Clinical and Electroencephalographic Predictors of Seizures and Status Epilepticus in 12,450 Critically Ill Adults: A Retrospective Cohort Study.

OBJECTIVES: Status epilepticus (SE) is associated with significantly higher morbidity and mortality than isolated seizures. Our objective was to identify clinical diagnoses and rhythmic and periodic electroencephalogram patterns (RPPs) associated with SE and seizures. DESIGN: Retrospective cohort study. SETTING: Tertiary-care hospitals. SUBJECTS: Twelve thousand four hundred fifty adult hospitalized patients undergoing continuous electroencephalogram (cEEG) monitoring in selected participating sites in the Critical Care EEG Monitoring Research Consortium database (February 2013 to June 2021). INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: We defined an ordinal outcome in the first 72 hours of cEEG: no seizures, isolated seizures without SE, or SE (with or without isolated seizures). Composite groups included isolated seizures or SE (AnySz) and no seizure or isolated seizures. In this cohort (mean age: 60 ± 17 yr), 1,226 patients (9.8%) had AnySz and 439 patients (3.5%) had SE. In a multivariate model, factors independently associated with SE were cardiac arrest (9.2% with SE; adjusted odds ratio, 8.8 [6.3-12.1]), clinical seizures before cEEG (5.7%; 3.3 [2.5-4.3]), brain neoplasms (3.2%; 1.6 [1.0-2.6]), lateralized periodic discharges (LPDs) (15.4%; 7.3 [5.7-9.4]), brief potentially ictal rhythmic discharges (BIRDs) (22.5%; 3.8 [2.6-5.5]), and generalized periodic discharges (GPDs) (7.2%; 2.4 [1.7-3.3]). All above variables and lateralized rhythmic delta activity (LRDA) were also associated with AnySz. Factors disproportionately increasing odds of SE over isolated seizures were cardiac arrest (7.3 [4.4-12.1]), clinical seizures (1.7 [1.3-2.4]), GPDs (2.3 [1.4-3.5]), and LPDs (1.4 [1.0-1.9]). LRDA had lower odds of SE compared with isolated seizures (0.5 [0.3-0.9]). RPP modifiers did not improve SE prediction beyond RPPs presence/absence ( p = 0.8). CONCLUSIONS: Using the largest existing cEEG database, we identified specific predictors of SE (cardiac arrest, clinical seizures prior to cEEG, brain neoplasms, LPDs, GPDs, and BIRDs) and seizures (all previous and LRDA). These findings could be used to tailor cEEG monitoring for critically ill patients.

Association of Epileptiform Abnormality on Electroencephalography with Development of Epilepsy After Acute Brain Injury.

BACKGROUND/OBJECTIVES: Epileptiform abnormalities (EA) on continuous electroencephalography (cEEG) are associated with increased risk of acute seizures; however, data on their association with development of long-term epilepsy are limited. We aimed to investigate the association of EA in patients with acute brain injury (ABI): ischemic or hemorrhagic stroke, traumatic brain injury, encephalitis, or posterior reversible encephalopathy syndrome, and subsequent development of epilepsy. METHODS: This was a retrospective, single-center study of patients with ABI who had at least 6 hours of cEEG during the index admission between 1/1/2017 and 12/31/2018 and at least 12 months of follow-up. We compared patients with EAs; defined as lateralized periodic discharges (LPDs), lateralized rhythmic delta activity (LRDA), generalized periodic discharges (GPDs), and sporadic interictal epileptiform discharges (sIEDs) to patients without EAs on cEEG. The primary outcome was the new development of epilepsy, defined as the occurrence of spontaneous clinical seizures following hospital discharge. Secondary outcomes included time to development of epilepsy and use of anti-seizure medications (ASMs) at the time of last follow-up visit. RESULTS: One hundred and one patients with ABI met study inclusion criteria. Thirty-one patients (30.7%) had EAs on cEEG. The median (IQR) time to cEEG was 2 (1-5) days. During a median (IQR) follow-up period of 19.1 (16.2-24.3) months, 25.7% of patients developed epilepsy; the percentage of patients who developed epilepsy was higher in those with EAs compared to those without EAs (41.9% vs. 18.6%, p = 0.025). Patients with EAs were more likely to be continued on ASMs during follow-up compared to patients without EAs (67.7% vs. 38.6%, p = 0.009). Using multivariable Cox regression analysis, after adjusting for age, mental status, electrographic seizures on cEEG, sex, ABI etiology, and ASM treatment on discharge, patients with EAs had a significantly increased risk of developing epilepsy compared to patients without EA (hazard ratio 3.39; 95% CI 1.39-8.26; p = 0.007). CONCLUSIONS: EAs on cEEG in patients with ABI are associated with a greater than three-fold increased risk of new-onset epilepsy. cEEG findings in ABI may therefore be a useful risk stratification tool for assessing long-term risk of seizures and serve as a biomarker for new-onset epilepsy.

Electrical Wada for pre-surgical memory testing: a case report.

