In Journal of travel medicine
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.
Brown D Garrett, Worby Colin J, Pender Melissa A, Brintz Ben J, Ryan Edward T, Sridhar Sushmita, Oliver Elizabeth, Harris Jason B, Turbett Sarah E, Rao Sowmya R, Earl Ashlee M, LaRocque Regina C, Leung Daniel T
2023-Mar-02
ESBL, antibiotic resistance, clinical prediction, international travel, machine learning
