A Comparison of Different Strategies for Optimizing the Selection of Empiric Antibiotic Therapy for Pneumonia Caused by Gram-Negative Bacteria in Intensive Care Units: Unit-Specific Combination Antibiograms Versus Patient-Specific Risk Factors.

Background: Guidelines suggest dual antipseudomonal therapy for empiric treatment of pneumonia caused by gram-negative bacteria in intensive care unit (ICU) patients. Additionally, consideration of local susceptibility data and patient-specific risk factors for resistance is recommended for selecting optimal empiric regimens. However, data assessing how to best do this are lacking, and it is unclear whether a local susceptibility data-based or a patient-specific risk factor-based approach will better drive appropriate empiric treatment. This study aims to compare these 2 strategies. Methods: This retrospective study was divided into 2 periods. In period I, gram-negative respiratory cultures from ICU patients were used to develop unit-specific combination antibiograms, and individual patient charts were reviewed to assess the impact of risk factors on antimicrobial susceptibility to develop a risk factor-based treatment algorithm. Optimal empiric regimens based on these 2 strategies were then defined. In period II, these regimens were hypothetically applied to patients to compare rates of appropriate empiric therapy and overuse by the 2 methods. Results: Risk factor-based regimens had a higher appropriateness rate compared to regimens derived from antibiograms (89.9% vs 83.7%). Additionally, applying antibiogram-based regimens resulted in a higher prevalence of antibiotic overuse than a patient-specific risk factor-based approach (69.8% vs 40.3%), with excess overuse driven by a higher frequency of unnecessary use of combination therapy. Conclusions: Both strategies provided high rates of appropriateness in empiric antibiotic selection. However, the patient-specific risk factor-based approach demonstrated a higher rate of appropriate therapy and offered advantages in reducing rates of unnecessary combination therapy.