Our lab tackles the growing threat of AMR with the power of AI and machine learning. Our recent work involves building a framework to analyze routinely collected AMR surveillance data. This will not only provide data-driven evidence of trends and associations but also illuminate its critical links to achieving the UN's Sustainable Development Goals, ultimately promoting global health and well-being.

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AMRSense is a One Health Data Ecosystem for solving the challenge of Antimicrobial Resistance. Brought to reality by a collaboration between IIIT-Delhi, ICMR, CHRI-PATH, and Tata, AMRSense closes the gaps in data and AI for evidence-based implementation of AMR surveillance.

Jasmine Kaur, Nittin Yadav, Tavpritesh Sethi, Harpreet Singh (ICMR), Tikesh Bisen (CHRI/PATH), Nikhil Doegar (TATA 1mg)

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Emerging trends in antimicrobial resistance in bloodstream infections: multicentric longitudinal study in India (2017–2022)

Background: Antimicrobial resistance (AMR) has escalated to pandemic levels, posing a significant global health threat. This study examines the patterns and trends of AMR in Bloodstream Infections (BSIs) across India, aiming to inform better surveillance and intervention strategies. Methods: Six-year data from 21 tertiary care centers in the Indian Council of Medical Research's AMR Surveillance Network (IAMRSN) were retrospectively analyzed to estimate cluster-robust trends in resistance. Time-series analysis was used to discern lead/lag relationships between antibiotic pairs and the directional influence of resistance in community and hospital-acquired BSIs(CA/HA BSIs). A data-driven Bayesian network ensemble averaged over 301 bootstrap samples was modelled to uncover systemic associations between AMR and Sustainable Development Goals (SDGs).Findings: Our findings indicate significant (p < 0.001) monthly increases in Imipenem and Meropenem resistance for Klebsiella, E. coli, and Acinetobacter BSIs. Importantly, Carbapenem resistance in HA-BSIs preceded that in CA-BSIs for Klebsiella and Acinetobacter (p < 0.05). At a national level, Cefotaxime resistance emerged as a potential early indicator for emerging Carbapenem resistance, proposing a novel surveillance marker. In Klebsiella BSIs, states with higher achievement of SDG3 goals showed lower Imipenem resistance. A model-based AMR scorecard is introduced for focused interventions and continuous monitoring.Interpretation: The identified spatiotemporal trends and drug resistance associations offer critical insights for AMR surveillance aligning with WHO GLASS standards.The escalation of carbapenem resistance in BSIs demands vigilant monitoring and may be crucial for achieving SDGs by 2030. Implementing the proposed framework for data-driven evidence can help nations achieve proactive AMR surveillance.

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Jasmine Kaur, Harpreet Singh, Tavpritesh Sethi

5-Year Trends and Associations of Antimicrobial Resistance in Urinary Tract Infections in India (2017-2021): A Multi-Centric, Retrospective, Longitudinal Analysis

Urinary Tract Infections (UTIs) affect nearly 150 million people worldwide annually. High antimicrobial resistance (AMR) is reported in India. However, no studies provide multicentric, nationwide data on trends and associations of AMR in UTIs. To our knowledge, this study presents the most comprehensive estimates of trends and associations of AMR across India.

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Jasmine Kaur, Aditya Nagori, Balaji Veeraraghavan, Vinod Ohri, Rakesh Lodha, Kamini Walia, Harpreet Singh, Tavpritesh Sethi

Stewarding antibiotic stewardship in intensive care units with Bayesian artificial intelligence

Emerging antimicrobial resistance (AMR) is a global threat to life. Injudicious use of antibiotics is the biggest driver of resistance evolution, creating selection pressures on micro-organisms. Intensive care units (ICUs) are the strongest contributors to this pressure, owing to high infection and antibiotic usage rates. Antimicrobial stewardship programs aim to control antibiotic use; however, these are mostly limited to descriptive statistics. Genomic analyses lie at the other extreme of the value-spectrum, and together these factors predispose to siloing of knowledge arising from AMR stewardship. In this study, we bridged the value-gap at a Pediatric ICU by creating Bayesian network (BN) artificial intelligence models with potential impacts on antibiotic stewardship. Methods, actionable insights and an interactive dashboard for BN analysis upon data observed over 3 years at the PICU are described. BNs have several desirable properties for reasoning from data, including interpretability, expert knowledge injection and quantitative inference. Our pipeline leverages best practices of enforcing statistical rigor through bootstrapping, ensemble averaging and Monte Carlo simulations. Competing, shared and independent drug resistances were discovered through the presence of network motifs in BNs. Inferences guided by these visual models are also discussed, such as increasing the sensitivity testing for chloramphenicol as a potential mechanism of avoiding ertapenem overuse in the PICU. Organism, tissue and temporal influences on drug co-resistances are also discussed. While the model represents inferences that are tailored to the site, BNs are excellent tools for building upon pre-learnt structures, hence the model and inferences were wrapped into an interactive dashboard not only deployed at the site, but also made openly available to the community via GitHub. Shared repositories of such models could be a viable alternative to raw-data sharing and could promote partnering, learning across sites and charting a joint course for antimicrobial stewardship programs in the race against AMR.

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Tavpritesh Sethi, Shubham Maheshwari, Aditya Nagori, Rakesh Lodha