Heart rate characteristics monitoring in newborn infants

描述（由申请人提供）：脓毒症是一个主要的健康问题， 新生儿重症监护室（NICU）中的高危新生儿， 发生在25%的极低出生体重婴儿中， 死亡率翻了一番，住院时间增加了50%。我们的长期 目的是检验检测异常心率 持续非侵入性监测的特征（HRC）将改善护理 在这些患者中，通过早期诊断脓毒症和其他亚急性， 潜在的灾难性疾病。我们建议朝着这个目标前进 完成两个研究目标。 在目标1中，我们将前瞻性地研究在大学新生儿重症监护病房（NICU）的早产儿 为了验证假设，即异常HRC将与即将到来的 根据客观疾病标准定义的脓毒症和脓毒症样疾病，以及 人权委员会增加了重要的独立诊断信息， 败血症和败血症样疾病与出生体重和 胎龄临床研究设计是为了推导 来自一组患者的多变量预测统计模型， 通过使用来自第二组患者的临床数据的验证阶段。 在目标2中，我们将制定人权委员会的新措施， 新生儿败血症的早期检测。我们将研究最佳选择 样本熵计算参数、心率时间平稳性 系列使用传统的和新的措施的基础上，经验累积 分布函数和频域分析，使用一种新的方法。

DESCRIPTION (provided by applicant): Sepsis is a major health problem in high-risk newborn infants in the neonatal intensive care unit (NICU), where it occurs in 25 percent of very low birth weight infants and leads to a more than doubling of mortality and a 50 percent increase in hospital stay. Our long-term objective is to test the hypothesis that detection of abnormal heart rate characteristics (HRC) with continuous non-invasive monitoring will improve care of these patients by earlier diagnosis of sepsis and other sub-acute, potentially catastrophic illnesses. We propose to advance toward this objective by completing two research aims. In Aim 1, we will prospectively study unselected infants in a university NICU to test the hypotheses that abnormal HRC will be associated with upcoming sepsis and sepsis-like illness as defined by objective illness criteria, and that HRC adds significant independent diagnostic information about the risk of sepsis and sepsis-like illness to clinical variables of birth weight and gestational age. The clinical research design is for derivation of multivariable predictive statistical models from one set of patients followed by a validation phase using clinical data from a second set of patients. In Aim 2, we will develop new measures of HRC that are specific to the task of early detection of neonatal sepsis. We will investigate optimum choice of parameters for sample entropy calculation, stationarity of heart rate time series using conventional and novel measures based on the empirical cumulative distribution function, and frequency domain analysis using a novel method.