Abstract
Background Among children with respiratory failure from viral lower respiratory tract infection (LRTI), up to 39% will develop pulmonary bacterial coinfection, yet nearly all will receive antibiotics. We sought to identify patients with viral LRTI requiring mechanical ventilation at low risk of bacterial coinfection through the use of a risk prediction model. Methods We performed a retrospective cohort study identifying all patients admitted to the intensive care unit with laboratory-confirmed viral LRTI requiring invasive mechanical ventilation over a 2-year period and partitioned data in experimental and validation datasets. A multivariate probit regression model was constructed including variables associated with bacterial coinfection in the experimental dataset. Model was validated and recalibrated using the validation dataset. Model discrimination was assessed using receiver operating characteristic curve analysis. Results There were 126 patients included in the analysis. Variables associated with bacterial coinfection included tracheostomy in situ, Gram-stained smear white blood cells, and bacteria. The final recalibrated model discriminating between no coinfection and coinfection had an area under the curve of 0.8696. Conclusion Our prediction model identifies patients with viral LRTI requiring mechanical ventilation at very low risk of bacterial coinfection and has the potential to decrease antibiotic utilization without negatively impacting clinical outcome.