Department of Pediatrics, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA.
Department of Public Health Sciences, University of Rochester Medical Center, Rochester, New York, USA.
J Hosp Med. 2022 Nov;17(11):893-900. doi: 10.1002/jhm.12956. Epub 2022 Aug 29.
Febrile infants are at risk for invasive bacterial infections (IBIs) (i.e., bacteremia and bacterial meningitis), which, when undiagnosed, may have devastating consequences. Current IBI predictive models rely on serum biomarkers, which may not provide timely results and may be difficult to obtain in low-resource settings.
The aim of this study was to derive a clinical-based IBI predictive model for febrile infants.
DESIGNS, SETTING, AND PARTICIPANTS: This is a cross-sectional study of infants brought to two pediatric emergency departments from January 2011 to December 2018. Inclusion criteria were age 0-90 days, temperature ≥38°C, and documented gestational age, fever duration, and illness duration.
To detect IBIs, we used regression and ensemble machine learning models and evidence-based predictors (i.e., sex, age, chronic medical condition, gestational age, appearance, maximum temperature, fever duration, illness duration, cough status, and urinary tract inflammation). We up-weighted infants with IBIs 8-fold and used 10-fold cross-validation to avoid overfitting. We calculated the area under the receiver operating characteristic curve (AUC), prioritizing a high sensitivity to identify the optimal cut-point to estimate sensitivity and specificity.
Of 2311 febrile infants, 39 had an IBI (1.7%); the median age was 54 days (interquartile range: 35-71). The AUC was 0.819 (95% confidence interval: 0.762, 0.868). The predictive model achieved a sensitivity of 0.974 (0.800, 1.00) and a specificity of 0.530 (0.484, 0.575). Findings suggest that a clinical-based model can detect IBIs in febrile infants, performing similarly to serum biomarker-based models. This model may improve health equity by enabling clinicians to estimate IBI risk in any setting. Future studies should prospectively validate findings across multiple sites and investigate performance by age.
发热婴儿有发生侵袭性细菌感染（IBI）（即菌血症和细菌性脑膜炎）的风险，如果未被诊断，可能会产生灾难性的后果。目前的 IBI 预测模型依赖于血清生物标志物，这些标志物可能无法提供及时的结果，并且在资源匮乏的环境中可能难以获得。
本研究旨在为发热婴儿建立一种基于临床的 IBI 预测模型。
设计、地点和参与者：这是一项 2011 年 1 月至 2018 年 12 月期间在两家儿科急诊部门就诊的婴儿的横断面研究。纳入标准为年龄 0-90 天，体温≥38°C，并有记录的胎龄、发热持续时间和疾病持续时间。
为了检测 IBI，我们使用了回归和集成机器学习模型以及基于证据的预测因子（即性别、年龄、慢性疾病、胎龄、外观、最高体温、发热持续时间、疾病持续时间、咳嗽状态和尿路感染）。我们将 IBI 婴儿的权重提高 8 倍，并使用 10 倍交叉验证来避免过度拟合。我们计算了接收器操作特征曲线下的面积（AUC），优先考虑高灵敏度以确定最佳切点来估计敏感性和特异性。
在 2311 名发热婴儿中，有 39 名患有 IBI（1.7%）；中位数年龄为 54 天（四分位距：35-71）。AUC 为 0.819（95%置信区间：0.762，0.868）。预测模型的灵敏度为 0.974（0.800，1.00），特异性为 0.530（0.484，0.575）。研究结果表明，基于临床的模型可以检测发热婴儿的 IBI，其性能与基于血清生物标志物的模型相似。该模型可以通过使临床医生能够在任何环境下估计 IBI 风险，从而提高卫生公平性。未来的研究应在多个地点前瞻性验证这些发现，并研究按年龄划分的性能。
