This $305,000 project grant awarded by the National Institute of General Medical Sciences (NIGMS), under the Biomedical Research and Research Training program (CFDA 93.859), supports the development of a clinical decision support system (CDSS) called Sepsis FLO. The CDSS is designed to precisely guide blood volume diagnosis and treatment for sepsis patients. Key activities include: 1) Developing an alpha prototype of the CDSS through user-centered design; 2) Validating three functional...
This Project Grant award of $455,000 from the National Institute of General Medical Sciences (NIGMS) Biomedical Research and Research Training Program (CFDA 93.859) aims to decrease sepsis mortality in the pediatric population. The key products and services to be delivered include: Developing an AI-guided early sepsis identification tool to enable timely diagnosis and treatment. Performing sepsis phenotyping and creating digital twin patient models to predict personalized treatment responses....
The University of California, San Diego (UCSD) received a $312,974 Project Grant award from the National Institute of General Medical Sciences (NIGMS) under the Biomedical Research and Research Training program (CFDA 93.859) to develop accurate, efficient, and interpretable multi-modal foundation models for the early detection of sepsis. Over the 5-year project period from February 1, 2025 to January 31, 2030, UCSD will focus on several key objectives: curating large-scale electronic health...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award (CFDA 47.070) provides $1,200,000 to the University of California, San Diego (UCSD) to develop advanced foundation models and algorithms for early detection of sepsis using clinical time series data. The 4-year project aims to create innovative technologies, including a new transformer-based model and optimization techniques, to accurately and efficiently process complex clinical...
This $294,244 Project Grant award, funded by the Department of Health and Human Services (DHHS) Agency for Healthcare Research and Quality (AHRQ) under the Research on Healthcare Costs, Quality and Outcomes (CFDA 93.226) program, supports research to identify sepsis phenotypes associated with antibiotic-resistant pathogens. The project aims to leverage machine learning techniques, including large language models applied to clinical notes, to augment electronic health record (EHR) data and define...
This Project Grant award of $412,250 from the National Heart, Lung, and Blood Institute (NHLBI) under the Cardiovascular Diseases Research program (CFDA 93.837) will fund the development of a continuous, real-time, automated smart catheter analyzer to enable early identification of sepsis through urine biomarkers. The primary goal is to create a device that can quickly diagnose sepsis, a life-threatening condition, without requiring invasive blood tests. The 2-year R61 (Phase I) project will...
The National Institute of Allergy and Infectious Diseases (NIAID) awarded a $473,937 Project Grant under the Allergy and Infectious Diseases Research Program (CFDA 93.855) to The General Hospital Corporation, doing business as Massachusetts General Hospital (MGH), to develop a novel point-of-care liquid biopsy for early detection of sepsis-associated organ damage. The project aims to optimize a microfluidic enrichment and digital PCR assay to detect tissue-derived circulating rare cells...
This $700,000 Project Grant award from the National Institute of General Medical Sciences (NIGMS) Biomedical Research and Research Training Program (CFDA 93.859) supports the clinical validation of Prenosis, Inc.'s IMMUNIX platform, a precision medicine endotyping solution for sepsis. The project aims to: 1) validate the IMMUNOSCAN multiplex rapid test and mapping functions to Luminex immunoassays; 2) validate a parsimonious model for point-of-care endotyping; and 3) demonstrate the ability to...
This Project Grant award of $186,221 from the National Institute of Allergy and Infectious Diseases (CFDA 93.855 - Allergy and Infectious Diseases Research) supports research to develop personalized sepsis therapeutics for pediatric patients. The key objectives are to: 1) Augment sepsis phenotypes using high-frequency physiologic data to identify additional subtypes and predict disease progression; 2) Retrospectively identify signs of bloodstream infection prior to traditional diagnostic signs...
This $181,772 project grant from the National Heart, Lung and Blood Institute will support research into coagulopathy in sepsis patients. The grantee, Beth Israel Deaconess Medical Center, will conduct an ancillary study using viscoelastic monitoring to examine the effects of randomly assigned fluid resuscitation strategies on coagulation parameters in sepsis patients. Specifically, the study aims to: 1) analyze how liberal versus restrictive fluid administration impacts coagulation measurements...
AIVIS: NEXT GENERATION VIGILANT INFORMATION SEEKING ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT FOR SEPSIS - ABSTRACT 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.