Performance and hypothetical clinical impact of an mNGS-based machine learning model for antimicrobial susceptibility prediction of five ESKAPEE bacteria

Metagenomic next-generation sequencing (mNGS)-based antimicrobial susceptibility prediction (AST) is a novel method for predicting the antimicrobial susceptibility of ESKAPEE bacteria using a machine learning approach and short-read sequencing data. Assuming that mNGS-based AST results were obtained during clinical management, it could significantly reduce turnaround time while maintaining a high level of accuracy, allowing for earlier therapeutic adjustments for patients. Furthermore, mNGS-based AST can be integrated with clinical mNGS to maximize the utility of short-read data without substantial cost increases. This study demonstrates the potential of mNGS-based AST for precise, individualized antibacterial selection and highlights its broader applicability in enhancing clinical antimicrobial use for various infections.