Related Experiment Video
Updated: Oct 26, 2025
Author Spotlight: Simulating Pediatric Cardiac Surgery Using a Neonatal Piglet Model
Published on: May 26, 2023
Evaluation of Seizure Risk in Infants After Cardiopulmonary Bypass in the Absence of Deep Hypothermic Cardiac Arrest
Rebecca J Levy1,2, Elizabeth W Mayne3, Amanda G Sandoval Karamian4
1Division 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.
Insights
Seizure incidence after cardiopulmonary bypass (CPB) in infants is 10.7%, similar to recent findings. Novel risk factors were identified using a prediction model, aiding in early seizure detection in high-risk infants.
Area of Science:
- Pediatric Cardiology
- Neonatal Neurology
- Clinical Neurophysiology
Background:
- Guidelines recommend seizure evaluation in at-risk neonates and children, including post-cardiopulmonary bypass (CPB).
- Previous studies reported varying seizure incidences (3-12%) post-CPB, with deep hypothermic cardiac arrest linked to higher risk.
- This study addresses seizure incidence and risk factors in infants post-CPB without deep hypothermic cardiac arrest.
Purpose of the Study:
- To determine the incidence of electrographic seizures in infants following CPB.
- To identify novel risk factors for seizures in this population.
- To develop a predictive model for seizure risk post-CPB.
Main Methods:
- Retrospective chart review of 112 infants (≤3 months) undergoing screening EEG post-CPB.
- Data collected included perioperative clinical and laboratory information.
- A random forest algorithm was used to build a seizure risk prediction model.
Main Results:
- Seizure incidence was 10.7%, with a median time to first seizure of 28.1 hours.
- Key predictors included postoperative neuromuscular blockade, prematurity, delayed sternal closure, bypass time, and preoperative critical illness.
- Abnormal postoperative neuroimaging and peak lactate were also highly predictive; the model achieved 90.2% accuracy.
Conclusions:
- Seizure incidence post-CPB in infants, without deep hypothermic cardiac arrest, aligns with recent estimates.
- Novel risk factors were identified, enabling the creation of a robust seizure risk prediction model.
- External validation of the developed model is recommended for future research.
Background:
Guidelines 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.
Methods:
A 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.
Results:
A 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).
Conclusions:
Seizure 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.
Related Concept Videos
01:28Cardiomyopathy VII: Pre and Post Operative Nursing Management
01:25Cardiopulmonary Resuscitation IV: Pharmacological Management