Neurologic Statistical Prognostication and Risk Assessment for Kids on Extracorporeal Membrane Oxygenation-Neuro SPARK.
Neel ShahSaurabh MathurPrashanth ShanmughamXilong LiRavi R ThiagarajanSriraam NatarajanLakshmi RamanPublished in: ASAIO journal (American Society for Artificial Internal Organs : 1992) (2023)
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.
Keyphrases
- extracorporeal membrane oxygenation
- acute respiratory distress syndrome
- respiratory failure
- risk factors
- risk assessment
- machine learning
- mechanical ventilation
- end stage renal disease
- ejection fraction
- prognostic factors
- intensive care unit
- palliative care
- human health
- deep learning
- cancer therapy
- climate change
- childhood cancer