• Rebecca J Levy, Elizabeth W Mayne, Amanda G Sandoval Karamian, Mehreen Iqbal, Natasha Purington, Kathleen R Ryan, and Courtney J Wusthoff.
• Division of Child Neurology, Lucile Packard Children's Hospital at Stanford University, Dr Levy 750 Welch Road Suite 317, Palo Alto, CA, USA. rjlevy@stanford.edu.
• Neurocrit Care. 2022 Feb 1; 36 (1): 30-38.

BackgroundGuidelines recommend evaluation for electrographic seizures in neonates and children at risk, including after cardiopulmonary bypass (CPB). Although initial research using screening electroencephalograms (EEGs) in infants after CPB found a 21% seizure incidence, more recent work reports seizure incidences ranging 3-12%. Deep hypothermic cardiac arrest was associated with increased seizure risk in prior reports but is uncommon at our institution and less widely used in contemporary practice. This study seeks to establish the incidence of seizures among infants following CPB in the absence of deep hypothermic cardiac arrest and to identify additional risk factors for seizures via a prediction model.MethodsA retrospective chart review was completed of all consecutive infants ≤ 3 months who received screening EEG following CPB at a single center within a 2-year period during 2017-2019. Clinical and laboratory data were collected from the perioperative period. A prediction model for seizure risk was fit using a random forest algorithm, and receiver operator characteristics were assessed to classify predictions. Fisher's exact test and the logrank test were used to evaluate associations between clinical outcomes and EEG seizures.ResultsA total of 112 infants were included. Seizure incidence was 10.7%. Median time to first seizure was 28.1 h (interquartile range 18.9-32.2 h). The most important factors in predicting seizure risk from the random forest analysis included postoperative neuromuscular blockade, prematurity, delayed sternal closure, bypass time, and critical illness preoperatively. When variables captured during the EEG recording were included, abnormal postoperative neuroimaging and peak lactate were also highly predictive. Overall model accuracy was 90.2%; accounting for class imbalance, the model had excellent sensitivity and specificity (1.00 and 0.89, respectively).ConclusionsSeizure incidence was similar to recent estimates even in the absence of deep hypothermic cardiac arrest. By employing random forest analysis, we were able to identify novel risk factors for postoperative seizure in this population and generate a robust model of seizure risk. Further work to validate our model in an external population is needed.© 2021. Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society.

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