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AIVIS: Next Generation Vigilant Information Seeking Artificial Intelligence-based Clinical Decision Support for Sepsis

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R42AI177108-01
Agency Tracking Number: R42AI177108
Amount: $257,723.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: NIAID
Solicitation Number: PA22-178
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-07-07
Award End Date (Contract End Date): 2024-06-30
Small Business Information
San Diego, CA 92122
United States
DUNS: 118526455
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 (928) 812-0116
Business Contact
Phone: (650) 678-1941
Research Institution
LA JOLLA, CA 92093-0621
United States

 Nonprofit College or University

Sepsis, a heterogeneous syndrome characterized by whole-body inflammation caused by the body's
response to an infection, is the most expensive and deadly condition treated in hospitals, with over 270,000
cases of sepsis-related deaths in the U.S. alone. The cornerstones of optimal sepsis care are early
recognition accompanied by appropriate antimicrobial therapy, and use of evidence-based hemodynamic
therapies such as fluid resuscitation and vasoactive medications. While data-driven approaches based on
machine learning (ML) have shown promise in finding patterns in high-dimensional clinical data to forecast
sepsis among hospitalized patients, there are no clinically validated and FDA-approved clinical decision
support (CDS) system that can reliably identify patients at risk of developing sepsis. Moreover, existing
ML-based solutions are as good as the quality of the data presented to them, and the presence of outliers
and missingness can have deleterious effects on their performance. For instance, it has been suggested
that such systems are essentially looking over clinician's shoulders-using clinical behavior as the expression
of preexisting intuition and suspicion to generate a prediction. As such, there is a critical need for sepsis
prediction tools that can effectively use the routinely collected EHR data, assess prediction confidence, and
if needed, take necessary steps to gather additional information to reduce prediction uncertainty and
improve diagnostic accuracy without significant demand on the end-users.
This project aims to assess the clinical utility, safety, and efficacy of a novel uncertainty-aware sepsis
prediction system designed and developed in collaboration between UC San Diego Health and Healcisio
Inc., a UCSD start-up focused on scalable development and commercialization of advanced analytical
systems in critically care settings. The Healcisio system is explicitly designed to improve compliance with
the Centers for Medicaid and Medicare Services (CMS) care protocol for sepsis (the SEP1 bundle) and to
address the existing delays and variabilities in determining the sepsis onset time, so that life-saving
antibiotics and hemodynamic support can be delivered in a timely fashion. To maintain software quality
assurance a quality management system (QMS) will be developed to accompany a 510(k) FDA submission
package to demonstrate safety and effectiveness. To enhance hospital quality improvement (QI) teams’
ability to measure impact of earlier recognition and SEP-1 bundle compliance, a novel quality measure
(SEP1+) and a causal impact analysis tool is introduced. Ultimately, the novel technologies developed and
tested under this project will enhance our ability to use advanced analytics to predict adverse events,
assess patients’ response to therapy, and optimize and personalize care at the beside through a rapid-cycle
‘learning healthcare system’ framework.

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

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