R33TW012211
Project Grant
Overview
Grant Description
Development of a Mobile Health Personalized Physiologic Analytics Tool for Pediatric Patients with Sepsis - Project Summary
Sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, encompasses a continuum that ranges from sepsis to severe sepsis, septic shock, multiple organ dysfunction syndrome (MODS), and eventually death if untreated.
Sepsis is the leading cause of child mortality worldwide, with most of these deaths occurring in low and middle-income countries (LMICs). Yet few clinical tools have been developed for identifying, monitoring, or managing septic children in LMICs.
There is immense potential for novel clinical tools that can help clinicians more rapidly identify children with advanced stages of sepsis (severe sepsis, septic shock, and MODS), who are at highest risk for decompensation and death.
Mobile health (mHealth) tools, wearable devices, and artificial intelligence techniques have rapidly proliferated for a multitude of medical applications and could serve to bridge the gap in care of critically ill patients in LMIC settings.
By enabling the detection of subtle physiologic changes indicating clinical deterioration, these tools may allow clinicians to intervene earlier, better direct care, and allocate scarce resources, all without the need for advanced laboratory diagnostics or critical care infrastructure.
Furthermore, remote monitoring capabilities may also prove highly valuable in improving patient care and protecting the safety of healthcare workers during times of infectious disease outbreaks such as from novel coronavirus 2019 (COVID-19).
This proposed research will develop a context-appropriate mHealth tool linking continuous physiologic data obtained from a wearable device with a novel machine learning approach known as Personalized Physiologic Analytics (PPA) run on a standard smartphone to provide clinicians with accurate assessments of sepsis severity and mortality risk in septic children admitted to the Dhaka Hospital of the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B).
Formative research among clinicians at ICDDR,B will be used to develop this mHealth tool incorporating the PPA algorithm with a clinical decision support and alert system for use by front-line clinicians.
Finally, the tool's feasibility, usability, and accuracy for detection of sepsis severity and MODS will be validated in a new population of pediatric patients with sepsis.
Knowledge gained from this study will greatly advance the evidence base for the use of mHealth tools and artificial intelligence techniques to help clinicians worldwide better care for critically ill children in LMIC settings earlier in the course of their disease, thereby reducing morbidity and mortality from sepsis.
The results of this investigational research will be used to inform a multi-center clinical trial which would seek to assess the impact of using this mHealth tool on clinical outcomes as well as the cost-effectiveness of this tool.
This tool may also provide an effective means of assessing patient responses to various therapeutic interventions via continuous physiologic monitoring in future clinical trials.
The proposed initiatives will also build a base of technical and professional expertise at ICDDR,B in mHealth research capacity and user-centered design.
Sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, encompasses a continuum that ranges from sepsis to severe sepsis, septic shock, multiple organ dysfunction syndrome (MODS), and eventually death if untreated.
Sepsis is the leading cause of child mortality worldwide, with most of these deaths occurring in low and middle-income countries (LMICs). Yet few clinical tools have been developed for identifying, monitoring, or managing septic children in LMICs.
There is immense potential for novel clinical tools that can help clinicians more rapidly identify children with advanced stages of sepsis (severe sepsis, septic shock, and MODS), who are at highest risk for decompensation and death.
Mobile health (mHealth) tools, wearable devices, and artificial intelligence techniques have rapidly proliferated for a multitude of medical applications and could serve to bridge the gap in care of critically ill patients in LMIC settings.
By enabling the detection of subtle physiologic changes indicating clinical deterioration, these tools may allow clinicians to intervene earlier, better direct care, and allocate scarce resources, all without the need for advanced laboratory diagnostics or critical care infrastructure.
Furthermore, remote monitoring capabilities may also prove highly valuable in improving patient care and protecting the safety of healthcare workers during times of infectious disease outbreaks such as from novel coronavirus 2019 (COVID-19).
