Public Health Laboratory, HSE, Cherry Orchard Hospital, Dublin, Ireland.
European Public Health Microbiology Training Programme (EUPHEM), European Centre for Disease Prevention and Control, Stockholm, Sweden.
Eur J Pediatr. 2024 Jul;183(7):2983-2993. doi: 10.1007/s00431-024-05441-6. Epub 2024 Apr 18.
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.
早期识别婴儿血流感染（BSI）可能具有挑战性，因为症状可能不具有特异性，且培养可能需要长达 48 小时。因此，许多婴儿在等待培养结果时会接受不必要的抗生素治疗。在这项研究中，我们旨在开发一种模型，该模型可以基于全血细胞计数（FBC）和 C 反应蛋白（CRP）值，可靠地识别出那些没有阳性血培养（并因此没有 BSI）的婴儿。使用 2005 年至 2022 年期间爱尔兰都柏林一家三级儿科医院中 2693 名年龄在 7 至 60 天且疑似 BSI 的婴儿的 FBC 和 CRP 值，训练了多个模型（即多变量逻辑回归、线性判别分析、K 最近邻、支持向量机、随机森林模型和决策树）。所有测试的模型都显示出相似的敏感性（范围为 47%-62%）和特异性（范围为 85%-95%）。经过训练的决策树和随机森林模型应用于全数据集以及包含 2023 年疑似 BSI 婴儿的数据集，显示出低风险和高风险组的良好分离。对于这两个模型，全数据集的阴性预测值较高（>99%），2023 年数据集的阴性预测值也较高（>97%），而两个数据集的阳性预测值都较低（4%-20%）。结论：我们确定了几种可以预测 7 至 60 天龄疑似 BSI 婴儿阳性血培养的模型。应用这些模型可以防止对不需要抗生素治疗和繁琐诊断的婴儿进行治疗。已知情况：•婴儿血流感染（BSI）引起非特异性症状，可能难以诊断。•血液培养结果可能需要长达 48 小时。新情况：•机器学习模型可以为尚未获得血培养结果时的婴儿 BSI 临床决策提供帮助。
