• Punkaj Gupta, Jeffrey M Gossett, Danny Kofos, and Mallikarjuna Rettiganti.
• Section of Cardiac Critical Care, Methodist Children's Hospital, San Antonio, TX, United States; Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, United States. Electronic address: punkaj_gupta@hotmail.com.
• J Crit Care. 2019 Feb 1; 49: 21-26.

PurposeTo create a real-time prediction tool to predict probability of ECMO deployment in children with cardiac or pulmonary failure.Materials And MethodsPatients ≤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.ResultsA 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/.ConclusionsHere, 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.Copyright © 2018 Elsevier Inc. All rights reserved.

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