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Prediction Score for Identification of ESBL Producers in Urinary Infections Overestimates Risk in
Jorge Alberto Cortés1,2, Julián Antonio Niño-Godoy1, Heidi Johanna Muñoz-Latorre3
1Department of Internal Medicine, School of Medicine, Universidad Nacional de Colombia, Sede Bogotá, Bogotá 111321, Colombia.
Abstract:
Background/Objectives: Urinary tract infections (UTIs) caused by extended-spectrum beta-lactamase (ESBL) Enterobacterales have become more frequent. Therefore, strategies for assessing the risks posed by ESBL-producing infections have been developed, creating the need for local validation. The aim of this study was to validate the scoring system designed by Tumbarello et al. to identify ESBL producers in patients with a UTI that require hospital care in a region with a high prevalence of ESBL Escherichia coli. Methods: A retrospective cohort study was conducted in a third-level hospital in Bogotá (Colombia) between 2013 and 2020.The study included 817 patients, who were hospitalized due to pyelonephritis and treated with cefuroxime (the first-line antibiotic according to local guidelines), with an ESBL frequency of 9.68%. Diagnostic performances were estimated for a modified version of Tumbarello's score (omitting admission from another healthcare facility) evaluating the area under the curve (AUC) for ESBL presence with respect to resistance to second- and third-generation cephalosporins. Results: With an index cut-off of ≥6, the score showed a sensitivity of 18% and a specificity of 83%. The AUC for this cut-off was 0.47. This threshold index could not efficiently predict either third- (AUC = 0.49) or second-generation cephalosporin resistance (AUC = 0.51). Conclusions: In Colombia, a region with a high prevalence of ESBL E. coli producers, as the use of the Tumbarello index would result in excessive utilization of wide-spectrum antibiotics, it is not recommended in this specific scenario for UTIs. Further studies are required in order to develop accurate tools to assess the risk of ESBL producers in high-prevalence settings.
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