Identifying critically ill patients at risk for inappropriate antibiotic therapy: A pilot study of a point-of-care decision support alert

Objective: To develop an automated alert aimed at reducing inappropriate antibiotic therapy of serious healthcare-associated infections. Design: Single-center cohort study from November 2011 to November 2012. Setting: Barnes-Jewish Hospital (1,250-bed academic hospital). Patients: A total of 3,616 critically ill patients receiving treatment with antibiotics targeting healthcare-associated infections due to Gram-negative bacteria. Interventions: Upon antibiotic order entry in the ICU for a Gram-negative antibiotic, the antibiotic and microbiologic history for each patient was electronically queried in real time across all 13 BJC HealthCare hospitals. Patients were assigned to the alert group if they had exposure to the same antibiotic class currently being prescribed (cefepime, meropenem, or piperacillin-tazobactam) or had a positive culture isolating a Gram-negative organism with resistance to the prescribed antibiotic in the previous 6 months. Measurements and main results: Nine hundred patients (24.2%) generated an alert. Alerted patients were significantly more likely to receive inappropriate antibiotic therapy (7.1% vs 2.9%; p < 0.001). Based on clinical information available in the alert, 34 of 64 of the alerted patients that received inappropriate therapy (53.1%) could have received an alternative β-lactam antibiotic with in vitro susceptibility to the identified pathogen. Independent predictors (adjusted odds ratio [95% CI]) of inappropriate therapy included alert generation (1.788 [1.167-2.740]; p = 0.008), medical ICU patients (1.528 [1.007-2.319]; p = 0.046), and a pulmonary source of infection (2.063 [1.363-3.122]; p = 0.0001). Patients in the alert group had significantly greater hospital mortality (29.9% vs 23.6%; p < 0.001) and hospital length of stay (median, 13.1 vs 10.7 d; p < 0.001) compared with nonalert patients. Conclusions: Our results suggest that a simple automated alert could identify more than 40% of critically ill patients prescribed inappropriate antibiotic therapy for healthcare-associated infections. These data suggest that an opportunity exists to employ hospital informatics systems to improve the prescription of antibiotic therapy in ICU patients with suspected healthcare-associated infections.