• J Travel Med · Oct 2023

    Development of a prediction model for the Acquisition of Extended Spectrum Beta-Lactam Resistant Organisms in U.S. international travellers.

    • David Garrett Brown, Colin J Worby, Melissa A Pender, Ben J Brintz, Edward T Ryan, Sushmita Sridhar, Elizabeth Oliver, Jason B Harris, Sarah E Turbett, Sowmya R Rao, Ashlee M Earl, Regina C LaRocque, and Daniel T Leung.
    • Division of Infectious Diseases, University of Utah School of Medicine, Salt Lake City, UT, USA.
    • J Travel Med. 2023 Oct 31; 30 (6).

    BackgroundExtended spectrum beta-lactamase producing Enterobacterales (ESBL-PE) present a risk to public health by limiting the efficacy of multiple classes of beta-lactam antibiotics against infection. International travellers may acquire these organisms and identifying individuals at high risk of acquisition could help inform clinical treatment or prevention strategies.MethodsWe used data collected from a cohort of 528 international travellers enrolled in a multicentre US-based study to derive a clinical prediction rule (CPR) to identify travellers who developed ESBL-PE colonization, defined as those with new ESBL positivity in stool upon return to the United States. To select candidate features, we used data collected from pre-travel and post-travel questionnaires, alongside destination-specific data from external sources. We utilized LASSO regression for feature selection, followed by random forest or logistic regression modelling, to derive a CPR for ESBL acquisition.ResultsA CPR using machine learning and logistic regression on 10 features has an internally cross-validated area under the receiver operating characteristic curve (cvAUC) of 0.70 (95% confidence interval 0.69-0.71). We also demonstrate that a four-feature model performs similarly to the 10-feature model, with a cvAUC of 0.68 (95% confidence interval 0.67-0.69). This model uses traveller's diarrhoea, and antibiotics as treatment, destination country waste management rankings and destination regional probabilities as predictors.ConclusionsWe demonstrate that by integrating traveller characteristics with destination-specific data, we could derive a CPR to identify those at highest risk of acquiring ESBL-PE during international travel.© International Society of Travel Medicine 2023. Published by Oxford University Press.

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