ArticleNature communications2022
Seeking patterns of antibiotic resistance in ATLAS, an open, raw MIC database with patient metadata.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Therapeutic target database 2026: facilitating targeted therapies and precision medicine.Nucleic acids research · 2026Article
- Wild-type MIC distribution and evaluation of epidemiological cut-offs of second-line TB-drugs in susceptible and MDR-TB clinical isolates from Chennai, India.Frontiers in microbiology · 2026Article
- Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning.Scientific reports · 2025Article
- Recent Advances in Antimicrobial Resistance: Insights fromMicroorganisms · 2024Review
- Applications of Machine Learning on Electronic Health Record Data to Combat Antibiotic Resistance.The Journal of infectious diseases · 2024Review
- Challenges for global antibiotic regimen planning and establishing antimicrobial resistance targets: implications for the WHO Essential Medicines List and AWaRe antibiotic book dosing.Clinical microbiology reviews · 2024Review
- β-lactamase expression induces collateral sensitivity in Escherichia coli.Nature communications · 2024Article
- MIC distribution analysis identifies differences in AMR between population sub-groups.Wellcome open research · 2024Article
- Empowering Global AMR Research Community: Interactive GIS dashboards for AMR data analysis and informed decision-making.Wellcome open research · 2024Article
- Investigating the feasibility and potential of combining industry AMR monitoring systems: a comparison with WHO GLASS.Wellcome open research · 2024Article
- Article
- Ribosome-binding antibiotics increase bacterial longevity and growth efficiency.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Antibiotic resistance represents a growing medical concern where raw, clinical datasets are under-exploited as a means to track the scale of the problem. We therefore sought patterns of antibiotic resistance in the Antimicrobial Testing Leadership and Surveillance (ATLAS) database. ATLAS holds 6.5M minimal inhibitory concentrations (MICs) for 3,919 pathogen-antibiotic pairs isolated from 633k patients in 70 countries between 2004 and 2017. We show most pairs form coherent, although not stationary, timeseries whose frequencies of resistance are higher than other databases, although we identified no systematic bias towards including more resistant strains in ATLAS. We sought data anomalies whereby MICs could shift for methodological and not clinical or microbiological reasons and found artefacts in over 100 pathogen-antibiotic pairs. Using an information-optimal clustering methodology to classify pathogens into low and high antibiotic susceptibilities, we used ATLAS to predict changes in resistance. Dynamics of the latter exhibit complex patterns with MIC increases, and some decreases, whereby subpopulations' MICs can diverge. We also identify pathogens at risk of developing clinical resistance in the near future.
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