Validation of Available Extended-Spectrum-Beta-Lactamase Clinical Scoring Models in Predicting Drug Resistance in Patients with Enteric Gram-Negative Bacteremia Treated at South Texas Veterans Health Care System.

Abstract

Extended-spectrum-beta-lactamase (ESBL)-producing Enterobacteriaceae are increasingly common; however, predicting which patients are likely to be infected with an ESBL pathogen is challenging, leading to increased use of carbapenems. To date, five prediction models have been developed to distinguish between patients infected with ESBL pathogens. The aim of this study was to validate and compare each of these models to better inform antimicrobial stewardship. This was a retrospective cohort study of patients with Gram-negative bacteremia treated at the South Texas Veterans Health Care System over 3 months from 2018 to 2019. We evaluated isolate, clinical syndrome, and score variables for the five published prediction models/scores: Italian "Tumbarello," Duke, University of South Carolina (USC), Hopkins clinical decision tree, and modified Hopkins. Each model was assessed using the area under the receiver operating characteristic curve (AUROC) and Pearson correlation. One hundred forty-five patients were included for analysis, of which 20 (13.8%) were infected with an ESBL Escherichiacoli or Klebsiella spp. The most common sources of infection were genitourinary (55.8%) and gastrointestinal/intraabdominal (24.1%), and the most common pathogen was E. coli (75.2%). The prediction model with the strongest discriminatory ability (AUROC) was Tumbarello (0.7556). The correlation between prediction model score and percent ESBL was strongest with the modified Hopkins model (R2 = 0.74). In this veteran population, the modified Hopkins and Duke prediction models were most accurate in discriminating between Gram-negative bacteremia patients when considering both AUROC and correlation. However, given the moderate discriminatory ability, many patients with ESBL Enterobacteriaceae (at least 25%) may still be missed empirically.