基于临床的评估方法对发热婴儿的细化与验证。

Refinement and Validation of a Clinical-Based Approach to Evaluate Young Febrile Infants.

机构信息

aDepartments of Pediatrics, and.

cPublic Health Sciences, University of Rochester Medical Center, Rochester, New York.

出版信息

Hosp Pediatr. 2022 Apr 1;12(4):399-407. doi: 10.1542/hpeds.2021-006214.

DOI:10.1542/hpeds.2021-006214

PMID:35347337

Abstract

BACKGROUND AND OBJECTIVE

For febrile infants, predictive models to detect bacterial infections are available, but clinical adoption remains limited by implementation barriers. There is a need for predictive models using widely available predictors. Thus, we previously derived 2 novel predictive models (machine learning and regression) by using demographic and clinical factors, plus urine studies. The objective of this study is to refine and externally validate the predictive models.

METHODS

This is a cross-sectional study of infants initially evaluated at one pediatric emergency department from January 2011 to December 2018. Inclusion criteria were age 0 to 90 days, temperature ≥38°C, documented gestational age, and insurance type. To reduce potential biases, we derived models again by using derivation data without insurance status and tested the ability of the refined models to detect bacterial infections (ie, urinary tract infection, bacteremia, and meningitis) in the separate validation sample, calculating areas-under-the-receiver operating characteristic curve, sensitivities, and specificities.

RESULTS

Of 1419 febrile infants (median age 53 days, interquartile range = 32-69), 99 (7%) had a bacterial infection. Areas-under-the-receiver operating characteristic curve of machine learning and regression models were 0.92 (95% confidence interval [CI] 0.89-0.94) and 0.90 (0.86-0.93) compared with 0.95 (0.91-0.98) and 0.96 (0.94-0.98) in the derivation study. Sensitivities and specificities of machine learning and regression models were 98.0% (94.7%-100%) and 54.2% (51.5%-56.9%) and 96.0% (91.5%-99.1%) and 50.0% (47.4%-52.7%).

CONCLUSIONS

Compared with the derivation study, the machine learning and regression models performed similarly. Findings suggest a clinical-based model can estimate bacterial infection risk. Future studies should prospectively test the models and investigate strategies to optimize clinical adoption.

摘要

背景与目的

对于发热婴儿，已有预测模型可用于检测细菌感染，但由于实施障碍，临床应用仍然有限。因此，我们需要使用广泛可用的预测因子来建立预测模型。为此，我们之前使用人口统计学和临床因素以及尿液研究建立了 2 种新的预测模型（机器学习和回归）。本研究的目的是改进和外部验证这些预测模型。

方法

这是一项横断面研究，纳入 2011 年 1 月至 2018 年 12 月在一家儿科急诊部就诊的初始评估的婴儿。纳入标准为年龄 0 至 90 天，体温≥38°C，记录的胎龄和保险类型。为了减少潜在的偏倚，我们在没有保险状态的情况下再次使用推导数据来建立模型，并在单独的验证样本中测试改进后的模型检测细菌感染（即尿路感染、菌血症和脑膜炎）的能力，计算接受者操作特征曲线下面积、敏感性和特异性。

结果

在 1419 名发热婴儿中（中位数年龄 53 天，四分位距 32-69），99 名（7%）患有细菌感染。机器学习和回归模型的接受者操作特征曲线下面积分别为 0.92（95%置信区间 [CI] 0.89-0.94）和 0.90（0.86-0.93），而在推导研究中为 0.95（0.91-0.98）和 0.96（0.94-0.98）。机器学习和回归模型的敏感性和特异性分别为 98.0%（94.7%-100%）和 54.2%（51.5%-56.9%）以及 96.0%（91.5%-99.1%）和 50.0%（47.4%-52.7%）。