Performance of an Easy and Simple New Scoring Model in Predicting Multidrug-Resistant Enterobacteriaceae in Community-Acquired Urinary Tract Infections

Abstract Background Multidrug resistance (MDR) is a growing global problem in bacterial community-acquired urinary tract infections (CUTIs). We aimed to propose an easy-to-use clinical prediction model to identify patients with MDR in CUTI. Methods We conducted a retrospective study including 770 patients with documented CUTI diagnosed during 2010–2017. Logistic regression–based prediction scores were calculated based on variables independently associated with MDR. Sensitivities and specificities at various cutoff points were determined, and the area under the receiver operating characteristic curve (AUROC) was computed. Results We found MDR Enterobacteriaceae in 372 cases (45.1%). Multivariate analysis showed that age ≥70 years (adjusted odds ratio [aOR], 2.5; 95% confidence interval [CI], 1.8–3.5), diabetes mellitus (aOR, 1.65; 95% CI, 1.19–2.3), history of urinary tract surgery in the last 12 months (aOR, 4.5; 95% CI, 1.22–17), and previous antimicrobial therapy in the last 3 months (aOR, 4.6; 95% CI, 3–7) were independent risk factors of MDR in CUTI. The results of Hosmer-Lemshow chi-square testing were indicative of good calibration of the model (χ2 = 3.4; P = .49). At a cutoff of ≥2, the score had an AUROC of 0.71, a sensitivity of 70.5%, a specificity of 60%, a positive predictive value of 60%, a negative predictive value of 70%, and an overall diagnostic accuracy of 65%. When the cutoff was raised to 6, the sensitivity dropped (43%), and the specificity increased appreciably (85%). Conclusions We developed a novel scoring system that can reliably identify patients likely to be harboring MDR in CUTI.