Predicting Drug-Resistant Gram-Negative Infections in Hospitalized Adults

Researchers have developed a clinical prediction tool to estimate the probabilities of third-generation cephalosporin-resistant Enterobacteriaceae, carbapenem-resistant Enterobacteriaceae, and multidrug-resistant Pseudomonas aeruginosa.

Researchers have developed a clinical prediction tool to estimate the probability of third-generation cephalosporin-resistant Enterobacteriaceae, carbapenem-resistant Enterobacteriaceae (CRE), and multidrug-resistant (MDR) Pseudomonas aeruginosa occurring in hospitalized adult patients with confirmed community- and hospital-acquired gram-negative infections, according to study results published in BMC Infectious Diseases. Moreover, this prediction tool can be used as a user-friendly clinical instrument for use at the bedside.

Because definitive culture results are typically not available within the first 24 to 72 hours of infection onset, it is important that clinicians promptly identify patients at risk of being infected with resistant pathogens, to ensure that these patients receive early appropriate therapy. For this reason, researchers developed a clinical prediction tool using the Premier Hospital database, the largest hospital-based database in the United States. This retrospective observational study included 124,068 hospital admissions from approximately 160 institutions that contributed microbiology data during the period January 1, 2011 to October 1, 2015.

Potential predictors in the models included infection-, patient-, and hospital-level characteristics. An infection was considered hospital-acquired if the patient had an index culture date ≥3 days after hospital admission; it was considered community-acquired if an index culture date was <3 days after hospital admission. The researchers developed 6 models: 3 each for community-acquired and hospital-acquired infections with third-generation cephalosporin-resistant Enterobacteriaceae, CRE, and MDR P aeruginosa.

Of the 124,068 patients included in the analysis, 61.5% were admitted for complicated urinary tract infection, 26.4% for bloodstream infection, 16.5% for hospital-acquired/ventilator-associated pneumonia, and 4.6% for complicated intra-abdominal infection with some patients having more than 1 infection type.

The percentage of resistant infection was 12.09% for third-generation cephalosporin-resistant Enterobacteriaceae, 1.90% for CRE, and 3.91% for MDR P aeruginosa. Within each microbial category, a greater percentage of resistant infections were community-acquired compared with hospital-acquired (third-generation cephalosporin-resistant Enterobacteriaceae, 9.27% vs 3.42%, CRE, 1.30% vs 0.62%; MDR P aeruginosa, 2.39% vs 1.59%).

The most important predictors across most of the 6 models were number of prior antibiotics received, infection site, infection during the previous 3 months, and hospital prevalence of the resistant pathogen.

In order to apply the 6 predictive multivariate logistic regression models to real-world clinical practice, researchers developed a user-friendly interface that estimates the risk for resistant infection. This tool will allow clinicians to “make more informed empiric antibiotic selection decisions and thereby increase the likelihood of appropriate empiric antibiotic therapy,” noted the researchers.

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Researchers noted that the tool does not make specific recommendations regarding treatment options. In addition, they stressed that a validation study should be performed using an external database.

Disclosure: This study was supported by Allergan plc. Please see the original reference for a full list of authors’ disclosures.


Lodise TP, Bonine NG, Ye JM, Folse HJ, Gillard P. Development of a bedside tool to predict the probability of drug-resistant pathogens among hospitalized adult patients with gram-negative infections. BMC Infect Dis. 2019;19(1):718.