Electrographic seizures and ictal-interictal continuum (IIC) patterns in critically ill patients.

Critical care long-term continuous electroencephalogram (cEEG) monitoring has expanded dramatically in the last several decades spurned by technological advances in EEG digitalization and several key clinical findings: 1-Seizures are relatively common in the critically ill-large recent observational studies suggest that around 20% of critically ill patients placed on cEEG have seizures. 2-The majority (~75%) of patients who have seizures have exclusively "electrographic seizures", that is, they have no overt ictal clinical signs. Along with the discovery of the unexpectedly high incidence of seizures was the high prevalence of EEG patterns that share some common features with archetypical electrographic seizures but are not uniformly considered to be "ictal". These EEG patterns include lateralized periodic discharges (LPDs) and generalized periodic discharges (GPDs)-patterns that at times exhibit ictal-like behavior and at other times behave more like an interictal finding. Dr. Hirsch and colleagues proposed a conceptual framework to describe this spectrum of patterns called the ictal-interictal continuum (IIC). In the following years, investigators began to answer some of the key pragmatic clinical concerns such as which patients are at risk of seizures and what is the optimal duration of cEEG use. At the same time, investigators have begun probing the core questions for critical care EEG-what is the underlying pathophysiology of these patterns, at what point do these patterns cause secondary brain injury, what are the optimal treatment strategies, and how do these patterns affect clinical outcomes such as neurological disability and the development of epilepsy. In this review, we cover recent advancements in both practical concerns regarding cEEG use, current treatment strategies, and review the evidence associating IIC/seizures with poor clinical outcomes.

Validation of the 2HELPS2B Seizure Risk Score in Acute Brain Injury Patients.

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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

A quantitative model to differentiate nonaneurysmal perimesencephalic subarachnoid hemorrhage from aneurysmal etiology.

OBJECTIVE: Nonaneurysmal perimesencephalic subarachnoid hemorrhage (pmSAH) is considered to have a lower-risk pattern than other types of subarachnoid hemorrhage (SAH). However, a minority of patients with pmSAH may harbor a causative posterior circulation aneurysm. To exclude this possibility, many institutions pursue exhaustive imaging. In this study the authors aimed to develop a novel predictive model based on initial noncontrast head CT (NCHCT) features to differentiate pmSAH from aneurysmal causes. METHODS: The authors retrospectively reviewed patients admitted to an academic center for treatment of a suspected aneurysmal SAH (aSAH) during the period from 2016 to 2021. Patients with a final diagnosis of pmSAH or posterior circulation aSAH were included. Using NCHCT, the thickness (continuous variable) and location of blood in basal cisterns and sylvian fissures (categorical variables) were compared between groups. A scoring system was created using features that were significantly different between groups. Receiver operating characteristic curve analysis was used to measure the accuracy of this model in predicting aneurysmal etiology. A separate patient cohort was used for external validation of this model. RESULTS: Of 420 SAH cases, 48 patients with pmSAH and 37 with posterior circulation aSAH were identified. Blood thickness measurements in the crural and ambient cisterns and interhemispheric and sylvian fissures and degree of extension into the sylvian fissure were all significantly different between groups (all p < 0.001). The authors developed a 10-point scoring model to predict aneurysmal causes with high accuracy (area under the curve [AUC] 0.99; 95% CI 0.98-1.00; OR per point increase 10; 95% CI 2.18-46.4). External validation resulted in persistently high accuracy (AUC 0.97; 95% CI 0.92-1.00) of this model. CONCLUSIONS: A risk stratification score using initial blood clot burden may accurately differentiate between aneurysmal and nonaneurysmal pmSAH. Larger prospective studies are encouraged to further validate this quantitative tool.

Radiographic predictors of aneurysmal etiology in patients with aneurysmal pattern subarachnoid hemorrhage.

OBJECTIVE: Spontaneous angiogram-negative nonperimesencephalic subarachnoid hemorrhage (an-NPSAH) can represent a diagnostic and management dilemma. The authors sought to determine radiographic predictors of aneurysmal etiology based on admission noncontrast head CT scans. METHODS: The authors performed a retrospective cohort study of prospectively collected data from consecutive patients who were admitted for spontaneous subarachnoid hemorrhage (SAH) with suspected aneurysmal etiology to an academic center from 2016 to 2021. They compared blood thickness in the basal cisterns and sylvian fissures and modified Graeb scores on admission head CT scans between the two groups and subsequently developed a predictive model to identify aneurysmal etiology. RESULTS: Of 259 included patients (mean age 56 years [SD 12.7 years]; 55% female), 209 had aneurysmal SAH (aSAH) and 50 had an-NPSAH. The median modified Graeb scores were similar for aSAH and an-NPSAH (6 [IQR 2-10] vs 3.5 [IQR 0-8.5], p = 0.33). The mean blood thickness was greater in the sylvian fissure (p = 0.010) and interhemispheric cisterns (p = 0.002), and there was a greater median degree of extension of blood in the sylvian fissures (p = 0.001) in aSAH than in an-NPSAH patients, but the mean blood thickness was less in the prepontine cistern (p = 0.014). The authors' scoring model was constructed based on differences in radiographic features. Receiver operating characteristic curve analysis showed acceptable accuracy in predicting aneurysmal etiology (area under the curve 0.71, 95% CI 0.62-0.79). CONCLUSIONS: There are differences in radiographic features on admission head CT between an-NPSAH and aSAH patients. The authors' proposed risk stratification model may be considered for further development and use in clinical practice in the future.

