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BACKGROUND: Extended 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.METHODS: We used data collected from a cohort of 528 international travellers enrolled in a multicenter 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.RESULTS: A CPR using machine learning and logistic regression on ten 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 ten 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.CONCLUSIONS: We 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.