Development of a Mobile Health Personalized Physiologic Analytics Tool for Pediatric Patients with Sepsis

为脓毒症儿科患者开发移动健康个性化生理分析工具

基本信息

批准号：
10472047
负责人：
Adam Carl Levine
金额：
$ 15.42万
依托单位：
RHODE ISLAND HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-10 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10472047
关键词：
2019-nCoV Algorithms Artificial Intelligence Bangladesh COVID-19 Caring Cellular Phone Cessation of life Child Child Mortality Childhood Clinical Clinical Trials Communicable Diseases Critical Care Critical Illness Critically ill children Data Detection Deterioration Development Devices Diagnostic Diarrhea Disease Disease Outbreaks Enrollment Equipment Functional disorder Future Health Personnel Hospitals Immune response Industry Infection Infrastructure Interdisciplinary Study International Investigation Knowledge Laboratories Life Link Machine Learning Malaria Malnutrition Medical Modeling Monitor Morbidity - disease rate Multi-Institutional Clinical Trial Multiple Organ Failure Organ Outcome Patient Care Patient Monitoring Patients Phase Physiologic Monitoring Physiological Pneumonia Population Proliferating Research Research Personnel Resources Running Safety Scientist Sepsis Septic Shock Severities Stratification System Techniques Technology Therapeutic Intervention Time Training Ursidae Family Variant acute infection analytical tool base clinical care clinical decision support co-infection cost effectiveness data format diarrheal disease evidence base high risk implementation framework improved individual patient international center low and middle-income countries mHealth mortality mortality risk novel novel coronavirus patient population patient response pediatric patients pediatric sepsis prevent prospective remote monitoring septic tool treatment response usability user centered design user-friendly wearable device wearable sensor technology

项目摘要

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.

项目概要脓毒症，定义为因宿主对感染反应失调而引起的危及生命的器官功能障碍，涵盖从败血症到严重败血症、败血性休克、多器官功能障碍的连续体脓毒症是全世界儿童死亡的主要原因。大多数死亡发生在低收入和中等收入国家 (LMIC)，但很少有临床工具能够已开发用于识别、监测或管理中低收入国家的败血症儿童，具有巨大的潜力。寻找新的临床工具，可以帮助更快地识别处于晚期阶段的儿童败血症（严重败血症、败血性休克和 MODS），其失代偿和死亡的风险最高。移动医疗 (mHealth) 工具、可穿戴设备和人工智能技术迅速普及适用于多种医疗应用，可以缩小在危重病人护理方面的差距通过检测表明临床恶化的细微生理变化，这些工具可以让新来者更早地干预、更好地直接护理并分配稀缺资源，而所有这些都不需要此外，还需要先进的实验室诊断或重症监护基础设施。能力在改善患者护理和保护医疗安全方面也可能非常有价值传染病爆发期间的工作人员，例如 2019 年新型冠状病毒 (COVID-19)。这项拟议的研究将开发一种适合环境的移动医疗工具，将连续的生理学联系起来使用称为个性化的新颖机器学习方法从可穿戴设备获取数据生理分析 (PPA) 在标准智能手机上运行，以提供准确的评估达卡国际医院收治的脓毒症儿童的脓毒症严重程度和死亡风险孟加拉国腹泻病研究中心 (icddr,b)。icddr,b 的赞助者的形成性研究。将用于开发这种移动医疗工具，将 PPA 算法与临床决策支持相结合，最后，该工具的可行性、可用性和检测准确性。脓毒症严重程度和 MODS 将在新的脓毒症儿科患者群体中进行验证。从这项研究中获得的知识将极大地推进移动医疗工具和人工智能技术帮助世界各地的圣人更好地照顾中低收入国家的危重儿童在病程早期，降低败血症的发病率和死亡率。这项研究将用于为一项多中心临床试验提供信息，该试验旨在评估使用该移动医疗工具对临床结果的影响以及该工具的成本效益。还可以提供一种有效的方法来评估患者对各种治疗干预措施的反应拟议的举措也将为未来临床试验中的持续生理监测奠定基础。 icddr,b 在移动医疗研究能力和以用户为中心的设计方面拥有技术和专业知识。