常规血液检测在区分儿童细菌性与病毒性感染方面优于CRP

中文摘要 AI 辅助

急性感染性疾病是全球儿童就医和住院的主要原因之一。这些感染主要由病毒或细菌引起，但区分两者仍是常见的临床挑战。因此，儿科医生常常默认选择更安全的抗生素处方，这加剧了日益严重的抗菌素耐药性问题。本研究旨在评估全血细胞计数（CBC）对判断当前感染的额外预测价值。这项回顾性研究使用了2022年至2026年间906名年龄在2至14岁之间、病毒或细菌感染检测呈阳性的儿科患者的数据。纳入标准还要求提供CBC结果和CRP水平测量值。这些实验室参数以及年龄被用作几种监督分类模型的输入特征。模型性能通过AUC、敏感性和特异性进行评估。表现最佳的模型是XGBoost，它包含了所有特征，实现了样本外AUC为81.7%、敏感性为70.8%、特异性为79.2%的性能。所有训练的模型在AUC方面均优于仅基于CRP的决策规则模型。我们建议，决定是否开具抗生素处方应基于多种因素，包括但不限于CBC，其中一些因素目前尚未纳入常规实践。

英文摘要

Acute infectious diseases are among the leading causes of medical consultations and hospitalizations in children worldwide. These infections are predominantly caused by viruses or bacteria, yet differentiating between the two remains a common clinical challenge. As a result, pediatricians often default to the safer option of prescribing antibiotics contributing to the growing problem of antimicrobial resistance. The objective is to assess the additional predictive value of CBC towards determining the current infection. This retrospective study used data from 906 pediatric patients aged between 2 and 14 years who were tested positive either for viral or bacterial infection between 2022 and 2026. Inclusion criteria further required availability of CBC results and CRP level measurements. These laboratory parameters as well as age were used as input features for several supervised classification models. Model performance was evaluated using AUC, sensitivity and specificity. The best performing model is XGBoost, which included all features, achieving out of-sample performance of AUC of 81.7% and sensitivity of 70.8%, specificity of 79.2%. All trained models outperform a CRP-based only decision-rule model in terms of AUC. We suggest that the decision to prescribe antibiotics should be based on a number of factors, including but not limited to CBC, some of which are not currently incorporated into routine practice.

发表机构

• Vector Labs（Vector 实验室）
• Zdraveto Hospital（Zdraveto 医院）

机构由 AI 辅助整理，请以论文原文为准。