Machine learning-based prediction of multi-level antimicrobial resistance in Klebsiella pneumoniae using whole-genome sequencing data.

OBJECTIVES

In this study, we developed an integrated approach for accurate and comprehensive prediction of anti-microbial resistance (AMR) using whole-genome sequencing (WGS) data.

METHODS

We developed machine learning (ML) models using WGS data from 5239 strains, spanning three independent, geographically and temporally diverse cohorts. The models were designed for multi-level AMR prediction, including resistant/susceptible (R/S), resistant/intermediate/susceptible (R/I/S), and high/low-level resistant (H/L) status against 11 antibiotics. We utilized nine ML algorithms and three anti-microbial susceptibility testing interpretation standards (CLSI, EUCAST, ECOFF), with robust five-fold cross-validation.

RESULTS

The model for distinguishing R/S categories exhibited excellent discrimination capability with area under the receiver operating characteristic curve (AUC) for all 11 antibiotics > 0.9 and a mean categorical agreement (CA) of 0.96. It also demonstrated robust performance across diverse regions (including Europe, the Americas, and Asia), sequence types, isolation sources, and over a long time span (2004-2022). We further developed models to simultaneously predict resistance (R), intermediate (I), and susceptible (S), as well as high and low levels of antibiotic resistance, achieving a mean AUC and CA > 0.9. The mean very major error, major error, and minor error for R/I/S models were 0.013, 0.015, and 0.024, respectively, indicating great promise for clinical application.

CONCLUSIONS

MLWA, our proposed model, leverages the largest dataset to date to enable accurate, multi-level AMR predictions that are generalizable across regions and stable over time. This ML-WGS approach enhances genotype-to-phenotype interpretation and facilitates precise and rapid anti-microbial selection, representing a significant step forward in managing AMR.