Decision-support models for empiric antibiotic selection in Gram-negative bloodstream infections.

OBJECTIVES: Early empiric antibiotic therapy in patients can improve clinical outcomes in Gram-negative bacteraemia. However, the widespread prevalence of antibiotic-resistant pathogens compromises our ability to provide adequate therapy while minimizing use of broad antibiotics. We sought to determine whether readily available electronic medical record data could be used to develop predictive models for decision support in Gram-negative bacteraemia. METHODS: We performed a multi-centre cohort study, in Canada and the USA, of hospitalized patients with Gram-negative bloodstream infection from April 2010 to March 2015. We analysed multivariable models for prediction of antibiotic susceptibility at two empiric windows: Gram-stain-guided and pathogen-guided treatment. Decision-support models for empiric antibiotic selection were developed based on three clinical decision thresholds of acceptable adequate coverage (80%, 90% and 95%). RESULTS: A total of 1832 patients with Gram-negative bacteraemia were evaluated. Multivariable models showed good discrimination across countries and at both Gram-stain-guided (12 models, areas under the curve (AUCs) 0.68-0.89, optimism-corrected AUCs 0.63-0.85) and pathogen-guided (12 models, AUCs 0.75-0.98, optimism-corrected AUCs 0.64-0.95) windows. Compared to antibiogram-guided therapy, decision-support models of antibiotic selection incorporating individual patient characteristics and prior culture results have the potential to increase use of narrower-spectrum antibiotics (in up to 78% of patients) while reducing inadequate therapy. CONCLUSIONS: Multivariable models using readily available epidemiologic factors can be used to predict antimicrobial susceptibility in infecting pathogens with reasonable discriminatory ability. Implementation of sequential predictive models for real-time individualized empiric antibiotic decision-making has the potential to both optimize adequate coverage for patients while minimizing overuse of broad-spectrum antibiotics, and therefore requires further prospective evaluation. SUMMARY: Readily available epidemiologic risk factors can be used to predict susceptibility of Gram-negative organisms among patients with bacteraemia, using automated decision-making models.

Utility of prior cultures in predicting antibiotic resistance of bloodstream infections due to Gram-negative pathogens: a multicentre observational cohort study.

OBJECTIVES: Appropriate empiric antibiotic therapy in patients with bloodstream infections due to Gram-negative pathogens can improve outcomes. We evaluated the utility of prior microbiologic results for guiding empiric treatment in Gram-negative bloodstream infections. METHODS: We conducted a multicentre observational cohort study in two large health systems in Canada and the United States, including 1832 hospitalized patients with Gram-negative bloodstream infection (community, hospital and intensive care unit acquired) from April 2010 to March 2015. RESULTS: Among 1832 patients with Gram-negative bloodstream infection, 28% (n = 504) of patients had a documented prior Gram-negative organism from a nonscreening culture within the previous 12 months. A most recent prior Gram-negative organism resistant to a given antibiotic was strongly predictive of the current organism's resistance to the same antibiotic. The overall specificity was 0.92 (95% confidence interval (CI) 0.91-0.93), and positive predictive value was 0.66 (95% CI 0.61-0.70) for predicting antibiotic resistance. Specificities and positive predictive values ranged from 0.77 to 0.98 and 0.43 to 0.78, respectively, across different antibiotics, organisms and patient subgroups. Increasing time between cultures was associated with a decrease in positive predictive value but not specificity. An heuristic based on a prior resistant Gram-negative pathogen could have been applied to one in four patients and in these patients would have changed therapy in one in five. CONCLUSIONS: In patients with a bloodstream infection with a Gram-negative organism, identification of a most recent prior Gram-negative organism resistant to a drug of interest (within the last 12 months) is highly specific for resistance and should preclude use of that antibiotic.