Abstract
BACKGROUND: Antibiotic resistance is a growing problem in community-acquired urinary tract infections (CAUTI) leading to significant challenges and costs in the healthcare system. We aimed to propose a reliable and an easy-to-use clinical prediction model to identify patients with multidrug-resistant (MDR) uro-pathogens. METHODS: We conducted a retrospective study including 824 patients with documented CAUTI diagnosed at an infectious diseases department during 2010–2017. Logistic-regression-based prediction scores were calculated based on variables independently associated with MDR. Sensitivities and specificities at various point cutoffs were studied and the determination of area under the receiver operating characteristic curve (AUROC) was performed. RESULTS: The median age of 824 patients with documented CAUTI was 54 years (IQR = [33–72 years]) and 542 cases (65.8%) were females. MDR germs were found in 372 cases (45.1%). Multivariate analysis showed that age ≥ 70 years (Adjusted OR = 2.5; 95% CI = [1.8–3.5]), diabetes (adjusted OR = 1.65; 95% CI = [1.19–2.3]), history of urinary tract surgery in the last past 12 months (adjusted OR = 4.5; 95% CI = [1.22–17]) and previous antimicrobial therapy in the last past 3 months (adjusted OR = 4.6; 95% CI = [3–7]) were the independent risk factors of MDR in CAUTI. The results of Hosmer-Lemshow chi-squared testing (χ(2) = 3.4; P = 0.49) were indicative of good calibration of the model. At a cut-off of ≥2, the score had an AUROC of 0.71, a good sensitivity (70.5%) but a lower specificity (60%), a PPV of 60%, an NPV of 70% and an overall diagnostic accuracy of 65%. When the cutoff was raised to 6, the sensitivity dropped to 43% and the specificity increased to 85%. CONCLUSION: Our study provided an insight into the clinical predictors of MDR in CAUTI. We developed a novel scoring system that can reliably identify patients likely to be harboring MDR uro-pathogens on hospital admission. DISCLOSURES: All authors: No reported disclosures.