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

• 批准号: 10880477
• 负责人: Stephanie Chow Garbern
• 金额: $ 24.71万
• 依托单位: RHODE ISLAND HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-10 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10880477
• 关键词: 2019-nCoV; Admission activity; 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; Early Diagnosis; Enrollment; Equipment; Functional disorder; Future; Health Personnel; Health protection; Hospitals; Immune response; Industry; Infection; Infrastructure; Interdisciplinary Study; International; Knowledge; Laboratories; Life; Link; Machine Learning; Malaria; Malnutrition; Medical; Modeling; Monitor; Morbidity - disease rate; Multi-Institutional Clinical Trial; Multiple Organ Failure; Organ; Outcome; Patient Admission; 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; Variant; acute infection; analytical tool; base; clinical care; clinical decision support; co-infection; cost effectiveness; diarrheal disease; evidence base; health training; 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; skills; tool; treatment response; usability; user centered design; user-friendly; wearable device; wearable sensor technology

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.

项目摘要 脓毒症，定义为由于宿主对感染的反应失调而导致的危及生命的器官功能障碍， 包括从脓毒症到严重脓毒症、感染性休克、多器官功能障碍的连续体 综合征(MODS)，如果不治疗，最终会死亡。脓毒症是全球儿童死亡的主要原因， 由于这些死亡大多发生在低收入和中等收入国家(LMIC)，但很少有临床工具 已开发用于识别、监测或管理LMICs中的败血症儿童。有巨大的潜力 寻找新的临床工具，帮助临床医生更快地识别患有晚期癌症的儿童 败血症(严重败血症、感染性休克和多器官功能障碍综合征)，失代偿和死亡的风险最高。 移动健康(MHealth)工具、可穿戴设备和人工智能技术迅速激增 用于多种医疗应用，并可弥补在护理危重病人方面的差距 LMIC设置。通过检测表明临床恶化的细微生理变化，这些 工具可以让临床医生更早地进行干预，更好地直接护理，并分配稀缺的资源，所有这些都不需要 需要先进的实验室诊断或重症监护基础设施。此外，远程监控 在改善患者护理和保护医疗保健安全方面，能力也可能被证明是非常有价值的 在传染病暴发期间的工作人员，例如新型冠状病毒2019(新冠肺炎)。 这项拟议的研究将开发一种上下文适当的mHealth工具，将连续生理学 通过一种称为个性化的新型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，为临床医生提供准确的评估 国际达卡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临床医生的形成性研究，b 将用于开发将PPA算法与临床决策支持相结合的mHealth工具，并 供一线临床医生使用的警报系统。最后，对该工具用于检测的可行性、可用性和准确性进行了验证 脓毒症的严重程度和MODS将在新的儿科败血症患者中得到验证。 从这项研究中获得的知识将极大地促进使用mHealth工具和 人工智能技术，帮助世界各地的临床医生更好地护理LMIC环境中的危重儿童 在他们病程的早期，从而减少脓毒症的发病率和死亡率。结果是 这项调查性研究将被用来为多中心临床试验提供信息，该试验将寻求评估 使用该mHealth工具对临床结果的影响以及该工具的成本效益。此工具 也可以提供一种有效的手段来评估患者对各种治疗干预措施的反应 在未来的临床试验中进行持续的生理监测。拟议的举措还将建立一个基础 在移动健康研究能力和以用户为中心的设计方面具有技术和专业知识。