Department of Pharmacy E-302, Sunnybrook Health Sciences Centre (SHSC), 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada.
BMC Pediatr. 2019 Jul 24;19(1):253. doi: 10.1186/s12887-019-1633-1.
Clinical and laboratory parameters can aid in the early identification of neonates at risk for bacteremia before clinical deterioration occurs. However, current prediction models have poor diagnostic capabilities. The objective of this study was to develop, evaluate and validate a screening tool for late onset (> 72 h post admission) neonatal bacteremia using common laboratory and clinical parameters; and determine its predictive value in the identification of bacteremia.
A retrospective chart review of neonates admitted to a neonatal intensive care unit (NICU) between March 1, 2012 and January 14, 2015 and a prospective evaluation of all neonates admitted between January 15, 2015 and March 30, 2015 were completed. Neonates with late-onset bacteremia (> 72 h after NICU admission) were eligible for inclusion in the bacteremic cohort. Bacteremic patients were matched to non-infected controls on several demographic parameters. A Pearson's Correlation matrix was completed to identify independent variables significantly associated with infection (p < 0.05, univariate analysis). Significant parameters were analyzed using iterative binary logistic regression to identify the simplest significant model (p < 0.05). The predictive value of the model was assessed and the optimal probability cut-off for bacteremia was determined using a Receiver Operating Characteristic curve.
Maximum blood glucose, heart rate, neutrophils and bands were identified as the best predictors of bacteremia in a significant binary logistic regression model. The model's sensitivity, specificity and accuracy were 90, 80 and 85%, respectively, with a false positive rate of 20% and a false negative rate of 9.7%. At the study bacteremia prevalence rate of 51%, the positive predictive value, negative predictive value and negative post-test probability were 82, 89 and 11%, respectively.
The model developed in the current study is superior to currently published neonatal bacteremia screening tools. Validation of the tool in a historic data set of neonates from our institution will be completed.
临床和实验室参数可帮助在临床恶化发生之前，早期识别有发生菌血症风险的新生儿。然而，目前的预测模型诊断能力较差。本研究旨在使用常见的实验室和临床参数开发、评估和验证一种用于晚期发病（入院后>72 小时）新生儿菌血症的筛查工具，并确定其在识别菌血症方面的预测价值。
对 2012 年 3 月 1 日至 2015 年 1 月 14 日期间入住新生儿重症监护病房（NICU）的新生儿进行回顾性图表审查，并对 2015 年 1 月 15 日至 2015 年 3 月 30 日期间入住的所有新生儿进行前瞻性评估。符合入选标准的患儿为晚发型菌血症（NICU 入院后>72 小时）。菌血症患儿与非感染对照组在多个人口统计学参数上相匹配。使用 Pearson 相关矩阵确定与感染显著相关的独立变量（p<0.05，单变量分析）。使用迭代二项逻辑回归分析显著参数，以确定最简单的显著模型（p<0.05）。评估模型的预测价值，并使用受试者工作特征曲线确定最佳菌血症概率截断值。
最大血糖、心率、中性粒细胞和带核细胞被确定为菌血症的最佳预测因素，这些因素在显著的二项逻辑回归模型中。模型的灵敏度、特异度和准确度分别为 90%、80%和 85%，假阳性率为 20%，假阴性率为 9.7%。在研究中菌血症的患病率为 51%时，阳性预测值、阴性预测值和阴性后测概率分别为 82%、89%和 11%。
本研究中开发的模型优于目前发表的新生儿菌血症筛查工具。将在我们机构的历史新生儿数据集上验证该工具。
