Comparison of clinical prediction models for resistant bacteria in community-onset pneumonia.

Abstract

Six recently published algorithms classify pneumonia patients presenting from the community into high- and low-risk groups for resistant bacteria. Our objective was to compare performance of these algorithms for identifying patients infected with bacteria resistant to traditional community-acquired pneumonia antibiotics. This was a retrospective study of consecutive adult patients diagnosed with pneumonia in an emergency department and subsequently hospitalized. Each patient was classified as high or low risk for resistant bacteria according to the following algorithms: original health care-associated pneumonia (HCAP) criteria, Summit criteria, Brito and Niederman strategy, Shorr model, Aliberti model, and Shindo model. The reference for comparison was detection of resistant bacteria, defined as methicillin-resistant Staphylococcus aureus or Gram-negative bacteria resistant to ceftriaxone or levofloxacin. A total of 614 patients were studied, including 36 (5.9%) with resistant bacteria. The HCAP criteria classified 304 (49.5%) patients as high risk, with an area under the receiver operating characteristic curve (AUC) of 0.63 (95% confidence interval [CI] = 0.54 to 0.72), sensitivity of 0.69 (95% CI = 0.52 to 0.83), and specificity of 0.52 (95% CI = 0.48 to 0.56). None of the other algorithms improved both sensitivity and specificity or significantly improved the AUC. Compared to the HCAP criteria, the Shorr and Aliberti models classified more patients as high risk, resulting in higher sensitivity and lower specificity. The Shindo model classified fewer patients as high risk, with lower sensitivity and higher specificity. All algorithms for identification of resistant bacteria included in this study had suboptimal performance to guide antibiotic selection. New strategies for selecting empirical antibiotics for community-onset pneumonia are necessary.