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Utilizing Machine Learning to Predict Neurological Injury in Venovenous Extracorporeal Membrane Oxygenation Patients: An Extracorporeal Life Support Organization Registry Analysis
In a study analyzing 37,473 VV-ECMO patients, machine learning was used to predict acute brain injury (ABI), revealing a 7.1% incidence of ABI and identifying pre-ECMO cardiac arrest as the most significant risk factor. The study’s machine learning models, however, showed sub-optimal performance in predicting ABI, attributed to the low prevalence of ABI and the lack of standardized neuromonitoring and imaging protocols in the ELSO Registry data.