AMROrbit: a trajectory-based scorecard for antimicrobial stewardship using routine susceptibility testing data.

Authors

Jasmine Kaur, Noel Abraham Tiju, Rishi Pendyala, Muthuraj Vairamuthu, Aryan Gupta, Tavpritesh Sethi

Published in

JAC-antimicrobial resistance. Volume 8. Issue 4. Pages dlag116. Epub Jul 22, 2026.

Abstract

Antimicrobial resistance (AMR) is a serious threat to global health. However, current stewardship approaches rely on static antibiograms that overlook temporal changes in resistance. We developed AMROrbit, an explainable trajectory-based scorecard to proactively identify emerging AMR patterns using routinely generated antimicrobial susceptibility data.
As a proof of concept, AMROrbit was applied to the Pfizer ATLAS dataset, including ESKAPE pathogens and Escherichia coli across four samples. For each antibiotic-microorganism-sample-country combination, yearly resistance percentages were modelled in rolling windows to generate estimates of resistance amplitude (baseline resistance) and velocity (rate of change). The estimates were plotted in a 2D space stratified using global medians into four quadrants. Q1 (ideal; low amplitude, low velocity), Q2 (high, low), Q3 (low, high) and Q4 (concerning; high, high). Temporal trajectories were classified as containment or spiral-in (moving towards Q1), persistent (stable within quadrant) or spiral-out (moving towards Q4).
Analysis revealed 62.4% of the combinations demonstrate increasing baseline resistance. Among the 5920 country-level trajectories, 17.7% depicted spiralling-out patterns, compared with only 13.4% spiralling-in, suggesting a concerning global imbalance. The patterns differed across sample-microorganism-antibiotic combinations, highlighting heterogeneity in trajectories across settings. An open-access dashboard and analytical suite are provided to generate trajectory-based stewardship insights from routine antimicrobial susceptibility testing datasets (https://amrorbit.tavlab.iiitd.edu.in:3002/).
AMROrbit extends conventional surveillance by jointly characterizing resistance levels and their rate of change within an interpretable trajectory-based framework. This allows emerging resistance patterns to be identified earlier and may support more timely stewardship responses. Its reliance on routinely collected microbiology data supports scalability across diverse health system settings.

PMID:
42488786
Bibliographic data and abstract were imported from PubMed on 23 Jul 2026.

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