In Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
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 both 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 blood-stream 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 post-intervention) at a large academic tertiary care institution.
RESULTS : Episodes of bacteremia (n=182) were identified in the pre-intervention and post-intervention (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=0.02). 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 group and 55% in the intervention group; aOR=2.04, 95%CI 1.27-3.27, p=0.003). Time to adequate therapy was similar in the intervention and control groups (5 vs 4 hours; p=0.95).
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.
Elligsen Marion, Pinto Ruxandra, Leis Jerome A, Walker Sandra A N, Daneman Nick, MacFadden Derek R
anti-bacterial agents, antibiotic resistance, antimicrobial stewardship, clinical decision-making, machine learning