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Seeking patterns of antibiotic resistance in ATLAS, an open, raw MIC database with patient metadata
Pablo Catalán1,2, Emily Wood3, Jessica M A Blair4
1Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4QD, UK. pcatalan@math.uc3m.es.
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.
Insights
Antibiotic resistance surveillance using clinical data reveals complex resistance patterns and predicts future trends. Analysis of the ATLAS database identified data anomalies and pathogens at risk of developing clinical resistance.
Area of Science:
- Microbiology
- Infectious Diseases
- Data Science
Background:
- Antibiotic resistance is a significant global health threat.
- Clinical datasets are underutilized for tracking antibiotic resistance.
- The Antimicrobial Testing Leadership and Surveillance (ATLAS) database contains extensive antimicrobial susceptibility data.
Purpose of the Study:
- To analyze patterns of antibiotic resistance within the ATLAS database.
- To identify data anomalies and assess their impact.
- To predict future trends in antibiotic resistance.
Main Methods:
- Utilized 6.5 million minimal inhibitory concentrations (MICs) from the ATLAS database (2004-2017).
- Analyzed 3,919 pathogen-antibiotic pairs across 70 countries.
- Employed an information-optimal clustering methodology to classify pathogen susceptibility.
- Identified and analyzed data anomalies in MICs.
Main Results:
- Most pathogen-antibiotic pairs showed coherent, albeit non-stationary, resistance timeseries.
- ATLAS data revealed higher resistance frequencies compared to other databases without systematic bias.
- Data anomalies were detected in over 100 pathogen-antibiotic pairs.
- Complex dynamics, including MIC increases and divergent subpopulations, were observed in high-resistance groups.
- Specific pathogens at risk of future clinical resistance were identified.
Conclusions:
- The ATLAS database provides valuable insights into antibiotic resistance trends.
- Methodological artifacts can impact resistance data, necessitating careful analysis.
- Predictive modeling using susceptibility classification can forecast emerging resistance patterns.
- Identifying at-risk pathogens is crucial for proactive antimicrobial stewardship.
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