Exploring the Utility of MALDI-TOF Mass Spectrometry and Antimicrobial Resistance in Hospital Outbreak Detection

TL;DR

This study evaluates the effectiveness of MALDI-TOF mass spectrometry and antimicrobial resistance patterns, combined with machine learning, as rapid, cost-effective tools for hospital outbreak detection, potentially reducing dependence on whole genome sequencing.

Contribution

The paper introduces a machine learning framework that integrates MALDI-TOF spectra and antimicrobial resistance data for outbreak detection, offering a practical alternative to WGS in clinical settings.

Findings

MALDI-TOF and AR can sometimes effectively identify outbreak clusters.

Fusion of MALDI-TOF and AR improves detection accuracy.

Potential to enable faster, more accessible hospital outbreak surveillance.

Abstract

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.