A clinical prediction tool for extended-spectrum β-lactamase-producing Enterobacteriaceae urinary tract infection

Abstract

Abstract Background Prevalence of extended-spectrum beta-lactamase-producing-Enterobacteriaceae (ESBL-E) has risen in patients with urinary tract infections. The objective of this study was to determine explore the risk factors of ESBL-E infection in hospitalized patients and establish a predictive model. Methods This retrospective study included all patients with an Enterobacteriaceae-positive urine sample at the first affiliated hospital of Jinan university from January 2018 to December 2019. Antimicrobial susceptibility patterns of ESBL-E were analyzed, and multivariate analysis of related factors was performed. From these, a nomogram was established to predict the possibility of ESBL-E infection. Simultaneously, susceptibility testing of a broad array of carbapenem antibiotics was performed on ESBL-E cultures to explore possible alternative treatment options. Results Of the total 874 patients with urinary tract infections (UTIs), 272 (31.1%) were ESBL-E positive. In the predictive analysis, five variables were identified as independent risk factors for ESBL-E infection: male gender (OR = 1.607, 95% CI 1.066–2.416), older age (OR = 4.100, 95% CI 1.678–12.343), a hospital stay in preceding 3 months (OR = 1.872, 95% CI 1.141–3.067), invasive urological procedure (OR = 1.810, 95% CI 1.197–2.729), and antibiotic use within the previous 3 months (OR = 1.833, 95% CI 1.055–3.188). In multivariate analysis, the data set was divided into a training set of 611 patients and a validation set of 263 patients The model developed to predict ESBL-E infection was effective, with the AuROC of 0.650 (95% CI 0.577–0.725). Among the antibiotics tested, several showed very high effectiveness against ESBL-E: amikacin (85.7%), carbapenems (83.8%), tigecycline (97.1%) and polymyxin (98.2%). Conclusions The nomogram is useful for estimating a UTI patient’s likelihood of infection with ESBL-E. It could improve clinical decision making and enable more efficient empirical treatment. Empirical treatment may be informed by the results of the antibiotic susceptibility testing.