Validation of a Model for Targeted EEG Monitoring Duration in Critically Ill Children

France W Fung, Jiaxin Fan, Darshana S Parikh, Lisa Vala, Maureen Donnelly, Marin Jacobwitz, Alexis A Topjian, Rui Xiao, Nicholas S Abend, France W Fung, Jiaxin Fan, Darshana S Parikh, Lisa Vala, Maureen Donnelly, Marin Jacobwitz, Alexis A Topjian, Rui Xiao, Nicholas S Abend

Abstract

Purpose: Continuous EEG monitoring (CEEG) to identify electrographic seizures (ES) in critically ill children is resource intense. Targeted strategies could enhance implementation feasibility. We aimed to validate previously published findings regarding the optimal CEEG duration to identify ES in critically ill children.

Methods: This was a prospective observational study of 1,399 consecutive critically ill children with encephalopathy. We validated the findings of a multistate survival model generated in a published cohort ( N = 719) in a new validation cohort ( N = 680). The model aimed to determine the CEEG duration at which there was <15%, <10%, <5%, or <2% risk of experiencing ES if CEEG were continued longer. The model included baseline clinical risk factors and emergent EEG risk factors.

Results: A model aiming to determine the CEEG duration at which a patient had <10% risk of ES if CEEG were continued longer showed similar performance in the generation and validation cohorts. Patients without emergent EEG risk factors would undergo 7 hours of CEEG in both cohorts, whereas patients with emergent EEG risk factors would undergo 44 and 36 hours of CEEG in the generation and validation cohorts, respectively. The <10% risk of ES model would yield a 28% or 64% reduction in CEEG hours compared with guidelines recommending CEEG for 24 or 48 hours, respectively.

Conclusions: This model enables implementation of a data-driven strategy that targets CEEG duration based on readily available clinical and EEG variables. This approach could identify most critically ill children experiencing ES while optimizing CEEG use.

Conflict of interest statement

N. S. Abend: Funding from NIH (NINDS) K02NS096058 and Wolfson Foundation for this study. Other funding from PCORI (to institution), UCB Pharma (to institution), Epilepsy Foundation (consulting), and Demos Publishing (royalties). The remaining authors have no funding or conflicts of interest to disclose.

Copyright © 2022 by the American Clinical Neurophysiology Society.

Figures

Figure 1.
Figure 1.
Summary of the model states. Subjects could remain in the entry state, transition from the entry state to the electrographic seizures (ES) state, or transition from the entry state to the EEG risk state. Subjects in the EEG risk state could remain in the EEG risk state or transition to the ES state.
Figure 2.
Figure 2.
Swimmer plots and multistate survival analysis results for the (A) validation (N=680) cohort and (B) combined (N=1399) cohort. Top Row: Swimmer plot showing the duration of continuous EEG monitoring (line length along x-axis) and state (entry = green; EEG risk = yellow; electrographic seizure = red) for each subject (y-axis). Middle Row: Proportion of subjects in each state (entry, ES risk, ES) over time. Some subjects remain in the same state over time (same color), some subjects develop EEG risk factors but never experience ES (green to yellow transition), some subjects experience ES without having experienced EEG risk factors (green to red transition), and some subjects experience EEG risk factors and then ES (green to yellow to red transitions). Bottom Row: Remaining risk of transitioning to the ES state for patients in the entry (green) and EEG risk (yellow) states. Shaded areas represent the 95% confidence intervals. For the full cohort and each of the four clinical risk states, the risk of transitioning to the ES state remains higher over time for subjects with EEG risk factors than those in the entry state.
Figure 2.
Figure 2.
Swimmer plots and multistate survival analysis results for the (A) validation (N=680) cohort and (B) combined (N=1399) cohort. Top Row: Swimmer plot showing the duration of continuous EEG monitoring (line length along x-axis) and state (entry = green; EEG risk = yellow; electrographic seizure = red) for each subject (y-axis). Middle Row: Proportion of subjects in each state (entry, ES risk, ES) over time. Some subjects remain in the same state over time (same color), some subjects develop EEG risk factors but never experience ES (green to yellow transition), some subjects experience ES without having experienced EEG risk factors (green to red transition), and some subjects experience EEG risk factors and then ES (green to yellow to red transitions). Bottom Row: Remaining risk of transitioning to the ES state for patients in the entry (green) and EEG risk (yellow) states. Shaded areas represent the 95% confidence intervals. For the full cohort and each of the four clinical risk states, the risk of transitioning to the ES state remains higher over time for subjects with EEG risk factors than those in the entry state.

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