Novel Clinical mNGS-Based Machine Learning Model for Rapid Antimicrobial Susceptibility Testing of Acinetobacter baumannii.

ABSTRACT: Multidrug-resistant (MDR) bacteria are important public health problems. Antibiotic susceptibility testing (AST) currently uses time-consuming culture-based procedures, which cause treatment delays and increased mortality. We developed a machine learning model using Acinetobacter baumannii as an example to explore a fast AST approach using metagenomic next-generation sequencing (mNGS) data. The key genetic characteristics associated with antimicrobial resistance (AMR) were selected through a least absolute shrinkage and selection operator (LASSO) regression model based on 1,942 A. baumannii genomes. The mNGS-AST prediction model was accordingly established, validated, and optimized using read simulation sequences of clinical isolates. Clinical specimens were collected to evaluate the perfo

SUBMITTER: Hu X 

PROVIDER: S-EPMC10204632 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature