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首页> 外文期刊>Pediatric critical care medicine: a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies >A clinic-biological score for diagnosing early-onset neonatal infection in critically ill preterm infants
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A clinic-biological score for diagnosing early-onset neonatal infection in critically ill preterm infants

机译：诊断重症早产儿早发新生儿感染的临床生物学评分

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Objective: To identify the best combination of serum cytokines and clinical parameters to diagnose rapidly early-onset neonatal infection (EONI) in critically ill preterm infants. At birth, most critically ill neonates are receiving broad-spectrum antibiotics pending bacterial culture results, because distinguishing infected from noninfected infants at birth is difficult.Design: Prospective study.Setting: Neonatal intensive care unit in a tertiary care hospital.Patients: Two hundred thirteen infants, born before 33 wks' gestation, admitted to the neonatal intensive care unit within 6 hrs of life with a presumptive diagnosis of EONI. Intervention: A presumptive diagnosis of EONI was associated with a 300-mul blood sample to measure six cytokine (interleukin [IL]-ibeta, IL-6, IL-8, IL-10, IL-12, tumor necrosis factor-alpha) concentrations, using the cytometric bead array technique.Measurements and Main Results: Of the 213 infants included, 31 had a definite or possible EONI and 182 were not infected. Concentrations of IL-6, IL-8, and IL-10 were significantly increased in infected neonates, in comparison with infants without EONI. In contrast, IL-1beta, IL-12, and tumor necrosis factor-alphaconcentrations were not. Logistic regression analyses were performed to construct multivariate predictive models that could distinguish infected from noninfected infants at birth. A clinical score was based on three parameters independently associated with EONI (i.e., interval of >12 hrs between the membranes rupture and delivery, prenatal maternal colonization and mechanical ventilation at birth). This score was compared with scores including clinical parameters and serum cytokines, alone or in combination. The best predictive model combined the three clinical parameters, IL-6 (positive threshold, 300 pg/mL) and IL-8 (positive threshold, 300 pg/mL) concentrations. Conclusion: A predictive model combining serum IL-6 and IL-8 measurements and selected clinical variables could dist...
机译：目的：确定血清细胞因子和临床参数的最佳组合，以诊断重症早产儿的快速早发型新生儿感染（EONI）。出生时，大多数重症新生儿正在接受广谱抗生素治疗，尚待细菌培养结果，因为很难区分出生时与未感染婴儿的感染与感染设计：前瞻性研究背景：三级医院的新生儿重症监护室患者：200 13名在33周妊娠之前出生的婴儿在生命的6小时内进入新生儿重症监护病房，并被诊断为EONI。干预：EONI的推定诊断与300 mul血样相关，以测量六种细胞因子（白介素[IL] -ibeta，IL-6，IL-8，IL-10，IL-12，肿瘤坏死因子-α）测量和主要结果：纳入的213例婴儿中，有31例确诊或可能出现EONI，而182例未感染。与没有EONI的婴儿相比，感染新生儿的IL-6，IL-8和IL-10浓度显着增加。相比之下，IL-1beta，IL-12和肿瘤坏死因子-α的浓度却没有。进行逻辑回归分析以构建多变量预测模型，该模型可以在出生时将感染婴儿与未感染婴儿区分开。临床评分基于与EONI独立相关的三个参数（即，膜破裂和分娩，产前母体定植和出生时机械通气之间的间隔> 12小时）。将该分数与单独或组合的包括临床参数和血清细胞因子在内的分数进行比较。最佳预测模型结合了三个临床参数，即IL-6（阳性阈值，300 pg / mL）和IL-8（阳性阈值，300 pg / mL）浓度。结论：结合血清IL-6和IL-8测量值以及所选临床变量的预测模型可以区分...

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