Exploring the Utility of MALDI-TOF Mass Spectrometry and Antimicrobial Resistance in Hospital Outbreak Detection
This work addresses the need for more accessible and rapid outbreak surveillance in clinical settings, though it appears incremental as it builds on existing methods.
The study tackled the problem of high cost and slow turnaround time of whole genome sequencing for hospital outbreak detection by exploring rapid alternatives like MALDI-TOF mass spectrometry and antimicrobial resistance patterns, developing a machine learning framework that showed potential to reduce reliance on WGS in some cases.
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.