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Bacterial resistance to first line antibiotics used to treat community-onset urinary tract infections (UTIs) continues to increase. We sought to create a clinical prediction tool for community-onset UTIs due to extended-spectrum cephalosporin-resistant (ESC-R) Enterobacterales (formerly Enterobacteriaceae, EB).

A case-control study was performed. The source population included patients presenting to an emergency department (ED) or outpatient practice with an EB UTI between 2010 and 2013. Case patients had ESC-R EB UTIs. Control patients had ESC-susceptible EB UTIs and were matched to cases 1:1 on study year. Multivariable conditional logistic regression was performed to develop the predictive model by maximizing the area under the receiver-operating curve (AUC). Internal validation was performed via bootstrapping.