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

Background: 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. Methods: 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 receiver-operating-characteristic curve (AUROC) and Pearson correlation. Results: 145 patients were included for analysis, of which 20 (13.8%) were infected with an ESBL E. coli 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). Correlation between prediction model score and percent ESBL was strongest with Modified Hopkins (R2=0.74). Conclusions: 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.

Predicting Extended-Spectrum Beta-Lactamase and Carbapenem Resistance in Enterobacteriaceae Bacteremia: A Diagnostic Model Systematic Review and Meta-Analysis

“…One additional study was included from a conference proceeding. In total, data were extracted from 10 studies for analysis, with 6106 unique patient encounters [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ].…”

Optimization of Empirical Antimicrobial Therapy in Enterobacterales Bloodstream Infection Using the Extended-Spectrum Beta-Lactamase Prediction Score

“…This definition was made in priori several years before the MERINO trial results were published [ 10 ]. Finally, the ESBL prediction score is not expected to identify all patients with BSI due to ESBL-E as per the score’s previously published performance characteristics [ 13 , 14 ].…”

“…Early initiation of appropriate antimicrobial therapy has been associated with improved survival and shorter hospital length of stay in patients with Gram-negative BSI [ 11 , 12 ]. Several clinical prediction tools have been derived and validated to identify patients at high risk for BSI due to ESBL-E [ 13 ]. However, the utility of these clinical prediction tools is yet to be examined in clinical practice.…”

Antibiotic Prescriptions in Critically Ill Patients with Bloodstream Infection Due to ESBL-Producing Enterobacteriaceae: Compliance with the French Guidelines for the Treatment of Infections with Third-Generation Cephalosporin-Resistant Enterobacteriaceae—A Multicentric Retrospective Cohort Study

“…Several ESBL-E infection prediction scores have been validated. However, even the most accurate one has a moderate discriminatory ability, leading to an underestimation of the risk of ESBL-E infection [17]. Elligsen M et al created an algorithm to predict resistance that accounts for previous microbiological results.…”