Prediction of A. Baumannii Amikacin Resistance in Clinical Metagenomics.

Respiratory tract infections are a serious threat to health, especially in the presence of antimicrobial resistance (AMR). Existing AMR detection methods are limited by slow turnaround times and low accuracy due to the presence of false positives and negatives. In this study, we simulate 1,116 clinical metagenomics samples on both Illumina and Nanopore sequencing from curated, real-world sequencing of A. baumannii respiratory infections and build AI models to predict resistance to amikacin. The best performance is achieved by XGBoost on Illumina sequencing (area under the ROC curve = 0.7993 on 5-fold cross-validation).

Citation

Michael Aaron Sy, Mattia Prosperi, Mohammadali Serajian, Christina Boucher, Panayotis Takis Benos, Simone Marini. Prediction of A. Baumannii Amikacin Resistance in Clinical Metagenomics. Studies in health technology and informatics. 2024 Aug 22;316:212-213

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PMID: 39176710

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