Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests

• Published in: PeerJ (San Francisco, CA) CA), 2024-10, Vol.12, p.e18213, Article e18213
• Main Authors: Ardila, Carlos M, Yadalam, Pradeep K, González-Arroyave, Daniel
• Format: Article
• Language: English
• Subjects: Analysis; Anti-Bacterial Agents - pharmacology; Anti-Bacterial Agents - therapeutic use; Antimicrobial resistance; Bacteria; Bacteria - drug effects; Bacteria - genetics; Computational Biology; Data Mining and Machine Learning; DNA sequencing; Drug Resistance, Bacterial - genetics; Forecasts and trends; Genomes; Genomics; Humans; Infectious Diseases; Machine Learning; Methicillin; Microbial Sensitivity Tests; Microbiology; Nucleotide sequencing; Pathogenic microorganisms; Prediction models; Risk score; Whole Genome Sequencing
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.18213

Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the efficacy of machine learning (ML) approaches in predicting antimicrobial resistance (AMR) in critical pathogens (CP), considering Whole Genome Sequencing (WGS) and antimicrobial susceptibility testing (AST). The search covered databases including PubMed/MEDLINE, EMBASE, Web of Science, SCOPUS, and SCIELO, from their inception until June 2024. The review protocol was officially registered on PROSPERO (CRD42024543099). The review included 26 papers, analyzing data from 104,141 microbial samples. Random Forest (RF), XGBoost, and logistic regression (LR) emerged as the top-performing models, with mean Area Under the Receiver Operating Characteristic (AUC) values of 0.89, 0.87, and 0.87, respectively. RF showed superior performance with AUC values ranging from 0.66 to 0.97, while XGBoost and LR showed similar performance with AUC values ranging from 0.83 to 0.91 and 0.76 to 0.96, respectively. Most studies indicate that integrating WGS and AST data into ML models enhances predictive performance, improves antibiotic stewardship, and provides valuable clinical decision support. ML shows significant promise for predicting AMR by integrating WGS and AST data in CP. Standardized guidelines are needed to ensure consistency in future research.