PERFORMANCE END DATE: 11/30/2024

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Solving Sepsis: Early Identification and Prompt Management Using Machine Learning - 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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $925,681 )
2024	2024	COHERE-MED, INC.	571 EUCLID AVE	BERKELEY	CA	94708	ALAMEDA	USA	Biomedical Research and Research Training	002	3	12/1/2023	NON-COMPETING CONTINUATION	$925,681
2024	2023	COHERE-MED, INC.	110 CORCORAN ST FL 5	DURHAM	NC	27701	DURHAM	USA	Biomedical Research and Research Training	001	2	10/30/2023	EXTENSION WITH OR WITHOUT FUNDS	$37,786
2024	2022	COHERE-MED, INC.	110 CORCORAN ST FL 5	DURHAM	NC	27701	DURHAM	USA	Biomedical Research and Research Training	000	1	10/30/2023	NEW	-$37,786
														Subtotal = $925,681

Issue Date FY: 2023 ( Subtotal = $912,755 )
2023	2023	COHERE-MED, INC.	1204 CABRILLO AVE	BURLINGAME	CA	94010	SAN MATEO	USA	Biomedical Research and Research Training	001	2	11/30/2022	EXTENSION WITH OR WITHOUT FUNDS	$912,755
2023	2022	COHERE-MED, INC.	1204 CABRILLO AVE	BURLINGAME	CA	94010	SAN MATEO	USA	Biomedical Research and Research Training	000	1	11/22/2022	NEW	$0
														Subtotal = $912,755

Issue Date FY: 2022 ( Subtotal = $275,766 )
2022	2022	COHERE-MED, INC.	1204 CABRILLO AVE	BURLINGAME	CA	94010	SAN MATEO	USA	Biomedical Research and Research Training	001	1	8/4/2022	NEW	$0
2022	2022	COHERE-MED, INC.	1204 CABRILLO AVE	BURLINGAME	CA	94010	SAN MATEO	USA	Biomedical Research and Research Training	000	1	5/16/2022	NEW	$275,766
														Subtotal = $275,766

Grand Total All Awards = $2,114,202

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Solving Sepsis: Early Identification and Prompt Management Using Machine Learning

Award Number: R42GM144999

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 06/01/2022

PERIOD OF PERFORMANCE END DATE: 11/30/2024

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