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1511. Utility of Clinical Scoring Models in Predicting Community Acquired Urinary Tract Infections with Extended-Spectrum β-Lactamase-Producing Escherichia coli in a General Hospital in Mexico City
Author(s) -
V Alvarez-Wyssmann,
Marco Villanueva Reza,
David Humberto Martínez-Oliva,
Paulo Castañeda
Publication year - 2018
Publication title -
open forum infectious diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.546
H-Index - 35
ISSN - 2328-8957
DOI - 10.1093/ofid/ofy210.1340
Subject(s) - medicine , receiver operating characteristic , urinary system , antibiotics , medical prescription , area under the curve , cutoff , logistic regression , microbiology and biotechnology , biology , physics , quantum mechanics , pharmacology
Background Urinary tract infections (UTIs) are among the most common causes for antibiotic prescription. The use of clinical scoring models in predicting infection with extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (E. coli) may help select an adequate empiric treatment. Methods This retrospective case–control study included all urine cultures with E. coli from symptomatic patients 18 years of age or more admitted to Medica Sur Hospital from December 2014 to 2016. Cases were ESBL producing cultures and controls non-ESBL. Demographic and clinical information was drawn from electronic file. Sensitivities and specificities were performed at various cutoffs and area under the receiver curve (ROC AUC) was determined for each of the two models studied. Results A total of 171 cases and 294 controls were included. Table 1 displays the statistically significant variables associated with ESBL in a multivariate regression model. ROC AUC in Figure 1 was 0.691 for Tumbarello and 0.670 for Duke. With a 2-point cutoff, sensitivity for Tumbarello was 71% and specificity 61%, for Duke 58% and 75%, increasing cutoff to 4 points increases specificity to 87 and 93%, decreasing sensibility to 35 and 20%, respectively. Table 2 classifies by type of UTI, shows the percentage of adequate initial antibiotic for ESBL, and the number of cases predicted by each model. Tumbarello’s model predicts all cases, while Duke’s model predicts most cases of cystitis and pyelonephritis and all cases of complicated UTI and urosepsis.Figure 1Table 1 Variable β-Coefficient P Confidence Interval 95% Recent antibiotic therapy 0.23 <0.001 0.16–0.35 Diabetes mellitus 0.17 <0.001 0.11–0.32 Previous hospitalization 0.16 <0.001 0.10–0.32 Connective tissue disease 0.11 0.014 0.06–0.48 Complicated UTI 0.11 0.017 0.02–0.19Table 2 Type of UTI/Initial Antibiotic ESBL E. coli Non-ESBL E. coli Tumbarello Duke Cystitis 62 118 87 60 Nitrofurantoin o fosfomycin 10% 5% Pyelonephritis 77 140 89 71 Carbapenem 58% 31% Complicated UTI 89 93 126 89 Carbapenem 56% 42% Urosepsis 40 40 64 45 Carbapenem 65% 78% Conclusion Clinical scoring models have a high specificity identifying best non-ESBL infections, this aids in the choice of a more adequate empirical antibiotic for community-acquired UTI. Disclosures All authors: No reported disclosures.

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