Abstract
This study presents Neuro-SPARK, the first scoring system developed to assess the risk of neurologic injury in pediatric and neonatal patients on extracorporeal membrane oxygenation (ECMO). Using the extracorporeal life support organization (ELSO) registry, we applied robust machine learning methodologies and clinical expertise to a 10 years dataset. We produced separate models for veno-venous (V-V ECMO) and veno-arterial (V-A ECMO) configurations due to their different risk factors and prevalence of neurologic injury. Our models identified 14 predictor variables for V-V ECMO and 20 for V-A ECMO, which demonstrated moderate accuracy in predicting neurologic injury as defined by the area under the receiver operating characteristic (AUROC) (V-V = 0.63, V-A = 0.64) and good calibration as measured by the Brier score (V-V = 0.1, V-A = 0.15). Furthermore, our post-hoc analysis identified high- and low-risk groups that may aid clinicians in targeted neuromonitoring and guide future research on ECMO-associated neurologic injury. Despite the inherent limitations, Neuro-SPARK lays the foundation for a risk-assessment tool for neurologic injury in ECMO patients, with potential implications for improved patient outcomes.