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: 9762954
• 负责人: KAREN D FAIRCHILD
• 金额: $ 66.26万
• 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 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9762954
• 关键词: Adverse event; Age; Algorithms; Autonomic nervous system; Award; Breathing; Caring; Catastrophic Illness; Central Sleep Apnea; Characteristics; Chest; Clinical; Clinical Data; Collaborations; Data; Data Set; Databases; Death Rate; Deceleration; Delivery Rooms; Development; Diagnosis; Early Diagnosis; Early Intervention; Electrocardiogram; Environment; Event; Functional disorder; Funding; Gestational Age; Goals; Health; Heart Rate; Hemorrhage; Hospitalization; Hospitals; Infant; Infection; Inflammatory; Informatics; Intubation; Lead; Life; Modeling; Monitor; National Institute of Child Health and Human Development; Necrotizing Enterocolitis; Neonatal; Neonatal Intensive Care Units; Nurses; Outcome; Oxygen saturation measurement; Patient Care; Patients; Pattern; Periodicity; Phenotype; Physiologic Monitoring; Physiologic pulse; Physiological; Physiology; Population; Positioning Attribute; Premature Infant; Prostaglandins; Pulse Oximetry; Randomized Clinical Trials; Reporting; Research; Research Design; Resources; Risk; Savings; Sepsis; Signal Transduction; Site; Source; Statistical Data Interpretation; Stream; System; Testing; Time; Universities; Ventilator; Very Low Birth Weight Infant; Virginia; Washington; Work; automated analysis; base; clinical development; clinically relevant; cytokine; experience; improved; improved outcome; large-scale database; mathematical analysis; mortality; novel; prediction algorithm; predictive tools; premature; prospective; randomized trial; respiratory; trend

Premature infants in the Neonatal ICU require hospitalization until they reach physiological maturity, an average of 60 days. While in the hospital, though, they are at risk of subacute potentially catastrophic illnesses such as infection, respiratory decompensation leading to urgent unplanned intubation, and intracranial bleeding. These illnesses are common and deadly. In each case, early diagnosis has the promise to improve outcome through early intervention. The long-term goal of our group is to develop such novel predictive monitoring strategies as early warning systems through advanced mathematical and statistical analysis of waveforms and other informatics data from the bedside monitor. This kind of approach recently led the group and its colleagues at 7 other centers to complete a NICHD- sponsored randomized clinical trial in 3000 premature infants, the largest ever conducted in this population. The result was very important - simply showing the results of a predictive monitor to clinicians reduced the death rate by more than 20%. This predictive tool, though, requires ICU level monitoring with chest leads for EKG and breathing signals. Many more infants could be helped if there were strategies for using just the ubiquitous pulse oximeter, which provides heart rate and O2 saturation data every 1 or 2 seconds. Deriving predictive algorithms that use this small data stream requires large databases of relevant clinical information and monitor data, including vital signs and waveforms, from many infants at multiple sites. The team of clinicians and mathematicians – a collaboration of University of Virginia, Washington University – St Louis, and Columbia University – will discover oximetry-based phenotypes of abnormal physiology and develop algorithms to detect them. The large-scale databases and computing capability for this work is in daily use, the UVa-Columbia collaboration has been productive in the first years of this award, and this competitive renewal proposal will leave them in the position to undertake randomized clinical trials to test the impact of the new monitoring. This represents a paradigm shift in patient care – monitors that report trends of development of health and illness rather than fleeting values.

新生儿ICU中的早产婴儿需要住院直到到达 生理成熟度，平均60天。但是，在医院里，他们在 亚急性的风险潜在灾难性疾病，例如感染，呼吸道 代偿性会导致紧急计划外的插管和颅内出血。 这些疾病是常见和致命的。在每种情况下，早期诊断都有承诺 通过早期干预改善结果。 我们小组的长期目标是制定这种新颖的预测性监测策略 作为预警系统，通过高级数学和统计分析 床头显示器中的波形和其他信息数据。这种方法 最近，该小组及其同事在其他7个中心完成了NICHD- 在3000名早产儿中赞助了随机临床试验，有史以来最大的婴儿 在这个人群中进行。结果非常重要 - 简单地显示结果 临床医生的预测监测率降低了20％以上。 但是，这种预测工具需要使用ICU级别的ICU级别监测，并为EKG和 呼吸信号。如果有使用的策略，可以帮助更多婴儿 只是无处不在的脉搏氧合，它提供心率和O2满意度数据 每1或2秒。得出使用此小数据流的预测算法 需要大量的相关临床信息和监视数据的数据库，包括至关重要 来自多个部位的许多婴儿的体征和波形。临床医生团队和 数学家 - 华盛顿大学弗吉尼亚大学的合作 路易斯和哥伦比亚大学 - 将发现异常的基于氧气的表型 生理和开发算法来检测它们。大规模数据库和 这项工作的计算能力是日常使用，UVA哥伦比亚的合作 在该奖项的头几年和该竞争性更新提案中有生产力 将使他们处于进行随机临床试验的位置，以测试 新监视。 这代表了患者护理的范式转变 - 监控报告趋势的 健康和疾病的发展，而不是短暂的价值观。