Clinical algorithm reduces antibiotic use among children presenting with respiratory symptoms to hospital in central Vietnam

Abstract

Objective: To assess the safety and utility of a pragmatic clinical algorithm to guide rational antibiotic use in children presenting with respiratory infection.; Methods: The effect of an algorithm to guide the management of young (< 5 years) children presenting with respiratory symptoms to the Da Nang Hospital for Women and Children, Vietnam, was evaluated in a before-after intervention analysis. The main outcome was reduction in antibiotic use, with monitoring of potential harm resulting from reduced antibiotic use. The intervention comprised a single training session of physicians in the use of an algorithm informed by local evidence; developed during a previous prospective observational study. The evaluation was performed one month after the training.; Results: Of the 1290 children evaluated before the intervention, 102 (7.9%) were admitted to hospital and 556/1188 (46.8%) were sent home with antibiotics. Due to COVID-19, only 166 children were evaluated after the intervention of whom 14 (8.4%) were admitted to hospital and 54/152 (35.5%) were sent home with antibiotics. Antibiotic use was reduced (from 46.8% to 35.5%; p = 0.009) after clinician training, but adequate comparison was compromised. The reduction was most pronounced in children with wheeze or runny nose and no fever, or a normal chest radiograph, where antibiotic use declined from 46.7% to 28.8% (p < 0.0001). The frequency of repeat presentation to hospital was similar between the two study periods (141/1188; 11.9% before and 10/152; 6.6% after; p = 0.10). No child represented with serious disease after being sent home without antibiotics.; Conclusions: We observed a reduction in antibiotic use in young children with a respiratory infection after physician training in the use of a simple evidence-based management algorithm. However, the study was severely impacted by COVID-19 restrictions, requiring further evaluation to confirm the observed effect. (© 2023. The Author(s).)