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

极低出生体重儿新生儿期中心和外周体温 新生儿感染与坏死性小肠结肠炎的关系 目的：我们将测量连续腹温(AT)和足温(FT)。 对来自5个新生儿单元的440名早产儿在28天内预测感染的发生。的异常值 中枢-外周温差(CpTD)大于2℃，小于0，是自主神经的标志 在感染期间发生的不稳定。我们将对异常CpTD值之间的时间关系进行建模 并对感染进行诊断，找出最佳预测模型，将连续体温监测作为 非侵入性、廉价的感染预测监测工具。为了进行比较，我们将连续测量 来自HERO监护仪的心率特征(HRC)使用心脏预测早产儿感染 速率可变性。将CpTD和HRC的预测模型进行比较和组合，以找到最优的 我们样本中的感染预测性监测工具。我们将检验婴儿人口统计的协变量 预测因素之间交互作用的因素、产妇产科病史和婴儿的临床环境 CpTD和HRC的感染。方法：婴儿将从北卡罗来纳州和南卡罗来纳州的五个NICU登记。 研究护士或现场PI将获得父母的同意，将婴儿登记在6小时内，如果他们 胎龄不到32周，出生时不到1500克。研究人员将附上一份 每个受试婴儿的脚底和腹部都有热敏电阻。热敏电阻将连接到一个 数据记录器，它将测量生命最初28天的每分钟温度。一位英雄班长将成为 连接到每个婴儿的标准心肺监护仪和英雄监护仪中的USB驱动器将收集 每小时一次的HRC评分为28天。产妇产科病史、婴儿人口学和婴儿临床情况 变量将被输入到RedCap数据库中，以便用生理数据进行分析。数据管理 分析将在南卡罗来纳大学护理学院进行。数据分析：我们的 这项研究的兴趣是CpTD(AT-FT=CpTD)。我们将把这一指标作为两个派生变量进行研究 HTD(使用CpTd&>2的分钟数/百分比)和NTD(使用CpTD&lt；0的分钟数/百分比) 它们都是连续变量。将使用以下定义的HTD和NTD信息构建预测模型 不同的前置时间长度(24至72小时)，以最终产生用于诊断的最佳预测模型 基于CpTD的感染。我们的预测模型将与使用蒙特卡罗的HRC模型进行比较 交叉验证方法，我们使用70%的数据用于训练，30%用于验证。最后，我们会 将HRC预测协变量与CpTD协变量组合，以调查组合模型是否 提供了对预测的进一步改进。如果我们的CpTD预测模型改进了HRC模型，它将服务于 作为随后提出的用新数据验证CpTD预测的基础。

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.