This proposed research will develop a context-appropriate mHealth tool linking continuous physiologic data obtained from a wearable device with a novel machine learning approach known as Personalized Physiologic Analytics (PPA) run on a standard smartphone to provide clinicians with accurate assessments of sepsis severity and mortality risk in septic children admitted to the Dhaka Hospital of the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B).
Formative research among clinicians at ICDDR,B will be used to develop this mHealth tool incorporating the PPA algorithm with a clinical decision support and alert system for use by front-line clinicians.
Finally, the tool's feasibility, usability, and accuracy for detection of sepsis severity and MODS will be validated in a new population of pediatric patients with sepsis.
Knowledge gained from this study will greatly advance the evidence base for the use of mHealth tools and artificial intelligence techniques to help clinicians worldwide better care for critically ill children in LMIC settings earlier in the course of their disease, thereby reducing morbidity and mortality from sepsis.
The results of this investigational research will be used to inform a multi-center clinical trial which would seek to assess the impact of using this mHealth tool on clinical outcomes as well as the cost-effectiveness of this tool.
This tool may also provide an effective means of assessing patient responses to various therapeutic interventions via continuous physiologic monitoring in future clinical trials.
The proposed initiatives will also build a base of technical and professional expertise at ICDDR,B in mHealth research capacity and user-centered design.
Awardee
Funding Goals
THE JOHN E. FOGARTY INTERNATIONAL CENTER (FIC) SUPPORTS RESEARCH AND RESEARCH TRAINING TO REDUCE DISPARITIES IN GLOBAL HEALTH AND TO FOSTER PARTNERSHIPS BETWEEN U.S. SCIENTISTS AND THEIR COUNTERPARTS ABROAD. FIC SUPPORTS BASIC BIOLOGICAL, BEHAVIORAL, AND SOCIAL SCIENCE RESEARCH, AS WELL AS RELATED RESEARCH TRAINING AND CAREER DEVELOPMENT. THE RESEARCH PORTFOLIO IS DIVIDED INTO SEVERAL PROGRAMS THAT SUPPORT A WIDE VARIETY OF FUNDING MECHANISMS TO MEET PROGRAMMATIC OBJECTIVES.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Providence,
Rhode Island
029034923
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 349% from $247,128 to $1,109,787.
Rhode Island Hospital was awarded
Pediatric Sepsis Analytics Tool for LMICs
Project Grant R33TW012211
worth $1,109,787
from Fogarty International Center in August 2021 with work to be completed primarily in Providence Rhode Island United States.
The grant
has a duration of 4 years 8 months and
was awarded through assistance program 93.989 International Research and Research Training.
The Project Grant was awarded through grant opportunity Mobile Health: Technology and Outcomes in Low and Middle Income Countries (R21/R33 - Clinical Trial Optional).
Status
(Ongoing)
Last Modified 5/5/25
Period of Performance
8/10/21
Start Date
4/30/26
End Date
Funding Split
$1.1M
Federal Obligation
$0.0
Non-Federal Obligation
$1.1M
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for R33TW012211
Transaction History
Modifications to R33TW012211
Additional Detail
Award ID FAIN
R33TW012211
SAI Number
R33TW012211-1433125122
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Nonprofit With 501(c)(3) IRS Status (Other Than An Institution Of Higher Education)
Awarding Office
75NF00 NIH Fogarty International Center
Funding Office
75NF00 NIH Fogarty International Center
Awardee UEI
N876TLXYGCG4
Awardee CAGE
1HTV4
Performance District
RI-02
Senators
Sheldon Whitehouse
John Reed
John Reed
Budget Funding
Federal Account | Budget Subfunction | Object Class | Total | Percentage |
---|---|---|---|---|
John E. Fogarty International Center, National Institutes of Health, Health and Human Services (075-0819) | Health research and training | Grants, subsidies, and contributions (41.0) | $410,964 | 100% |
Modified: 5/5/25