A Clinical Prediction Tool for Extended-Spectrum Cephalosporin Resistance in Community-Onset Enterobacterales Urinary Tract Infection

Background 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). Methods 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. Results A total of 302 patients with a community-onset EB UTI were included, with 151 cases and 151 controls. After multivariable analysis, we found that presentation with an ESC-R EB community-onset UTI could be predicted by the following: (1) a history of malignancy; (2) a history of diabetes; (3) recent skilled nursing facility or hospital stay; (4) recent trimethoprim-sulfamethoxazole exposure; and (5) pyelonephritis at the time of presentation (AUC 0.73, Hosmer-Lemeshow goodness-of-fit P value 0.23). With this model, each covariate confers a single point, and a patient with ≥ 2 points is considered high risk for ESC-R EB (sensitivity 80%, specificity 54%). The adjusted AUC after bootstrapping was 0.71. Conclusions Community-onset ESC-R EB UTI can be predicted using the proposed scoring system, which can help guide diagnostic and therapeutic interventions.
Bacterial resistance continues to increase in the community. We found that community-onset urinary tract infections due to extended-spectrum cephalosporin-resistant Enterobacterales (formerly Enterobacteriaceae) could be predicted with knowledge of 5 clinical factors.