Validation of the prediction rules identifying drug-resistant pathogens in community-onset pneumonia

Abstract

Background: Appropriate initial antibiotic treatment and avoiding administration of unnecessary broad-spectrum antibiotics are important for the treatment of pneumonia. To achieve this, assessment of risk for drug-resistant pathogens (DRPs) at diagnosis is essential. Purpose: The aim of this study was to validate a predictive rule for DRPs that we previously proposed (the community-acquired pneumonia drug-resistant pathogen [CAP-DRP] rule), comparing several other predictive methods. Patients and methods: A prospective observational study was conducted in hospitalized patients with community-onset pneumonia at four institutions in Japan. Pathogens identified as not susceptible to ceftriaxone, ampicillin–sulbactam, macrolides, and respiratory fluoroquinolones were defined as CAP-DRPs. Results: CAP-DRPs were identified in 73 (10.1%) of 721 patients analyzed. The CAP-DRP rule differentiated low vs high risk of CAP-DRP at the threshold of ≥3 points or 2 points plus any of methicillin-resistant Staphylococcus aureus specific factors with a sensitivity of 0.45, specificity of 0.87, positive predictive value of 0.47, negative predictive value of 0.87, and accuracy of 0.79. Its discrimination performance, area under the receiver operating characteristic curve, was 0.73 (95% confidence interval 0.66–0.79). Specificity of the CAP-DRP rule against CAP-DRPs was the highest among the six predictive rules tested. Conclusion: The performance of the predictive rules and criteria for CAP-DRPs was limited. However, the CAP-DRP rule yielded high specificity and could specify patients who should be treated with non-broad-spectrum antibiotics, eg, a non-pseudomonal β-lactam plus a macrolide, more precisely. Keywords: antibiotic resistance, algorithms, community-acquired pneumonia, healthcare-associated pneumonia