Harnessing AI to generate patterns of antibiotic resistance in real time Premium

IIIT-Delhi researchers develop AI tools for real-time antibiotic resistance patterns, enabling timely interventions at global, national, and hospital levels.

A team of researchers from IIIT- Delhi have come up with AI-powered data integration and predictive analytics tools, to understand the patterns of antibiotic resistance in real time, enabling various agencies to act on them speedily.  

As part of a collaboration between Indraprastha Institute of Information Technology-Delhi, CHRI-PATH, Tata 1mg, and Indian Council of Medical Research scientists, the AI-driven tool AMRSense has been deployed to use routine data that is generated in hospitals to generate accurate and early insights on antimicrobial resistance counched in the global level, national level and hospital level. 

In a paper,‘Emerging trends in antimicrobial resistance in bloodstream infections: multicentric longitudinal study in India’, published in The Lancet Regional Health - Southeast Asia, authors, Jasmine Kaur, Harpreet Singh, and Tavpritesh Sethi show results from analysing six-year data from 21 tertiary care centers in the Indian Council of Medical Research’s AMR surveillance network retrospectively, establishing relationships between antibiotic pairs and the directional influence of resistance in community and hospital-acquired infections.  

“There is a shared mechanism of resistance between antibiotics, we already know. Usually to do that, people use genomics, but that’s an expensive proposition,” explains Dr. Sethi. “We have proposed a way, which is inexpensive, because it uses these routine data sets from hospitals. We show that by using routine data effectively, we can discern relationships between different antibiotics pairs and the direction AMR is taking – whether it is rising or not. Say, for instance, if resistance to one specific antibiotic is going up, some months down the line, it is quite likely that resistance to an antibiotic pair might also shoot up. With these connections, we generated actionable pieces of evidence.” 

Dr. Sethi adds: “We have tried to go beyond the traditional way of looking at AI - asking how can it enable better decision-making for a given patient in a clinical setting or a public health setting. We think AI can also be used to understand AMR stewardship and surveillance aspects, from the hospital level, upwards. Hospitals already routinely send out patient isolates, for example, blood, sputum, urine, pus, etc., for culture sensitivity testing in order to make informed decisions on treatment courses. We are saying that these reports can be used to construct AI-based pipelines and methods that can lead to AI-driven or AI-enhanced antimicrobial stewardship.” 

The AMROrbit Scorecard that the team developed also won an award at the 2024 AMR Surveillance Data Challenge. Can we use these scorecards to make it more timely? Dr. Sethi explains: “It plots the orbit of resistance, say of every hospital or department, alongside a global median of resistance and a global rate of change. So around those global values, how well does a department, a hospital, or a certain country fare? That is what the scorecard will be able to provide real time data for.”  

The ideal quadrant for any hospital or country to be in is where there is low baseline resistance and low rate of change as well, explains Jasmine Kaur, of IIIT-D, and lead author of the paper. Orbits spiral in or out, but the AI tool can offer information facilitating timely interventions that can bring it to a desirable range of resistance.