预测极早产儿感染：研究方案。
Predicting Infection in Very Preterm Infants: A Study Protocol.
出版信息
Nurs Res. 2021;70(2):142-149. doi: 10.1097/NNR.0000000000000483.
BACKGROUND
Neonatal sepsis causes morbidity and mortality in preterm infants. Clinicians need a predictive tool for the onset of neonatal infection to expedite treatment and prevent morbidity. Abnormal thermal gradients, a central-peripheral temperature difference (CPtd) of >2°C or <0°C, and elevated heart rate characteristic (HRC) scores are associated with infection.
OBJECTIVE
This article presents the protocol for the Predictive Analysis Using Temperature and Heart Rate Study.
METHODS
This observational trial will enroll 440 very preterm infants to measure abdominal temperature and foot temperature every minute and HRC scores hourly for 28 days to compare infection data. Time with abnormal thermal gradients (Model 1) and elevated HRC scores (Model 2) will be compared to the onset of infections. For data analysis, CPtd (abdominal temperature - foot temperature) will be investigated as two derived variables, high CPtd (number/percentage of minutes with CPtd of >2°C) and low CPtd (number/percentage of minutes with CPtd of <0°C). In the infant-level model, the outcome yi will be an indicator of whether the infant was diagnosed with an infection in the first 28 days of life, and the high CPtd and low CPtd variables will be the average over the entire observation period, logit(yi) = β0 + xiβ1 + ziγ. For the day-level model, the outcome yit will be an indicator of whether the ith infant was diagnosed with an infection on the tth day from t = 4 through t = 28 or the day that infection is diagnosed (25 possible repeated measures), logit(yit) = β0 + xitβ1 + zitγ. It will be determined whether a model with only high CPtd or only low CPtd is superior in predicting infection. Also, the correlation of abnormal HRC scores with high CPtd and low CPtd values will be assessed.
DISCUSSION
Study results will inform the design of an interventional study using temperatures and/or heart rate as a predictive tool to alert clinicians of cardiac and autonomic instability present with infection.
背景
新生儿败血症会导致早产儿发病和死亡。临床医生需要一种预测新生儿感染发作的工具，以加快治疗并预防发病。异常的温度梯度，即中心-外周温差（CPtd）>2°C 或<0°C，以及升高的心率特征（HRC）评分与感染有关。
目的
本文介绍了使用温度和心率进行预测分析的研究方案。
方法
本观察性试验将纳入 440 例极早产儿，在 28 天内每 1 分钟测量腹部温度和足部温度，每小时测量 HRC 评分，以比较感染数据。异常温度梯度（模型 1）和升高的 HRC 评分（模型 2）的持续时间将与感染发作进行比较。对于数据分析，CPtd（腹部温度-足部温度）将作为两个派生变量进行研究，高 CPtd（CPtd>2°C 的分钟数/百分比）和低 CPtd（CPtd<0°C 的分钟数/百分比）。在个体水平模型中，因变量 yi 将表示婴儿在生命的头 28 天是否被诊断为感染，高 CPtd 和低 CPtd 变量将是整个观察期的平均值，logit(yi) = β0 + xiβ1 + ziγ。在日水平模型中，因变量 yit 将表示第 i 个婴儿在第 t 天（t = 4 至 t = 28）或感染被诊断的那天（25 次可能的重复测量）是否被诊断为感染，logit(yit) = β0 + xitβ1 + zitγ。将确定仅使用高 CPtd 或仅使用低 CPtd 的模型在预测感染方面是否更优。此外，还将评估异常 HRC 评分与高 CPtd 和低 CPtd 值的相关性。
讨论
研究结果将为使用温度和/或心率作为预测工具来提醒临床医生感染时存在心脏和自主神经不稳定的干预性研究设计提供信息。
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