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.
20%
of all global deaths can be attributed to sepsis (WHO)
50%
of all sepsis cases worldwide occurred in children under 5 years of age (WHO)
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 (5R33TW012211-04) 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.
