A Multi-Criteria Decision Analysis-based Framework for Assessing and Prioritizing Antimicrobial Resistance Risks from Metagenomic Datasets

Metagenomic sequencing has greatly expanded our ability to detect and characterize resistance genes across diverse environments. However, translating the metagenomic findings into actionable clinical insights remains challenging. We developed a web-based tool that applies a Multi-Criteria Decision Analysis (MCDA) framework to metagenomic AMR datasets. The tool integrates gene abundance with risk attributes derived from the WHO Bacterial Priority Pathogens List, including mortality, transmissibility, and treatability scores. Users can upload AMR detection results alongside customized scoring matrices to generate risk profiles at the samples, species, and drug class levels. Validation was performed using upper respiratory tract samples from SARS-CoV-2 patients and controls in central India, as well as a publicly available dataset from Tanzania. From 48 SARS-CoV-2 samples, 9 produced a total of 10 records involving Salmonella enterica, Escherichia coli, and Streptococcus pneumoniae across fluoroquinolone, cephalosporin, and macrolide classes (cumulative scores 4.2–146). In contrast, 3 of 48 control samples yielded 3 records, all linked to macrolide resistance in S. pneumoniae (scores 5.46–92.43). Analysis of 17 Tanzanian samples identified 2 records, with Klebsiella pneumoniae (cephalosporin resistance, score 70.2) and S. pneumoniae (macrolide resistance, score 670.02) emerging as priority risks. This framework bridges the gap between raw metagenomic data and clinically relevant risk assessment.