PDF(Pubmed)
Abstract：
Early recognition of bloodstream infection (BSI) in infants can be difficult, as symptoms may be non-specific, and culture can take up to 48 h. As a result, many infants receive unneeded antibiotic treatment while awaiting the culture results. In this study, we aimed to develop a model that can reliably identify infants who do not have positive blood cultures (and, by extension, BSI) based on the full blood count (FBC) and C-reactive protein (CRP) values. Several models (i.e. multivariable logistic regression, linear discriminant analysis, K nearest neighbors, support vector machine, random forest model and decision tree) were trained using FBC and CRP values of 2693 infants aged 7 to 60 days with suspected BSI between 2005 and 2022 in a tertiary paediatric hospital in Dublin, Ireland. All models tested showed similar sensitivities (range 47% - 62%) and specificities (range 85%-95%). A trained decision tree and random forest model were applied to the full dataset and to a dataset containing infants with suspected BSI in 2023 and showed good segregation of a low-risk and high-risk group. Negative predictive values for these two models were high for the full dataset (> 99%) and for the 2023 dataset (> 97%), while positive predictive values were low in both dataset (4%-20%). Conclusion: We identified several models that can predict positive blood cultures in infants with suspected BSI aged 7 to 60 days. Application of these models could prevent administration of antimicrobial treatment and burdensome diagnostics in infants who do not need them. What is Known: • Bloodstream infection (BSI) in infants cause non-specific symptoms and may be difficult to diagnose. • Results of blood cultures can take up to 48 hours. What is New: • Machine learning models can contribute to clinical decision making on BSI in infants while blood culture results are not yet known.
摘要：
早期识别婴儿的血流感染（BSI）可能很困难，因为症状可能是非特异性的，培养可能需要48小时。因此，许多婴儿在等待培养结果时接受不必要的抗生素治疗。在这项研究中,我们的目标是开发一种模型，可以可靠地识别没有阳性血培养的婴儿(和，通过延伸,BSI）基于全血计数（FBC）和C反应蛋白（CRP）值。几个模型(即多变量逻辑回归，线性判别分析，K最近的邻居，支持向量机，随机森林模型和决策树）在都柏林的一家三级儿科医院中，使用2005年至2022年之间的2693名7至60天的可疑BSI婴儿的FBC和CRP值进行了训练，爱尔兰。所有测试的模型显示相似的敏感性（范围47％-62％）和特异性（范围85％-95％）。在2023年，将经过训练的决策树和随机森林模型应用于完整数据集和包含疑似BSI婴儿的数据集，并显示低风险和高风险人群的良好隔离。对于完整数据集（>99%）和2023年数据集（>97%），这两个模型的负预测值很高，而两个数据集中的阳性预测值均较低(4%-20%)。结论：我们确定了几种可以预测7至60天可疑BSI婴儿血培养阳性的模型。这些模型的应用可以防止在不需要它们的婴儿中施用抗微生物治疗和繁重的诊断。已知:•婴儿的血流感染(BSI)引起非特异性症状并且可能难以诊断。•血液培养的结果可能需要长达48小时。新功能：•机器学习模型可以为婴儿BSI的临床决策做出贡献，而血液培养结果尚不清楚。
