Escalation to Barbiturate-Induced Coma for Refractory Seizures after Liver Transplantation

Seizures after liver transplantation were previously thought to be a reliable harbinger of catastrophe, but more recent studies have found seizure activity to be relatively common, and most cases do not result in a poor outcome. Generalized seizures are the most common, and they typically occur de novo within the first two weeks after transplantation. The underlying cause for seizure activity in these patients may be complex, with potential etiologies including metabolic, infectious, cerebrovascular, and medication-induced causes. Identification of the underlying cause and the use of antiepileptic drugs (AEDs) is crucial for minimizing risk to the patient’s neurologic and overall health. In this report, we present the case of a patient with refractory seizures unresponsive to conventional treatment, requiring prolonged barbiturate burst suppression with ventilator support. Seizure activity eventually ceased, and the patient made a full recovery.

Burst Suppression During General Anesthesia and Postoperative Outcomes: Mini Review

In the last decade, burst suppression has been increasingly studied by many to examine whether it is a mechanism leading to postoperative cognitive impairment. Despite a lack of consensus across trials, the current state of research suggests that electroencephalogram (EEG) burst suppression, duration and EEG emergence trajectory may predict postoperative delirium (POD). A mini literature review regarding evidence about burst suppression impact and susceptibilities was conducted, resulting in conflicting studies. Primarily, studies have used different algorithm values to replace visual burst suppression examination, although many studies have since emerged showing that algorithms underestimate burst suppression duration. As these methods may not be interchangeable with visual analysis of raw data, it is a potential factor for the current heterogeneity between data. Even though additional research trials incorporating the use of raw EEG data are necessary, the data currently show that monitoring with commercial intraoperative EEG machines that use EEG indices to estimate burst suppression may help physicians identify burst suppression and guide anesthetic titration during surgery. These modifications in anesthetics could lead to preventing unfavorable outcomes. Furthermore, some studies suggest that brain age, baseline impairment, and certain medications are risk factors for burst suppression and postoperative delirium. These patient characteristics, in conjunction with intraoperative EEG monitoring, could be used for individualized patient care. Future studies on the feasibility of raw EEG monitoring, new technologies for anesthetic monitoring and titration, and patient-associated risk factors are crucial to our continued understanding of burst suppression and postoperative delirium.

Sedation-Induced Burst Suppression Predicts Positive Outcome Following Traumatic Brain Injury

While electroencephalogram (EEG) burst-suppression is often induced therapeutically using sedatives in the intensive care unit (ICU), there is hitherto no evidence with respect to its association to outcome in moderate-to-severe neurological patients. We examined the relationship between sedation-induced burst-suppression (SIBS) and outcome at hospital discharge and at 6-month follow up in patients surviving moderate-to-severe traumatic brain injury (TBI). For each of 32 patients recovering from coma after moderate-to-severe TBI, we measured the EEG burst suppression ratio (BSR) during periods of low responsiveness as assessed with the Glasgow Coma Scale (GCS). The maximum BSR was then used to predict the Glasgow Outcome Scale extended (GOSe) at discharge and at 6 months post-injury. A multi-model inference approach was used to assess the combination of predictors that best fit the outcome data. We found that BSR was positively associated with outcomes at 6 months (P = 0.022) but did not predict outcomes at discharge. A mediation analysis found no evidence that BSR mediates the effects of barbiturates or propofol on outcomes. Our results provide initial observational evidence that burst suppression may be neuroprotective in acute patients with TBI etiologies. SIBS may thus be useful in the ICU as a prognostic biomarker.

