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机器学习工具用于预测由ESBL产生细菌引起的儿科尿路感染
Chen Hajaj1, Shani Alkoby1, Shai Ashkenazi2,3
1Research Authority, Rabin Medical Center, Petach Tikva, Israel.
The Pediatric infectious disease journal
|February 12, 2026
概括
机器学习模型可以预测由扩展光谱β-乳酸酶 (ESBL) 生产细菌引起的儿科尿路感染 (UTIs). 这些工具有助于临床医生识别高风险病例,以进行适当的抗生素选择.
科学领域:
- 儿童传染病 儿童传染病
- 医疗信息学医学信息学
- 机器学习在医疗保健中的应用
背景情况:
- 儿童尿路感染 (UTI) 的全球流行率增加,由扩展光谱β-乳酸酶 (ESBL) 生产细菌引起.
- ESBL-UTIs需要专门的抗生素治疗,往往导致延迟经验治疗,增加ICU入院,发病率和长时间住院.
- 预测ESBL-UTIs具有挑战性,但对于及时和适当的患者管理至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测由ESBL产生细菌引起的儿科尿路感染.
- 帮助儿科医生识别患ESBL阳性尿路感染风险较高的儿童.
- 为了使ESBL-UTIs更早地开始适当的经验性抗生素治疗.
主要方法:
- 从2010年1月到2020年8月,对儿科患者 (1个月至18岁) 确诊的尿路感染的电子病历进行了回顾性分析.
- 数据提取包括人口统计,临床信息和实验室结果.
- 开发五个ML模型,使用在尿路感染呈现时可用的患者数据来预测ESBL阳性细菌感染.
主要成果:
- 这项研究分析了35,830例儿科尿路感染事件.
- 与ESBL阳性尿路感染显著相关的因素包括年龄,性别,社会经济地位,感染地点,先前使用抗生素,先前的ESBL-UTI病史和特定的尿病原体.
- 开发的ML模型显示出高负预测值 (~0.98),表明在排除ESBL阳性尿道感染方面表现强.
结论:
- 使用在尿路感染呈现时可用的数据的机器学习模型可以帮助临床医生评估儿童产生ESBL的细菌性尿路感染的可能性.
- 这些模型在支持经验性抗生素选择的临床决策方面表现有前途.
- 需要进一步的前性研究来完善模型性能并评估它们对临床结果的影响.
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