Predictive informatics monitoring in the Neonatal Intensive Care Unit

新生儿重症监护病房的预测信息学监测

• 批准号: 10363865
• 负责人: KAREN D FAIRCHILD
• 金额: $ 70.92万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-10 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10363865
• 关键词: Address; Adverse event; Alabama; Algorithms; Apnea; Breathing; Caring; Censuses; Characteristics; Clinical; Clinical Data; Collaborations; Complex; Data; Data Analytics; Data Commons; Data Science; Data Set; Demographic Impact; Deterioration; Development; Early Diagnosis; Early treatment; Electrocardiogram; Ensure; Environment; Ethnic Origin; Goals; Grant; Health; Heart Abnormalities; Heart Rate; Hour; Infant; Inflammatory; Informatics; Instruction; Investigation; Life; Low Birth Weight Infant; Machine Learning; Measures; Metadata; Methods; Monitor; Morbidity - disease rate; Necrotizing Enterocolitis; Neonatal Intensive Care Units; Outcome; Patient Care; Patients; Pattern; Performance; Physiologic Monitoring; Physiological; Population; Premature Infant; Premature Mortality; Process; Pulse Oximetry; Pulse Rates; Race; Randomized Clinical Trials; Reporting; Reproducibility; Research; Research Personnel; Resources; Risk; Schools; Seminal; Sepsis; Series; Signal Transduction; Site; Socioeconomic Status; System; Technology; Testing; Time; Time Series Analysis; United States National Institutes of Health; Universities; Variant; Very Low Birth Weight Infant; Virginia; Washington; Work; advanced analytics; adverse outcome; algorithmic bias; analytical tool; base; clinical care; comparative; data access; data sharing; data tools; demographics; deprivation; experience; improved; improved outcome; individual patient; innovation; interest; mortality; novel; operation; prediction algorithm; premature; profiles in patients; randomized trial; sex; socioeconomics; survival outcome; terabyte; trend

PROJECT SUMMARY/ABSTRACT Significance: Premature very low birth weight (VLBW) infants in the Neonatal ICU continue to suffer morbidity and mortality from sepsis and necrotizing enterocolitis (NEC), and early detection and treatment of these illnesses has been shown to improve survival and outcomes. Continuously monitored vital signs contain subtle changes in the early stage of sepsis and NEC, but these physiological markers are invisible with current technology, even for the most sophisticated monitors and experienced clinicians. Our group continues to build on experience and collaboration to develop early warning systems through advanced time series and machine learning data analytics that will improve outcomes for premature infants. Progress: Our first such early warning system, the HeRO monitor, alerts clinicians to abnormal heart rate characteristics and was shown to reduce sepsis-associated mortality by 40% in a randomized trial of 3003 VLBW infants. The past 7 years of NIH support produced several important discoveries, including 1) Adding pulse oximetry oxygenation data (SpO2) to electrocardiogram data improves sepsis and NEC prediction; 2) Center-specific differences in patient demographics and care practices impact algorithm performance; 3) High cross-correlation of heart rate and SpO2 reflects increased apnea and sepsis risk across multiple sites. Innovation: The proposed work represents a paradigm shift in patient care – monitors that report trends of development of health and illness rather than fleeting values, leading to improved outcomes of preterm infants in the NICU. Approach: In the current proposal we are adding a fourth large NICU to accomplish the following aims: Aim 1: Use advanced time series analytics and machine learning to refine and expand predictive monitoring algorithms for sepsis and NEC; Aim 2: Determine the impact of demographics and center on outcomes and algorithm performance; Aim 3: Share multi-center data and analytics globally by building on an existing platform. Investigators: Co-PI's Fairchild and Moorman have a longstanding collaboration and have led successful multicenter clinical and analytical research for the life of this grant and have strengthened the team by adding new collaborators and centers. Environment: The centers involved in this proposal are unique in their ability to collect and analyze large vital sign and clinical data sets, made possible by robust institutional research and computing support.

项目摘要/摘要 意义：新生儿ICU中的早产重量（VLBW）婴儿继续遭受发病率 败血症和坏死性小肠结肠炎（NEC）的死亡率以及这些的早期检测和治疗 疾病已被证明可以改善生存和结果。连续监控的生命体征包含微妙的 败血症和NEC的早期阶段的变化，但是这些物理标记是不可见的 技术，即使是最复杂的监视器和经验丰富的临床医生。我们的小组继续建立 通过经验和协作，通过高级时间序列和机器开发预警系统 学习数据分析，可以改善早产婴儿的结果。进度：我们的第一个这样的早期警告 系统，英雄监视器，提醒临床医生的心率异常，并被证明可以减少 在3003名VLBW婴儿的随机试验中，败血症相关的死亡率与40％相关。 NIH的过去7年 支持产生了几个重要发现，包括1）添加脉搏氧合数据（SPO2） 心电图数据可改善败血症和NEC预测； 2）患者的中心特异性差异 人口统计学和护理实践会影响算法性能； 3）心率高的互相关 SPO2反映了多个地点的呼吸暂停和败血症的风险增加。创新：拟议的工作 代表患者护理的范式转变 - 报告健康和疾病发展趋势的监视 而不是短暂的价值观，导致NICU中早产儿的预后改善。方法：在 当前的建议我们正在添加第四大NICU来实现以下目的：目标1：使用高级 时间序列分析和机器学习，以完善和扩展败血症的预测性监测算法 和NEC；目标2：确定人口统计和中心对结果和算法性能的影响； AIM 3：通过在现有平台上构建在全球范围内共享多中心数据和分析。调查人员：Co-Pi Fairchild和Moorman有一个长期的合作，并领导了成功的多中心临床和 该赠款一生的分析研究，并通过添加新的合作者和 中心。环境：该提案所涉及的中心在收集和分析的能力方面是独一无二的 大型生命体征和临床数据集，通过鲁棒的机构研究和计算支持使得成为可能。