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: 10268409
• 负责人: Adam Carl Levine
• 金额: $ 17.87万
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10268409
• 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; prevent; prospective; remote monitoring; septic; 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 败血症被定义为威胁生命的器官功能障碍，这是由于宿主对感染反应失调的反应， 包含从败血症到严重败血症的连续体，败血性休克，多器官功能障碍 综合征（mod），有时未经治疗，有时死亡。败血症是全球儿童死亡率的主要原因， 这些死亡大多数发生在低收入和中等收入国家（LMIC），但很少有临床工具 我们被开发用于识别，监测或管理LMIC的化粪池儿童。有巨大的潜力 对于可以帮助临床医生更快地识别患有晚期阶段的儿童的新型临床工具 败血症（严重的败血症，败血性休克和mods），他们的死亡和死亡风险最高。 移动健康（MHealth）工具，可穿戴设备和人工智能技术已迅速增殖 对于多种医疗应用，可以弥合差距，以照顾重病患者 LMIC设置。通过启用表明临床定义的细微生理变化的检测，这些 工具可能会使临床医生能够更早进行干预，更好的直接护理和分配稀缺资源，而无需 需要先进的实验室诊断或重症监护基础设施。此外，远程监视 能力也可能证明在改善患者护理和保护医疗保健安全方面可能非常有价值 在传染病暴发时期，例如新型冠状病毒2019（Covid-19）。 这项拟议的研究将开发一种适合上下文的MHealth工具，将连续生理联系起来 从可穿戴设备获得的数据，具有新颖的机器学习方法，称为个性化 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 败血症的严重程度和死亡率风险在国际达卡医院接受 孟加拉国腹泻病研究中心（ICDDR，B）。 ICDDR的临床医生的格式化研究 将用于开发此MHealth工具，以临床决策支持和 一线临床医生使用的警报系统。最后，该工具的可行性，可用性和准确性可检测 脓毒症的严重程度和mod将在新的败血症患者中得到验证。 从这项研究中获得的知识将大大提高使用MHealth工具的证据基础和 人工智能技术可帮助全球临床医生在LMIC环境中更好地护理重症儿童 在疾病的早期，从而降低了败血症的发病率和死亡率。结果 该研究研究将用于为多中心临床试验提供信息，该试验将试图评估 使用此MHealth工具对临床结果以及该工具的成本效益的影响。这个工具 还可以提供有效的方法来评估患者对各种治疗干预措施的反应 在以后的临床试验中进行连续的身体监测。拟议的倡议还将建立 ICDDR的技术和专业知识，B，MHealth研究能力和以用户为中心的设计。