Seizure Assessment and Forecasting With Efficient Rapid-EEG: A Retrospective Multicenter Comparative Effectiveness Study.

Neurology

From the Department of Neurology (M.K.-A., R.M., A.F.S.), and Epilepsy Division of the Department of Neurology (S.F., A.S., G.A., P.V.K., J.L., S.H.), University of Wisconsin-Madison; Department of Neurology (S.F.), Southern Illinois University, Carbondale; Department of Neurology (A.S.), UCLA Harbor Medical Center, Torrance, CA; Epilepsy Division of Department of Neurology (I.S.S., K.G.), Massachusetts General Hospital, Boston; Comprehensive Epilepsy Center (J. Cormier, J. Cespedes, L.J.H.), Department of Neurology, Yale University, New Haven, CT; University of Connecticut School of Medicine (J. Cormier), Farmington; Epilepsy Division of Department of Neurology (K.G.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA; UHS Wilson Square Neurology (G.A.), Johnson City, NY; Universidad Autonoma de Centro America (UACA) School of Medicine (J. Cespedes), Granadilla, Cipreses, Costa Rica; Neurology Department (A.A.E., N.K., M.D.), University of New Mexico, Albuquerque; University of South Dakota (A.A.E.), Sanford School of Medicine, Vermillion; Comprehensive Epilepsy Team (O.M.H.), Neurology Department, University of New Mexico, Albuquerque; Center for Neuroengineering and Therapeutics (J.L.), University of Pennsylvania, Philadelphia; Department of Neurology (B.W.), Massachusetts General Hospital; and Beth Israel Deaconess Medical Center (B.W.), Boston, MA.

Published: July 2024

Background And Objectives: Approximately 30% of critically ill patients have seizures, and more than half of these seizures do not have an overt clinical correlate. EEG is needed to avoid missing seizures and prevent overtreatment with antiseizure medications. Conventional-EEG (cEEG) resources are logistically constrained and unable to meet their growing demand for seizure detection even in highly developed centers. Brief EEG screening with the validated 2HELPS2B algorithm was proposed as a method to triage cEEG resources, but it is hampered by cEEG requirements, primarily EEG technologists. Seizure risk-stratification using reduced time-to-application rapid response-EEG (rrEEG) systems (∼5 minutes) could be a solution. We assessed the noninferiority of the 2HELPS2B score on a 1-hour rrEEG compared to cEEG.

Methods: A multicenter retrospective EEG diagnostic accuracy study was conducted from October 1, 2021, to July 31, 2022. Chart and EEG review performed with consecutive sampling at 4 tertiary care centers, included records of patients ≥18 years old, from January 1, 2018, to June 20, 2022. Monte Carlo simulation power analysis yielded n = 500 rrEEG; for secondary outcomes n = 500 cEEG and propensity-score covariate matching was planned. Primary outcome, noninferiority of rrEEG for seizure risk prediction, was assessed per area under the receiver operator characteristic curve (AUC). Noninferiority margin (0.05) was based on the 2HELPS2B validation study.

Results: A total of 240 rrEEG with follow-on cEEG were obtained. Median age was 64 (interquartile range 22); 42% were female. 2HELPS2B on a 1-hour rrEEG met noninferiority to cEEG (AUC 0.85, 95% CI 0.78-0.90, = 0.001). Secondary endpoints of comparison with a matched contemporaneous cEEG showed no significant difference in AUC (0.89, 95% CI 0.83-0.94, = 0.31); in false negative rate for the 2HELPS2B = 0 group ( = 1.0) rrEEG (0.021, 95% CI 0-0.062), cEEG (0.016, 95% CI 0-0.048); nor in survival analyses.

Discussion: 2HELPS2B on 1-hour rrEEG is noninferior to cEEG for seizure prediction. Patients with low-risk (2HELPS2B = 0) may be able to forgo prolonged cEEG, allowing for increased monitoring of at-risk patients.

Classification Of Evidence: This study provides Class II evidence that rrEEG is noninferior to cEEG in calculating the 2HELPS2B score to predict seizure risk.

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Source
http://dx.doi.org/10.1212/WNL.0000000000209621DOI Listing

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