Central & peripheral body temperature in VLBW preterm infants during the neonatal period: Relationship to neonatal infection and necrotizing enterocolitis

新生儿期VLBW早产儿的中心和外周体温： 新生儿感染与坏死性小肠结肠炎的关系 目的：我们将测量连续腹部温度（AT）和足部温度（FT）， 来自五个新生儿病房的440名早产儿在28天内预测感染的发生。异常值 中心-外周温差（CpTD）大于2º且小于0，是自主神经的标志 在感染过程中发生的不稳定性。我们将模拟异常CpTD值之间的时间关系 和感染的诊断，以找到最佳的预测模型，使用连续的温度监测， 非侵入性，廉价的感染预测监测工具。为了进行比较，我们将测量连续的 HeRO监测器的心率特征（HRC），该监测器使用心脏预测早产儿的感染 速率变异性将比较和组合CpTD和HRC的预测模型，以找到最佳方案 我们样本中感染的预测监测工具。我们将研究婴儿人口统计学的协变量， 因素，母亲的产科史，婴儿的临床背景之间的相互作用的影响，我们的预测 CpTD和HRC检测感染方法：婴儿将从北卡罗来纳州和南卡罗来纳州的五个NICU入组。 研究护士或研究中心PI将在以下情况下为入组6小时以下婴儿征得父母同意： 胎龄小于32周，出生时体重小于1500克。研究人员将附上 将热敏电阻贴到每只研究婴儿的脚底和腹部。热敏电阻将连接到 数据记录器，在出生后的前28天内每分钟测量一次温度。HeRO监测器将 连接到每个婴儿的标准心肺监测仪和HeRO监测仪中的USB驱动器， 每小时HRC评分28天。产妇产科史、婴儿人口统计学和婴儿临床背景 将变量输入RedCap数据库中，以与生理数据一起进行分析。数据管理 分析将在南卡罗来纳州大学护理学院进行。数据分析：我们的 本研究的目的是CpTD（AT-FT=CpTD）。我们将把这个度量作为两个导出变量来研究 HTD（CpTd>2的分钟数/百分比）和NTD（CpTD<0的分钟数/百分比） 连续变量预测模型将利用以下定义的HTD和NTD信息构建： 不同长度的前一时间（24至72小时），以最终产生用于诊断的最佳预测模型 感染的概率我们的预测模型将与利用蒙特卡罗的HRC模型进行比较 交叉验证方法，其中我们使用70%的数据进行训练，30%用于验证。最后我们将 联合收割机将HRC预测协变量与CpTD协变量结合，以研究联合模型是否 进一步改善预测。如果我们的CpTD预测模型改进了HRC模型， 作为后续建议的基础，以利用新数据验证CpTD预测。

Central & peripheral body temperature in VLBW preterm infants during the neonatal period: Relationship to neonatal infection and necrotizing enterocolitis Objective: We will measure continuous abdominal temperatures (AT) and foot temperatures (FT) in 440 preterm infants from five neonatal units over 28 days to predict occurrence of infection. Abnormal values of the central-peripheral temperature difference (CpTD), greater than 2º and less than 0, are a sign of autonomic instability occurring during infection. We will model the temporal relationship between abnormal CpTD values and the diagnosis of infection to find the best predictive model to use continuous temperature monitoring as a non-invasive, inexpensive predictive monitoring tool for infection. For comparison, we will measure continuous heart rate characteristics (HRC) from a HeRO monitor which predicts infection in preterm infants using heart rate variability. Predictive models of CpTD and HRC will be compared and combined to find the optimal predictive monitoring tool for infection in our sample. We will examine covariates of infants' demographic factors, maternal obstetrical histories, and infants' clinical context for interaction effects between our predictors of CpTD and HRC for infection. Methods: Infants will be enrolled from five NICUs in North and South Carolina. Research nurses or site PIs will obtain parental consent for infants to be enrolled up to 6 hours of age, if they are less than 32 weeks gestational age and less than 1500 grams at birth. Study personnel will attach a thermistor to the sole of the foot and to the abdomen for each study infant. Thermistors will be attached to a data logger which will measure temperature every minute for the first 28 days of life. A HeRO monitor will be attached to each infant's standard cardiopulmonary monitor and a USB drive in the HeRO monitor will collect hourly HRC scores for 28 days. Maternal obstetrical history, infant demographics and infant clinical context variables will be entered into a RedCap data base to be analyzed with physiological data. Data management and analyses will be conducted at University of South Carolina, College of Nursing. Data Analysis: Our interest for this study is the CpTD (AT-FT=CpTD). We will investigate that measure as two derived variables HTD (number/percentage of minutes with CpTd>2) and NTD (number/percentage of minutes with CpTD<0) which are continuous variables. Predictive models will be built utilizing HTD and NTD information defined over various lengths of preceding time (24 to 72 hours) to ultimately yield the optimal predictive model for diagnosis of infection based on CpTD. Our predictive model will be compared to the HRC model utilizing a Monte Carlo cross-validation approach in which we use 70% of the data for training and 30% for validation. Finally, we will combine the HRC predictive covariates to the CpTD covariates to investigate whether the combined model offers further improvements to prediction. If our CpTD predictive model improves the HRC model, it will serve as the foundation of a subsequent proposal to validate the CpTD predictive with new data.