Cisternal Score: A Radiographic Score to Predict Ventriculoperitoneal Shunt Requirement in Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND: Persistent hydrocephalus requiring a ventriculoperitoneal shunt (VPS) can complicate the management of aneurysmal subarachnoid hemorrhage (aSAH). Identification of high-risk patients may guide external ventricular drain management. OBJECTIVE: To identify early radiographic predictors for persistent hydrocephalus requiring VPS placement. METHODS: In a 2-center retrospective study, we compared radiographic features on admission noncontrast head computed tomography scans of patients with aSAH requiring a VPS to those who did not, at 2 referral academic centers from 2016 through 2021. We quantified blood clot thickness in the basal cisterns including interpeduncular, ambient, crural, prepontine, interhemispheric cisterns, and bilateral Sylvian fissures. We then created the cisternal score (CISCO) using features that were significantly different between groups. RESULTS: We included 229 survivors (mean age 55.6 years [SD 13.1]; 63% female) of whom 50 (22%) required VPS. CISCO was greater in patients who required a VPS than those who did not (median 4, IQR 3-6 vs 2, IQR 1-4; P < .001). Higher CISCO was associated with higher odds of developing persistent hydrocephalus with VPS requirement (odds ratio 1.6 per point increase, 95% CI 1.34-1.9; P < .001), independent of age, Hunt and Hess grades, and modified GRAEB scores. CISCO had higher accuracy in predicting VPS requirement (area under the curve 0.75, 95% CI 0.68-0.82) compared with other predictors present on admission. CONCLUSION: Cisternal blood clot quantification on admission noncontrast head computed tomography scan is feasible and can be used in predicting persistent hydrocephalus with VPS requirement in patients with aSAH. Future prospective studies are recommended to further validate this tool.

Electrographic Seizures in the Critically Ill.

Identifying and treating critically ill patients with seizures can be challenging. In this article, the authors review the available data on patient populations at risk, seizure prognostication with tools such as 2HELPS2B, electrographic seizures and the various ictal-interictal continuum patterns with their latest definitions and associated risks, ancillary testing such as imaging studies, serum biomarkers, and invasive multimodal monitoring. They also illustrate 5 different patient scenarios, their treatment and outcomes, and propose recommendations for targeted treatment of electrographic seizures in critically ill patients.

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.

Febrile Infection-Related Epilepsy Syndrome Treated Successfully With Anakinra in a 21-Year-Old Woman.

INTRODUCTION: Febrile infection-related epilepsy syndrome (FIRES) is a syndrome of new-onset status epilepticus preceded by fever and highly refractory to treatment, thus resulting in high mortality and severe neurologic morbidity in surviving patients. Anakinra is an IL-1 receptor antagonist that has previously demonstrated efficacy in treating children with FIRES. CASE PRESENTATION: A 21-year-old previously healthy woman presented with new-onset superrefractory status epilepticus following a febrile illness. This was subsequently diagnosed as FIRES after an extensive evaluation failed to identify an alternative etiology. The patient's seizures were refractory to numerous antiepileptic drugs and immunomodulatory therapy. She was maintained under pharmacologic sedation for 31 days. MANAGEMENT AND OUTCOME: Anakinra was initiated on day 32 of her hospital stay, with swift and complete remission of her status epilepticus. Seizures ceased within 24 hours. The patient remains in remission with minimal side effects from the medication and no known long-term morbidity. DISCUSSION: Here we report what we believe is the second case of super-refractory status epilepticus due to FIRES responding to anakinra, and the first such case in an adult patient. Anakinra was well tolerated with few side effects. Our results are further evidence for the autoinflammatory nature of FIRES and support the use of anakinra early in the treatment to prevent long-term sequelae.

Lateralized periodic discharges frequency correlates with glucose metabolism.

OBJECTIVE: To investigate the correlation between characteristics of lateralized periodic discharges (LPDs) and glucose metabolism measured by 18F-fluorodeoxyglucose (FDG)-PET. METHODS: We retrospectively reviewed medical records to identify patients who underwent FDG-PET during EEG monitoring with LPDs present during the FDG uptake period. Two blinded board-certified neurophysiologists independently interpreted EEGs. FDG uptake was measured using standardized uptake value (SUV). Structural images were fused with PET images to aid with localization of SUV. Two PET readers independently measured maximum SUV. Relative SUV values were obtained by normalization of the maximum SUV to the SUV of pons (SUVRpons). LPD frequency was analyzed both as a categorical variable and as a continuous measure. Other secondary variables included duration, amplitude, presence of structural lesion, and "plus" EEG features such as rhythmic or fast sharp activity. RESULTS: Nine patients were identified and 7 had a structural etiology for LPDs. Analysis using frequency as a categorical variable and continuous variable showed an association between increased LPD frequency and increased ipsilateral SUVRpons (p = 0.02). Metabolism associated with LPDs (0.5 Hz as a baseline) increased by a median of 100% at 1 Hz and for frequencies >1 Hz increased by a median of 309%. There were no statistically significant differences in SUVRpons for other factors including duration (p = 0.10), amplitude (p = 0.80), structural etiology (p = 0.55), or "plus" features such as rhythmic or fast sharp activity (p = 0.84). CONCLUSIONS: Metabolic activity increases monotonically with LPD frequency. LPD frequency should be a measure of interest when developing neuroprotection strategies in critical neurologic illness.