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Published on: November 1, 2019
Validation of a model to predict electroencephalographic seizures in critically ill children
France W Fung1,2,3, Darshana S Parikh3, Marin Jacobwitz3
1Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Insights
A validated model accurately predicts electroencephalographic seizures (ESs) in critically ill children, aiding in the judicious use of continuous electroencephalographic monitoring (CEEG) resources.
Area of Science:
- Pediatric critical care medicine
- Clinical neurophysiology
Background:
- Electroencephalographic seizures (ESs) are common in critically ill children with encephalopathy.
- Continuous electroencephalographic monitoring (CEEG) is resource-intensive for identifying ESs.
- A prior study developed a prediction rule for high-risk ES patients.
Purpose of the Study:
- To validate a previously developed prediction model for ESs in an independent cohort of critically ill children.
- To assess the model's performance in identifying patients who would benefit from CEEG.
Main Methods:
- A prospective validation cohort of 314 critically ill children with acute encephalopathy undergoing CEEG was analyzed.
- The previously developed prediction model, using clinical and EEG variables, was applied.
- Test characteristics including sensitivity and specificity were calculated.
Main Results:
- The incidence of ESs in the validation cohort was 22%.
- The model demonstrated good calibration and discrimination, with 90% sensitivity and 93% negative predictive value.
- Applying the model could reduce CEEG utilization by 31% while missing 10% of ES cases.
Conclusions:
- A five-variable prediction model for ESs is well-validated in a new cohort.
- The model can help target limited CEEG resources to high-risk critically ill children.
- Implementation may substantially reduce CEEG utilization, though not all ESs will be identified.
Objective:
Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but identification requires extensive resources for continuous electroencephalographic monitoring (CEEG). In a previous study, we developed a clinical prediction rule using three clinical variables (age, acute encephalopathy category, clinically evident seizure[s] prior to CEEG initiation) and two electroencephalographic (EEG) variables (EEG background category and interictal discharges within the first 30 minutes of EEG) to identify patients at high risk for ESs for whom CEEG might be essential. In the current study, we aimed to validate the ES prediction model using an independent cohort.
Methods:
The prospectively acquired validation cohort consisted of 314 consecutive critically ill children treated in the Pediatric Intensive Care Unit of a quaternary care referral hospital with acute encephalopathy undergoing clinically indicated CEEG. We calculated test characteristics using the previously developed prediction model in the validation cohort. As in the generation cohort study, we selected a 0.10 cutpoint to emphasize sensitivity.
Results:
The incidence of ESs in the validation cohort was 22%. The generation and validation cohorts were alike in most clinical and EEG characteristics. The ES prediction model was well calibrated and well discriminating in the validation cohort. The model had a sensitivity of 90%, specificity of 37%, positive predictive value of 28%, and negative predictive value of 93%. If applied, the model would limit 31% of patients from undergoing CEEG while failing to identify 10% of patients with ESs. The model had similar performance characteristics in the generation and validation cohorts.
Significance:
A model employing five readily available clinical and EEG variables performed well when validated in a new consecutive cohort. Implementation would substantially reduce CEEG utilization, although some patients with ESs would not be identified. This model may serve a critical role in targeting limited CEEG resources to critically ill children at highest risk for ESs.
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