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

Antimicrobial resistance is an escalating global health crisis, underscoring the urgent need for timely and targeted therapies to ensure effective clinical treatment. We developed a machine learning model based on metagenomic next-generation sequencing (mNGS) for rapid antimicrobial susceptibility prediction (mNGS-based AST), which was tailored to five ESKAPEE bacteria: IMPORTANCE: 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.