Background and Objective
Seizures are common after traumatic brain injury (TBI), aneurysmal subarachnoid hemorrhage (aSAH), subdural hematoma (SDH), and non-traumatic intraparenchymal hemorrhage (IPH)—collectively defined herein as acute brain injury (ABI). Most seizures in ABI are subclinical, meaning that they are only detectable with EEG. A method is required to identify patients at greatest risk of seizures and thereby in need of prolonged continuous EEG monitoring. 2HELPS2B is a simple point system developed to address this need. 2HELPS2B estimates seizure risk for hospitalized patients using five EEG findings and one clinical finding (pre-EEG seizure). The initial 2HELPS2B study did not specifically assess the ABI subpopulation. In this study, we aim to validate the 2HELPS2B score in ABI and determine its relative predictive accuracy compared to a broader set of clinical and electrographic factors.
We queried the Critical Care EEG Monitoring Research Consortium database for ABI patients age ≥ 18 with > 6 h of continuous EEG monitoring; data were collected between February 2013 and November 2018. The primary outcome was electrographic seizure. Clinical factors considered were age, coma, encephalopathy, ABI subtype, and acute suspected or confirmed pre-EEG clinical seizure. Electrographic factors included 18 EEG findings. Predictive accuracy was assessed using a machine-learning paradigm with area under the receiver operator characteristic (ROC) curve as the primary outcome metric. Three models (clinical factors alone, EEG factors alone, EEG and clinical factors combined) were generated using elastic-net logistic regression. Models were compared to each other and to the 2HELPS2B model. All models were evaluated by calculating the area under the curve (AUC) of a ROC analysis and then compared using permutation testing of AUC with bootstrapping to generate confidence intervals.
A total of 1528 ABI patients were included. Total seizure incidence was 13.9%. Seizure incidence among ABI subtype varied: IPH 17.2%, SDH 19.1%, aSAH 7.6%, TBI 9.2%. Age ≥ 65 (p = 0.015) and pre-cEEG acute clinical seizure (p < 0.001) positively affected seizure incidence. Clinical factors AUC = 0.65 [95% CI 0.60–0.71], EEG factors AUC = 0.82 [95% CI 0.77–0.87], and EEG and clinical factors combined AUC = 0.84 [95% CI 0.80–0.88]. 2HELPS2B AUC = 0.81 [95% CI 0.76–0.85]. The 2HELPS2B AUC did not differ from EEG factors (p = 0.51), or EEG and clinical factors combined (p = 0.23), but was superior to clinical factors alone (p < 0.001).
Accurate seizure risk forecasting in ABI requires the assessment of EEG markers of pathologic electro-cerebral activity (e.g., sporadic epileptiform discharges and lateralized periodic discharges). The 2HELPS2B score is a reliable and simple method to quantify these EEG findings and their associated risk of seizure.
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Critical Care EEG Monitoring Research Consortium provided research infrastructure to complete this study.
This study was supported by a Research Infrastructure Award from the American Epilepsy Society and the Epilepsy Foundation.
Conflict of interest
Dr. Hirsch, over the last 12 months, received research support to Yale University for investigator-initiated studies from Monteris, Upsher-Smith, and The Daniel Raymond Wong Neurology Research Fund at Yale. Consultation fees were collected for advising Adamas, Aquestive, Ceribell, Eisai, Medtronic and UCB. Royalties were received for authorship from UpToDate-Neurology, and from Wiley for book co-authorship: “Atlas of EEG in Critical Care”, by Hirsch and Brenner. Also received honoraria for speaking from Neuropace. All additional authors report that they have no conflicts of interest.
Ethical Approval/Informed Consent
This research study adhered to ethical standards. The IRB at each institution approved of the study. Given the de-identified and retrospective nature of the research, a waiver for informed consent was granted by each institution's IRB.
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Moffet, E.W., Subramaniam, T., Hirsch, L.J. et al. Validation of the 2HELPS2B Seizure Risk Score in Acute Brain Injury Patients. Neurocrit Care 33, 701–707 (2020). https://doi.org/10.1007/s12028-020-00939-x