Marion Elligsen1,2, Ruxandra Pinto3, Jerome A Leis4,5, Sandra A N Walker1,2, Nick Daneman4,6, Derek R MacFadden7. 1. Department of Pharmacy, Sunnybrook Health Sciences Centre, Toronto, Canada. 2. Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Canada. 3. Department of Critical Care and Population Health, Sunnybrook Health Sciences Centre, Toronto, Canada. 4. Division of Infectious Diseases, University of Toronto, Toronto, Canada. 5. Centre of Quality Improvement and Patient Safety, Sunnybrook Health Sciences Centre, Toronto, Canada. 6. Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada. 7. Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada.
Abstract
BACKGROUND: Timely selection of adequate empiric antibiotics has become increasingly difficult due to rising resistance rates and the competing desire to apply antimicrobial stewardship (AMS) principles. Individualized clinical prediction models offer the promise of reducing broad-spectrum antibiotic use and preserving/improving adequacy of treatment, but few have been validated in the clinical setting. METHODS: Multivariable models were used to predict the probability of susceptibility for gram-negative (GN) bacteria in bloodstream infections (bacteremia) to ceftriaxone, ciprofloxacin, ceftazidime, piperacillin-tazobactam, and meropenem. The models were combined with existing resistance-prediction methods to generate optimized and individualized suggestions for empiric therapy that were provided to prescribers by an AMS pharmacist. De-escalation of empiric antibiotics and adequacy of therapy were analyzed using a quasi-experimental design comparing two 9-month periods (pre- and postintervention) at a large academic tertiary care institution. RESULTS: Episodes of bacteremia (n = 182) were identified in the preintervention and postintervention (n = 201) periods. Patients who received the intervention were more likely to have their therapy de-escalated (29 vs 21%; aOR = 1.77; 95% CI, 1.09-2.87; P = .02). The intervention also increased the proportion of patients who were on the narrowest adequate therapy at the time of culture finalization (44% in the control and 55% in the intervention group; aOR = 2.04; 95% CI, 1.27-3.27; P = .003). Time to adequate therapy was similar in the intervention and control groups (5 vs 4 hours; P = .95). CONCLUSIONS: An AMS intervention, based on individualized predictive models for resistance, can influence empiric antibiotic selections for GN bacteremia to facilitate early de-escalation of therapy without compromising adequacy of antibiotic coverage.
BACKGROUND: Timely selection of adequate empiric antibiotics has become increasingly difficult due to rising resistance rates and the competing desire to apply antimicrobial stewardship (AMS) principles. Individualized clinical prediction models offer the promise of reducing broad-spectrum antibiotic use and preserving/improving adequacy of treatment, but few have been validated in the clinical setting. METHODS: Multivariable models were used to predict the probability of susceptibility for gram-negative (GN) bacteria in bloodstream infections (bacteremia) to ceftriaxone, ciprofloxacin, ceftazidime, piperacillin-tazobactam, and meropenem. The models were combined with existing resistance-prediction methods to generate optimized and individualized suggestions for empiric therapy that were provided to prescribers by an AMS pharmacist. De-escalation of empiric antibiotics and adequacy of therapy were analyzed using a quasi-experimental design comparing two 9-month periods (pre- and postintervention) at a large academic tertiary care institution. RESULTS: Episodes of bacteremia (n = 182) were identified in the preintervention and postintervention (n = 201) periods. Patients who received the intervention were more likely to have their therapy de-escalated (29 vs 21%; aOR = 1.77; 95% CI, 1.09-2.87; P = .02). The intervention also increased the proportion of patients who were on the narrowest adequate therapy at the time of culture finalization (44% in the control and 55% in the intervention group; aOR = 2.04; 95% CI, 1.27-3.27; P = .003). Time to adequate therapy was similar in the intervention and control groups (5 vs 4 hours; P = .95). CONCLUSIONS: An AMS intervention, based on individualized predictive models for resistance, can influence empiric antibiotic selections for GN bacteremia to facilitate early de-escalation of therapy without compromising adequacy of antibiotic coverage.
Authors: Michael S Niederman; Rebecca M Baron; Lila Bouadma; Thierry Calandra; Nick Daneman; Jan DeWaele; Marin H Kollef; Jeffrey Lipman; Girish B Nair Journal: Crit Care Date: 2021-08-26 Impact factor: 9.097