Publications

Abstract

BACKGROUND: Pneumonia is the leading cause of antibiotic use and hospitalization in Vietnam. There is a need for better prediction of unlikely bacterial pneumonia and adverse pneumonia outcome in order to guide hospital admission and improve rational antibiotic use. METHODS: All children under 5 admitted with pneumonia (per clinician assessment) to the Da Nang Hospital for Women and Children were prospectively enrolled. Children were classified as having likely or unlikely bacterial pneumonia and followed for outcome assessment. A Bayesian model averaging approach was used to identify predictors of unlikely bacterial pneumonia and adverse pneumonia outcome, which guided the development of a pragmatic management algorithm. RESULTS: Of 3817 patients assessed, 2199 (57.6%) met World Health Organization (WHO) pneumonia criteria. In total, 1594 (41.7%) children were classified as having unlikely and 129 (3.4%) as having likely bacterial pneumonia. The remainder (2399; 62.9%) were considered to have disease of uncertain etiology. Factors predictive of unlikely bacterial pneumonia were no fever, no consolidation on chest radiograph, and absolute neutrophil count <5 x 109/L at presentation, which had a negative predictive value (NPV) for likely bacterial pneumonia of 99.0%. Among those who met WHO pneumonia criteria, 8.6% (189/2199) experienced an adverse outcome. Not having any WHO danger sign or consolidation on chest radiograph had an NPV of 96.8% for adverse pneumonia outcome. CONCLUSIONS: An algorithm that screens for predictors of likely bacterial pneumonia and adverse pneumonia outcome could reduce unnecessary antibiotic use and hospital admission, but its clinical utility requires validation in a prospective study.