用质谱和耐药性数据快速识别医院感染暴发，减少对基因测序依赖。
Exploring the Utility of MALDI-TOF Mass Spectrometry and Antimicrobial Resistance in Hospital Outbreak Detection
- 融合质谱图谱与耐药模式，用机器学习提取爆发特征
- 多菌种分析显示部分场景可替代全基因组测序
- 适合资源有限的临床实验室做快速感染监测
准确及时地识别医院感染暴发集群对于防止具流行潜力的感染扩散至关重要。尽管全基因组测序（WGS）被视为暴发检测的金标准，但其高昂成本和较长周转时间限制了临床实验室的常规应用。本文探索了两种快速且成本低廉的替代方案：基质辅助激光解吸电离-飞行时间质谱（MALDI-TOF MS）和抗菌药物耐药性（AR）谱型。我们构建了一个机器学习框架，从MALDI-TOF谱图和耐药模式中提取有信息量的表示用于暴发检测，并研究二者融合效果。在多物种分析中，结果表明在某些情况下MALDI-TOF与耐药性数据具备降低对WGS依赖的潜力，从而实现更可及、更快速的暴发监测。
原文摘要 · Abstract (English)
Accurate and timely identification of hospital outbreak clusters is crucial for preventing the spread of infections that have epidemic potential. While assessing pathogen similarity through whole genome sequencing (WGS) is considered the gold standard for outbreak detection, its high cost and lengthy turnaround time preclude routine implementation in clinical laboratories. We explore the utility of two rapid and cost-effective alternatives to WGS, matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry and antimicrobial resistance (AR) patterns. We develop a machine learning framework that extracts informative representations from MALDI-TOF spectra and AR patterns for outbreak detection and explore their fusion. Through multi-species analyses, we demonstrate that in some cases MALDI-TOF and AR have the potential to reduce reliance on WGS, enabling more accessible and rapid outbreak surveillance.
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