在医院使用基因组,微生物学和患者移动性数据推断抗菌素耐药生物体的无症状载体
Sen Pei1, Dwayne Seeram2, Seth Blumberg3,4
1Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA. sp3449@cumc.columbia.edu.
Nature communications
|November 19, 2025
概括
识别抗微生物耐药生物体 (AMRO) 的无症状载体对于预防医疗相关感染 (HAI) 至关重要. 一个整合多种数据源的新框架准确地识别AMRO载体,并指导有针对性的干预措施,以减少病原体在医院中的传播.
科学领域:
- 流行病学 流行病学
- 传染性疾病 传染性疾病
- 计算生物学 计算生物学
背景情况:
- 抗微生物耐药生物体 (AMRO) 的无症状载体有助于医院的医疗相关感染.
- 现有的医院监控方法缺乏统一的框架来综合各种数据以识别AMRO载体.
研究的目的:
- 开发和验证一个推断框架,集成多式联运数据,以准确识别AMRO运营商.
- 评估不同数据源组合的有效性和推断导向干预措施的影响.
主要方法:
- 开发了一个新的推理框架,将机械传输模型与多式联络观测相结合.
- 利用临床培养,电子健康记录,患者流动性和基因组数据的数据.
- 通过模拟疫情验证了框架,并在现实医院环境中将其应用于抗卡巴胺的Klebsiella肺炎 (CRKP).
主要成果:
- 与单个数据源相比,多模式观测始终提高了识别AMRO运营商的准确性.
- 整合即使是稀疏的基因组数据与患者特征,也增强了CRKP载体的识别.
- 以推理为指导的有针对性的隔离策略显著降低了模拟中的AMRO负担.
结论:
- 使用多式联运数据的协同方法提高了AMRO运输风险估计.
- 开发的框架可以为改善和针对性干预提供信息,以减轻AMRO在医院中的传播.
- 这种方法为控制由AMROs引起的HAI提供了更有效的策略.
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