Detection Of Nonconvulsive Status Epilepticus Research Articles

Epileptic seizures and epilepsy in children after stroke

Despite the low prevalence of cerebrovascular diseases in childhood, up to 60% of children realize acute symptomatic epileptic seizures and up to 30% of patients develop post-stroke epilepsy in the long-term period. The lack of a unified terminology and temporal criteria for both acute symptomatic epileptic seizures and post-stroke epilepsy complicates the analysis of the studied groups. Many studies are limited to small groups, proprietary terminology, different age medians, and inclusion criteria. Both clinical and instrumental risk factors for the development of post-stroke epilepsy in childhood have not been identified, which makes it difficult to identify risk groups and predict the outcome in the long term period. The only significant risk factor in most publications is «younger age», but its boundaries are blurred. Most studies are retrospective, which may affect the incidence and type of epileptic seizures in acute cerebrovascular accident. Electroencephalography is the most informative method for detecting subclinical changes and identifying risk groups. Continuous electroencephalography is effective primarily in the detection of non-convulsive status epilepticus. Currently, there are no unified protocols for examining stroke patients, both at the onset and in follow-up. The search for articles was carried out in the scientific platforms PubMed, Google Scholar, eLIBRARY.

The diagnostic value of continuous EEG for the detection of non-convulsive status epilepticus in neurosurgical patients – A prospective cohort study

ObjectiveTo prospectively compare the diagnostic yields of standard EEG and continuous EEG (cEEG) monitoring for the diagnosis of non-convulsive status epilepticus (NCSE) in neurosurgical patients in the intensive care unit. MethodsWe included 50 consecutive patients with clinical suspicion of NCSE due to unexplained coma or subtle clinical phenomena such as discrete myoclonus. The initial 30-minute EEG recording and the following cEEG were analyzed separately for seizure activity. Data were collected on neurosurgical diagnosis, previous diagnosis of epilepsy, current medication, level of consciousness, and outcome at discharge from the neurosurgical department. ResultsRecurrent electrographic seizure activity was detected in five patients. This was within the first 30 mins for three patients and on the following cEEG for two patients. Antiepileptic treatment had been initiated in three of these patients. Most of the 50 patients had severe newly acquired neurological disability at discharge. ConclusionsThe prospective finding of a 10% seizure incidence was lower than reports from retrospective studies. SignificanceUse of cEEG led to detection of seizure activity in 2 of 50 patients (4%) and was thus a low-yield method in neurosurgical patients with suspicion of NCSE. Specific markers for patient selection for cEEG are needed.

Early Epileptiform Discharges and Clinical Signs Predict Nonconvulsive Status Epilepticus on Continuous EEG.

Critical care continuous electroencephalography (CCEEG) represents the gold standard for detection of nonconvulsive status epilepticus (NCSE) in neurological critical care patients. It is unclear which findings on short-term routine EEG and which clinical parameters predict NCSE during subsequent CCEEG reliably. The aim of the present study was to assess the prognostic significance of changes within the first 30min of EEG as well as of clinical parameters for the occurrence of NCSE during subsequent CCEEG. Systematic analysis of the first 30min and the remaining segments of prospective CCEEG recordings according to the ACNS Standardized Critical Care EEG Terminology and according to recently proposed NCSE criteria as well as review of clinical parameters of 85 consecutive neurological critical care patients. Logistic regression and binary classification tests were used to determine the most useful parameters within the first 30min of EEG predicting subsequent NCSE. The presence of early sporadic epileptiform discharges (SED) and early rhythmic or periodic EEG patterns of "ictal-interictal uncertainty" (RPPIIIU) (OR 15.51, 95% CI 2.83-84.84, p = 0.002) and clinical signs of NCS (OR 18.43, 95% CI 2.06-164.62, p = 0.009) predicted NCSE on subsequent CCEEG. Various combinations of early SED, early RPPIIIU, and clinical signs of NCS showed sensitivities of 79-100%, specificities of 49-89%, and negative predictive values of 95-100% regarding the incidence of subsequent NCSE (p < 0.001). Early SED and early RPPIIIU within the first 30min of EEG as well as clinical signs of NCS predict the occurrence of NCSE during subsequent CCEEG with high sensitivity and high negative predictive value and may be useful to select patients who should undergo CCEEG.

Assessment of hairline EEG as a screening tool for nonconvulsive status epilepticus: Response to Bubrick et al.

To the Editors: We thank Dr. Bubrick and her colleagues for interest in our paper. In their letter to Epilepsia (Bubrick et al., 2007), they raise some interesting points for discussion. Overall, they disagree with our conclusions because they feel the sensitivity and specificity for detection of nonconvulsive status epilepticus (NCSE) with a hairline electroencephalogram (EEG) is adequate, and that waiting to obtain a standard EEG will needlessly delay treatment decisions. Moreover, they routinely use this practice and have found it useful (Milligan & Bromfield, 2005). Since the impetus for our study was the delay in obtaining urgent and emergent EEG, we agree that a more rapid assessment would be preferable. Bubrick et al. suggest that the sensitivity and specificity of hairline EEG is adequate for this purpose. In our study, the sensitivity for detecting electrographic seizures was 72% and for periodic lateralized epileptiform discharges (PLED) was only 54% (Kolls & Husain, 2007). Certainly the detection of PLED is very important, since in certain situations it may represent an ictal or interictal pattern. Additionally, a review of the misinterpretations reveals that 33 seizure and 42 PLED patterns were misinterpreted as diffuse slowing (see Figs 3 and 4 in Kolls & Husain, 2007). Moreover, seven patterns with diffuse slowing were misinterpreted as seizures, PLED, or generalized periodic epileptiform discharges (GPED). These are significant errors that may gravely impact patient care. It should also be noted that samples in our study were interpreted by experience neurophysiologists; interpretation by neurology residents resulted in even lower sensitivities and specificities (unpublished data). Consequently, we maintain that a hairline EEG is inadequate for diagnosing NCSE. Bubrick et al. note that patterns of NCSE were not studied. The patterns that were selected, such as electrographic seizures, PLED, and GPED, were not random, and are very likely to be present in patients in NCSE. Identification of these patterns would warrant an emergent EEG and possibly treatment of NCSE. Knowledge of the clinical situation, while always helpful, would be only of marginal additional utility. Bubrick et al. also noted that they have taught the hairline EEG technique to their residents and have found it useful (Milligan & Bromfield, 2005). Unfortunately, the reference cited to support this claim of usefulness includes a single patient who was found to be in NCSE with a hairline EEG. We do not dispute that for the occasional patient, hairline EEG may reveal the correct diagnosis. However, anecdotal studies and case reports cannot establish the utility of this technique. Indeed, it was our belief that this technique would be adequate for and expedite the diagnosis of NCSE, However, our data has shown otherwise. Bubrick et al. are encouraged to evaluate their data in a prospective manner and publish their results. Emergent EEG is time consuming and not universally available. Though quicker and easier to obtain, hairline EEG does not appear to be an adequate substitute. Further research on this and other alternatives is needed to determine the best method for quickly and accurately determining whether a patient is in NCSE.