Neonatal apnea: Online risk score from new analyses of bedside monitor waveforms

新生儿呼吸暂停：根据床边监护仪波形的新分析得出的在线风险评分

基本信息

批准号：
7854155
负责人：
JOHN KATTWINKEL
金额：
$ 100.81万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-30 至 2011-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7854155
关键词：
Algorithms Apnea Applications Grants Area Arts Beds Birth Catastrophic Illness Censuses Cessation of life Characteristics Chest Clinical Clinical Engineering Computer software Computers Coupled Data Databases Detection Diagnosis Discipline Discipline of Nursing Disease Documentation Early Diagnosis Electrocardiogram Electronics Entropy Event Failure Foundations Frequencies Funding Agency Future Goals Grant Hand Handwriting Healthcare Heart Rate Home environment Hospital Costs Hospitalization Hospitals Impedance Plethysmography Infant Infant Health Intercept Judgment Knowledge Length of Stay Life Low Birth Weight Infant Measures Methods Metric Modeling Monitor Morbidity - disease rate National Institute of Child Health and Human Development National Institute of General Medical Sciences Natural History Neonatal Neonatal Apneic Attack Neonatal Intensive Care Units Nurses Outcome Oxygen Patients Premature Infant Public Health Randomized Records Research Research Personnel Resources Risk Risk Assessment Running Signal Transduction Solutions Sudden infant death syndrome System Technology Testing Therapy Clinical Trials Time United States National Institutes of Health Validation Ventilator Vital Statistics Work base cost data acquisition digital experience improved indexing innovation insight model development mortality neonatal sepsis predictive modeling premature programs public health relevance respiratory tool treatment planning

项目摘要

DESCRIPTION (provided by applicant): Neonatal apnea, or apnea of prematurity, is the most common serious disorder of premature infants in the neonatal intensive care unit (NICU). There are significant gaps in knowledge in its definition, documentation and management. For example, despite continuous electronic monitoring in the NICU, apnea episodes are diagnosed based only on clinical judgment and recorded only by hand on bedside flow sheets. The lack of quantitative and standardized apnea metrics has stalled efforts to understand its natural history, its therapy, and its relationship to the major public health problem of sudden infant death syndrome, or SIDS. Our long term goals are to change NICU practice by providing doctors and nurses with real time risk assessment for subacute, potentially catastrophic diseases. Here, our Specific Aim is to develop and validate multivariable predictive models for continuous monitoring of neonatal apnea using old and new mathematical measures of waveforms and vital signs recorded on NICU bedside monitors. We will deliver a short-term apnea index and a cumulative apnea burden as management and research tools suitable for mechanistic insights and for randomized therapeutic trials. The project is responsive to the scope and requirements of the Grand Opportunities grants program. We plan a large-scale and accelerated program to generate a large database of neonatal clinical and monitor waveform data, and to develop monitoring algorithms. The database is a rich resource for other researchers, and the algorithms will be innovative solutions to current barriers to data interpretation in NICUs, and promise improved infant health. New large scale computing hardware and software is already running. Our team has unique experience in developing new monitoring algorithms in the NICU, and represents clinical, engineering and mathematical disciplines. No other entity is likely to conceive or to execute this work, and no other funding source seems likely. PUBLIC HEALTH RELEVANCE: Apnea of prematurity (AOP) is a common life-threatening disorder that often extends expensive hospitalization of premature babies. While methods for detecting, recording, and classifying AOP currently depend on highly unreliable nursing observations, computer technology and storage capacity are now readily available to permit continuous on line analysis of existing signals from routine bedside monitors to predict apnea risk. We have the technology, expertise, and existing network to develop and verify a sensitive measure of apnea risk which could be widely implemented in other NICUs to save substantial hospital costs through earlier safe hospital discharge, as well as to contribute valuable knowledge about maturation of respiratory control and provide a useful tool and database for future studies of AOP and Sudden Infant Death Syndrome (SIDS).

描述（由申请人提供）：新生儿呼吸暂停或早产呼吸暂停是新生儿重症监护病房（NICU）中最常见的早产儿。知识的定义，文档和管理存在很大的差距。例如，尽管在NICU中进行了连续的电子监测，但仅根据临床判断而诊断呼吸暂停发作，并仅通过手工记录床头流量表。缺乏定量和标准化的呼吸暂停指标已经停滞了理解其自然史，疗法以及与猝死综合症的主要公共卫生问题的关系。我们的长期目标是通过为医生和护士对亚急性，潜在的灾难性疾病进行实时风险评估来改变NICU实践。在这里，我们的具体目的是开发和验证多变量预测模型，以连续监测新生儿呼吸暂停，并使用在NICU床边监视器上记录的波形和生命体征的旧数学和新的数学测量。我们将提供短期呼吸暂停指数和累积呼吸暂停负担，作为适合机械见解和随机治疗试验的管理和研究工具。该项目响应了大机会赠款计划的范围和要求。我们计划一个大规模和加速程序，以生成一个大型新生儿临床和监测波形数据的数据库，并开发监测算法。该数据库是其他研究人员的丰富资源，该算法将是NICUS数据解释障碍的创新解决方案，并承诺改善了婴儿的健康状况。新的大型计算硬件和软件已经运行。我们的团队在开发NICU的新监测算法方面具有独特的经验，并代表临床，工程和数学学科。没有其他实体可能会构想或执行这项工作，而且似乎没有其他资金来源。公共卫生相关性：早产呼吸暂停（AOP）是一种常见的威胁生命的疾病，通常会延长早产婴儿的昂贵住院治疗。尽管目前可以检测，记录和分类AOP的方法取决于高度不可靠的护理观察结果，但现在很容易获得计算机技术和存储容量，以允许连续分析常规床边监视器的现有信号以预测呼吸暂停风险。我们拥有技术，专业知识和现有网络来开发和验证对呼吸暂停风险的敏感度量，可以在其他NICUS中广泛实施，以通过较早的安全医院出院来节省大量医院成本，并为呼吸道控制的成熟提供有价值的知识，并为AOP和突然的婴儿死亡综合症（SIDS）提供有用的工具和数据库。