Predicting Infection in Very Preterm Infants: A Study Protocol.
Predicting Infection in Very Preterm Infants: A Study Protocol.
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DOI：
10.1097/nnr.0000000000000483
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发表时间：
2021-03-01
期刊：
影响因子：
2.5
通讯作者：
Murphy HJ
中科院分区：
文献类型：
--
作者：
Dail RB;Everhart KC;Hardin JW;Chang W;Kuehn D;Iskersky V;Fisher K;Murphy HJ
新生儿败血症导致早产儿的发病率和死亡率。临床医生需要一种预测新生儿感染发作的工具，以加快治疗和预防发病。异常的温度梯度、> 2°C或< 0°C的中心-外周温差（CPtd）以及心率特征（HRC）评分升高与感染相关。本文介绍了使用温度和心率的预测分析（PATH）研究的协议。这项观察性试验将招募440名极早产儿，每分钟测量腹部温度（AT）和足部温度（FT），每小时测量HRC评分，持续28天，以与感染数据进行比较。将异常热梯度（模型1）和HRC评分升高（模型2）的时间与感染发作进行比较。对于数据分析，CPtd（AT-FT）将作为两个衍生变量进行研究，即高CPtd（CPtd > 2°C的分钟数/百分比）和低CPtd（CPtd < 0°C的分钟数/百分比）。在婴儿水平模型中，结果yi将是婴儿在出生后28天内是否被诊断为感染的指标，高CPtd和低CPtd变量将是整个观察期的平均值; logit（yi）= β0 + xiβ1 + ziγ。对于日水平模型，结果yit将是第i个婴儿是否在t = 4至t = 28的第t天或诊断感染的当天（25个可能的重复测量）被诊断为感染的指标logit（yit）= β0 + zit β1 + zitγ。将确定仅具有高CPtd或仅具有低CPtd的模型在预测感染方面是上级的。此外，还将评估异常HRC评分与高CPtd和低CPtd值的相关性。研究结果将为使用温度和/或心率作为预测工具的干预性研究的设计提供信息，以提醒临床医生感染时存在心脏和自主神经不稳定。
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. This article presents the protocol for the Predictive Analysis using Temperature and Heart Rate (PATH) study. This observational trial will enroll 440 very preterm infants to measure abdominal temperature (AT) and foot temperature (FT) every minute and HRC scores hourly for 28 days to compare to 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 (AT-FT) will be investigated as two derived variables high CPtd (number/percentage of minutes with CPtd > 2°C) and low CPtd (number/percentage of minutes with CPtd < 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. 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.