We report a case study of a surgical candidate, a 51-year-old woman with left temporal lobe epilepsy, who failed a left injection intracarotid amobarbital procedure (e.g., Wada test), scoring 0 of 8 items. This raised concerns for postoperative memory decline. However, the patient was uninterested in a neuromodulatory approach and wished to be reconsidered for surgery. A stereotactic laser amygdalohippocampotomy (SLAH) was considered, encouraging the need for an alternative test to evaluate risk of memory decline. We developed a novel approach to testing memory during stimulation of a depth electrode implanted in the hippocampus, i.e., an electric Wada. During multiple stimulation trials across a range of amplitudes, the patient scored up to 8 of 8 items, which suggested strong contralateral memory support. The surgical team proceeded with a radiofrequency ablation and a subsequent SLAH. The patient remains seizure-free at 12 months post SLAH with no evidence of verbal or visuospatial memory decline based on a post-surgical neuropsychological battery. We believe that this case study provides a proof of concept for the feasibility and possible utility of an electric version of the Wada procedure. Future studies are needed to develop an optimal paradigm and to validate this approach.

Assessment of the Validity of the 2HELPS2B Score for Inpatient Seizure Risk Prediction.

Importance: Seizure risk stratification is needed to boost inpatient seizure detection and to improve continuous electroencephalogram (cEEG) cost-effectiveness. 2HELPS2B can address this need but requires validation. Objective: To use an independent cohort to validate the 2HELPS2B score and develop a practical guide for its use. Design, Setting, and Participants: This multicenter retrospective medical record review analyzed clinical and EEG data from patients 18 years or older with a clinical indication for cEEG and an EEG duration of 12 hours or longer who were receiving consecutive cEEG at 6 centers from January 2012 to January 2019. 2HELPS2B was evaluated with the validation cohort using the mean calibration error (CAL), a measure of the difference between prediction and actual results. A Kaplan-Meier survival analysis was used to determine the duration of EEG monitoring to achieve a seizure risk of less than 5% based on the 2HELPS2B score calculated on first- hour (screening) EEG. Participants undergoing elective epilepsy monitoring and those who had experienced cardiac arrest were excluded. No participants who met the inclusion criteria were excluded. Main Outcomes and Measures: The main outcome was a CAL error of less than 5% in the validation cohort. Results: The study included 2111 participants (median age, 51 years; 1113 men [52.7%]; median EEG duration, 48 hours) and the primary outcome was met with a validation cohort CAL error of 4.0% compared with a CAL of 2.7% in the foundational cohort (P = .13). For the 2HELPS2B score calculated on only the first hour of EEG in those without seizures during that hour, the CAL error remained at less than 5.0% at 4.2% and allowed for stratifying patients into low- (2HELPS2B = 0; <5% risk of seizures), medium- (2HELPS2B = 1; 12% risk of seizures), and high-risk (2HELPS2B, ≥2; risk of seizures, >25%) groups. Each of the categories had an associated minimum recommended duration of EEG monitoring to achieve at least a less than 5% risk of seizures, a 2HELPS2B score of 0 at 1-hour screening EEG, a 2HELPS2B score of 1 at 12 hours, and a 2HELPS2B score of 2 or greater at 24 hours. Conclusions and Relevance: In this study, 2HELPS2B was validated as a clinical tool to aid in seizure detection, clinical communication, and cEEG use in hospitalized patients. In patients without prior clinical seizures, a screening 1-hour EEG that showed no epileptiform findings was an adequate screen. In patients with any highly epileptiform EEG patterns during the first hour of EEG (ie, a 2HELPS2B score of ≥2), at least 24 hours of recording is recommended.

Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients.

Importance: Continuous electroencephalography (EEG) use in critically ill patients is expanding. There is no validated method to combine risk factors and guide clinicians in assessing seizure risk. Objective: To use seizure risk factors from EEG and clinical history to create a simple scoring system associated with the probability of seizures in patients with acute illness. Design, Setting, and Participants: We used a prospective multicenter (Emory University Hospital, Brigham and Women's Hospital, and Yale University Hospital) database containing clinical and electrographic variables on 5427 continuous EEG sessions from eligible patients if they had continuous EEG for clinical indications, excluding epilepsy monitoring unit admissions. We created a scoring system model to estimate seizure risk in acutely ill patients undergoing continuous EEG. The model was built using a new machine learning method (RiskSLIM) that is designed to produce accurate, risk-calibrated scoring systems with a limited number of variables and small integer weights. We validated the accuracy and risk calibration of our model using cross-validation and compared its performance with models built with state-of-the-art logistic regression methods. The database was developed by the Critical Care EEG Research Consortium and used data collected over 3 years. The EEG variables were interpreted using standardized terminology by certified reviewers. Exposures: All patients had more than 6 hours of uninterrupted EEG recordings. Main Outcomes and Measures: The main outcome was the average risk calibration error. Results: There were 5427 continuous EEGs performed on 4772 participants (2868 men, 49.9%; median age, 61 years) performed at 3 institutions, without further demographic stratification. Our final model, 2HELPS2B, had an area under the curve of 0.819 and average calibration error of 2.7% (95% CI, 2.0%-3.6%). It included 6 variables with the following point assignments: (1) brief (ictal) rhythmic discharges (B[I]RDs) (2 points); (2) presence of lateralized periodic discharges, lateralized rhythmic delta activity, or bilateral independent periodic discharges (1 point); (3) prior seizure (1 point); (4) sporadic epileptiform discharges (1 point); (5) frequency greater than 2.0 Hz for any periodic or rhythmic pattern (1 point); and (6) presence of "plus" features (superimposed, rhythmic, sharp, or fast activity) (1 point). The probable seizure risk of each score was 5% for a score of 0, 12% for a score of 1, 27% for a score of 2, 50% for a score of 3, 73% for a score of 4, 88% for a score of 5, and greater than 95% for a score of 6 or 7. Conclusions and Relevance: The 2HELPS2B model is a quick accurate tool to aid clinical judgment of the risk of seizures in critically ill patients.