Creation of an empiric tool to predict ECMO deployment in pediatric respiratory or cardiac failure

Abstract Purpose To create a real-time prediction tool to predict probability of ECMO deployment in children with cardiac or pulmonary failure. Materials and methods Patients ≤18 years old admitted to an ICU that participated in the Virtual Pediatric Systems database (2009–2015) were included. Logistic regression models using adaptive lasso methodology were used to identify independent factors associated with ECMO use. Results A total of 538,202 ICU patients from 140 ICUs qualified for inclusion. ECMO was deployed in 3484 patients (0.6%) with a mortality of 1450 patients (41.6%). The factors associated with increased probability of ECMO use included: younger age, pulmonary hypertension, congenital heart disease, high-complexity cardiac surgery, cardiomyopathy, acute lung injury, shock, renal failure, cardiac arrest, use of nitric oxide, use of either conventional mechanical ventilation or high frequency oscillatory ventilation, and higher annual ECMO center volume. The area under the receiver operating curve for this model was 0.90 (95% CI: 0.85–0.93). This tool can be accessed at https://soipredictiontool.shinyapps.io/ECMORisk/ . Conclusions Here, we present a tool to predict ECMO deployment among critically ill children; this tool will help create real-time risk stratification among critically ill children, and it will help with benchmarking, family counseling, and research.