Epilepsy of Infancy With Migrating Focal Seizures (EIMFS): Expansion of Clinical Phenotypic And Genotypic Spectra

EIMFS is a rare early infantile epileptic encephalopathy with unknown etiology and poor prognosis. This study included 36 patients who were diagnosed with EIMFS. 17/36 cases had causative variants across 11 genes, including 6 novel EIMFS genes: PCDH19, ALDH7A1, DOCK6, PRRT2, ALG1 and ATP7A. 13/36 patients had ineffective seizure control, 14/36 patients had severe retardation and 6/36 patients died. Of them, the genes for ineffective seizure control, severe retardation or death include KCNT1, SCN2A, SCN1A, ALG1, ATP7A and WWOX. 17 patients had abnormal MRI, of which 8 had ineffective seizure control, 7 had severe retardation and 4 died. 13 patients had hypsarrhythmia, of which 6 had ineffective seizure control, 6 had severe retardation and 2 died. Also, 7 patients had burst suppression, of which 1 had ineffective seizure control, 3 had severe retardation and 3 died. This study is the first to report that ALDH7A1, ATP7A, DOCK6, PRRT2, ALG1, and PCDH19 mutations cause the phenotypic spectrum of EIMFS to expand the genotypic spectrum. The genes KCNT1, SCN2A, SCN1A, ALG1, ATP7A and WWOX may be associated with poor prognosis. The patients presenting with MRI abnormalities, hypsarrhythmia and burst suppression in EEG may be associated with poor prognosis.

P.019 A machine learning approach to asymmetric burst suppression and refractory status epilepticus outcome

Background: Treatment of refractory status epilepticus (RSE) is often titrated to achieve EEG burst suppression. However, optimal burst suppression characteristics are largely unknown. We used an unsupervised machine learning algorithm to predict RSE outcome based on the quantitative burst suppression ratio (QBSR). Methods: We conducted principal component analysis (PCA) as a linear combination of 22 QBSR features from non-anoxic adult RSE patients at the Winnipeg Health Sciences Centre. We also determined the most predictive components that significantly differed between survivors and non-survivors. Results: Using 135,765 QBSRs from 7 survivors and 10 non-survivors, PCA identified a predominantly non-survivor cluster of 8 patients (75% non-survivors). The first 2 PCA components comprised 75% data variance. The most important first component feature was skewness of QBSR distribution in the right or left hemisphere (0.52 each). The most important second component feature was third QBSR quantile of the left hemisphere (0.49). Only right hemispheric QBSR features significantly differed between groups: QBSR skewness for the first component (Benjamini-Hochberg adjusted p=0.038) and average QBSR for the second component (0.32, Benjamini-Hochberg adjusted p=0.046). Conclusions: Our pilot study shows that RSE patient survival may be impacted by QBSR, with differential hemispheric EEG burst suppression characteristics predicting poor RSE outcome.

Instantaneous EEG Signal Analysis Based on Empirical Mode Decomposition Applied to Burst-Suppression in Propofol Anaesthesia

The human electroencephalogram (EEG) constitutes a nonstationary, nonlinear electrophysiological signal resulting from synchronous firing of neurons in thalamocortical structures of the brain. Due to the complexity of the brain's physiological structures and its rhythmic oscillations, analysis of EEG often utilises spectral analysis methods.Aim: to improve clinical monitoring of neurophysiological signals and to further explain basic principles of functional mechanisms in the brain during anaesthesia.Material and methods. In this paper we used Empirical Mode decomposition (EMD), a novel spectral analysis method especially suited for nonstationary and nonlinear signals. EMD and the related Hilbert-Huang Transform (HHT) decompose signal into constituent Intrinsic Mode Functions (IMFs). In this study we applied EMD to analyse burst-suppression (BS) in the human EEG during induction of general anaesthesia (GA) with propofol. BS is a state characterised by cyclic changes between significant depression of brain activity and hyper-active bursts with variable duration, amplitude, and waveform shape. BS arises after induction into deep general anaesthesia after an intravenous bolus of general anaesthetics. Here we studied the behaviour of BS using the burst-suppression ratio (BSR).Results. Comparing correlations between EEG and IMF BSRs, we determined BSR was driven mainly by alpha activity. BSRs for different spectral components (IMFs 1-4) showed differing rates of return to baseline after the end of BS in EEG, indicating BS might differentially impair neural generators of low-frequency EEG oscillations and thalamocortical functional connectivity.Conclusion. Studying BS using EMD represents a novel form of analysis with the potential to elucidate neurophysiological mechanisms of this state and its impact on post-operative patient prognosis.