Personalised antimicrobial susceptibility testing with clinical prediction modelling informs appropriate antibiotic use

Alex Howard (), David M. Hughes, Peter L. Green, Anoop Velluva, Alessandro Gerada, Simon Maskell, Iain E. Buchan and William Hope
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Alex Howard: University of Liverpool
David M. Hughes: University of Liverpool
Peter L. Green: University of Liverpool
Anoop Velluva: University of Liverpool
Alessandro Gerada: University of Liverpool
Simon Maskell: University of Liverpool, The Quadrangle, Brownlow Hill
Iain E. Buchan: University of Liverpool
William Hope: University of Liverpool

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Antimicrobial susceptibility testing is a key weapon against antimicrobial resistance. Diagnostic microbiology laboratories use one-size-fits-all testing approaches that are often imprecise, inefficient, and inequitable. Here, we report a personalised approach that adapts laboratory testing for urinary tract infection to maximise the number of appropriate treatment options for each patient. We develop and assess susceptibility prediction models for 12 antibiotics on real-world healthcare data using an individual-level simulation study. When combined with decision thresholds that prioritise selection of World Health Organisation Access category antibiotics (those least likely to induce antimicrobial resistance), the personalised approach delivers more susceptible results (results that encourage prescription of that antibiotic) per specimen for Access category antibiotics than a standard testing approach, without compromising provision of susceptible results overall. Here, we show that personalised antimicrobial susceptibility testing could help tackle antimicrobial resistance by safely providing more Access category antibiotic treatment options to clinicians managing urinary tract infection.

Date: 2024
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DOI: 10.1038/s41467-024-54192-3

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