Identifying risk for status epilepticus with the ADAN scale: a prospective multicenter validation study. / Escala ADAN para la identificación del estado epiléptico: estudio de validación prospectivo y multicéntrico.

OBJECTIVES: Status epilepticus (SE) is a serious event associated with high mortality. This study aims to validate the recently developed ADAN (Abnormal speech, ocular Deviation, Automatisms, and Number of motor epileptic seizures) scale for detecting high risk for SE. MATERIAL AND METHODS: Prospective, multicenter, observational study in adults with suspected epileptic seizures. Consecutive recruitment took place over a 27-month period in 4 hospital emergency departments (EDs). The main endpoint was the proportion of patients with criteria for SE based on the collection and analysis of clinical characteristics and the ADAN scale criteria on arrival at the ED. RESULTS: Of the 527 patients recruited, 203 (38.5%) fulfilled the criteria that predicted SE. Multiple regression analysis demonstrated that the 4 ADAN criteria were the only variables independently associated with a final diagnosis of SE (P .001). The predictive power of the scale was 90.9% (95% CI, 88.4%-93.4%) for a final SE diagnosis. We established 3 risk groups based on ADAN scores: low (score, 0-1: 8.7%), moderate (2, 46.6%), and high (> 2, 92.6%). A cut point of more than 1 had a sensitivity of 88.2% for predicting SE, specificity of 77.8%, positive predictive value of 71.3%, and negative predictive value of 91.3%. CONCLUSION: The ADAN scale is a prospectively validated, simple clinical tool for identifying patients in the ED who are at high risk for SE.

OBJETIVO: El estado epiléptico (EE) es una enfermedad grave con elevada mortalidad. Este estudio tiene como objetivo validar la escala ADAN, propuesta recientemente para identificar pacientes con alto riesgo de desarrollar un EE. METODO: Se realizó un estudio prospectivo, multicéntrico y observacional que incluyó a pacientes adultos con sospecha de crisis epilépticas. Se llevó a cabo un reclutamiento consecutivo durante 27 meses en los servicios de urgencias (SU) de cuatro hospitales. La variable principal fue la proporción de pacientes que cumplían criterios para EE. Se han recopilado y analizado las características clínicas y la puntuación en la escala ADAN a su llegada al SU. RESULTADOS: Se reclutaron 527 pacientes, de los cuales 203 (38,5%) cumplieron criterios de EE. En el análisis de regresión múltiple, se demostró que el habla anormal, la desviación ocular, los automatismos y el número de crisis epilépticas motoras fueron las únicas variables independientemente asociadas con un diagnóstico final de EE (p 0,001). La capacidad predictiva de la escala fue del 90,9% (intervalo de confianza del 95%, 88,4-93,4) para identificar el EE como diagnóstico final. Se establecieron tres grupos de riesgo: bajo (0 1 puntos: 8,7%), moderado (2: 46,6%) y alto (> 2: 92,6%). Una puntuación de corte > 1 punto proporcionó una sensibilidad del 88,2%, especificidad del 77,8%, valor predictivo positivo del 71,3% y valor predictivo negativo del 91,3% para predecir el EE. CONCLUSIONES: La escala ADAN es una herramienta clínica simple y validada de manera prospectiva para identificar, en los SU, a los pacientes con elevado riesgo de EE.

Exploratory study of blood biomarkers in patients with post-stroke epilepsy.

INTRODUCTION: In addition to clinical factors, blood-based biomarkers can provide useful information on the risk of developing post-stroke epilepsy (PSE). Our aim was to identify serum biomarkers at stroke onset that could contribute to predicting patients at higher risk of PSE. PATIENTS AND METHODS: From a previous study in which 895 acute stroke patients were followed-up, 51 patients developed PSE. We selected 15 patients with PSE and 15 controls without epilepsy. In a biomarker discovery setting, 5 Olink panels of 96 proteins each, were used to determine protein levels. Biomarkers that were down-regulated and overexpressed in PSE patients, and those that showed the strongest interactions with other proteins were validated using an enzyme-linked immunosorbent assay in samples from 50 PSE patients and 50 controls. A ROC curve analysis was used to evaluate the predictive ability of significant biomarkers to develop PSE. RESULTS: Mean age of the PSE discovery cohort was 68.56 ± 15.1, 40% women and baseline NIHSS 12 [IQR 1-25]. Nine proteins were down-expressed: CASP-8, TNFSF-14, STAMBP, ENRAGE, EDA2R, SIRT2, TGF-alpha, OSM and CLEC1B. VEGFa, CD40 and CCL4 showed greatest interactions with the remaining proteins. In the validation analysis, TNFSF-14 was the single biomarker showing statistically significant downregulated levels in PSE patients (p = 0.006) and it showed a good predictive capability to develop PSE (AUC 0.733, 95% CI 0.601-0.865). DISCUSSION AND CONCLUSION: Protein expression in PSE patients differs from that of non-epileptic stroke patients, suggesting the involvement of several different proteins in post-stroke epileptogenesis. TNFSF-14 emerges as a potential biomarker for predicting PSE.

Epileptiform electroencephalogram discharges increase seizure recurrence risk in patients with acute symptomatic seizure due to a structural brain lesion.

OBJECTIVE: The risk factors for seizure recurrence after acute symptomatic seizure due to a structural brain lesion are not well established. The aim of this study was to analyze possible associations between demographic, clinical, and electroencephalographic variables and epilepsy development in patients with acute symptomatic seizure due to an acute structural brain lesion. METHODS: We designed an observational prospective study of patients with acute symptomatic seizure due to an acute structural brain lesion (hemorrhagic stroke, ischemic stroke, traumatic brain injury, or meningoencephalitis) who underwent EEG during their initial admission between January 2015 and January 2020. We analyzed prospectively recorded demographic, clinical, electroencephalographic (EEG), and treatment-related variables. All variables were compared between patients with and without seizure recurrence during 2 years of follow up. RESULTS: We included 194 patients (41.2 % women; mean [SD] age, 57.3 [15.8] years) with acute symptomatic seizure due to an acute structural brain lesion. They all underwent EEG during admission and were followed for at least 2 years. The identifiable causes were hemorrhagic stroke (44.8 %), ischemic stroke (19.5 %), traumatic brain injury (18.5 %), and meningoencephalitis (17 %). Fifty-six patients (29 %) experienced a second seizure during follow-up. Seizure recurrence was associated with epileptiform discharges on EEG (52% vs 32 %; OR 2.3 [95 % CI, 1.2-4.3], p = 0.008) and onset with status epilepticus (17% vs 0.05 %, OR 4.03 [95 % CI 1.45-11.2], p = 0.009). CONCLUSIONS: Epileptiform discharges on EEG and status epilepticus in patients with acute symptomatic seizure due to an acute structural brain lesion are associated with a higher risk of epilepsy development.

Psychogenic nonepileptic seizures: The effect of accurate diagnosis on cognition.

OBJECTIVE: Much remains to be elucidated about the cognitive profile of patients with psychogenic nonepileptic seizures (PNES) and about how this changes over time and compares to that of patients with epilepsy. The aim of this study was to study the neuropsychological profile of patients with PNES and an age-matched group of patients with temporal lobe epilepsy (TLE) during admission to a video electroencephalography monitoring unit (VEMU) and 1 year after discharge. METHODS: Patients diagnosed with PNES or TLE at a VEMU were prospectively recruited. Neuropsychological, demographic, clinical, and treatment variables were collected at baseline and 1 year. To minimize multiple comparisons, scores from different cognitive tests were computed for attention and psychomotor speed, verbal memory, visual memory, language, and executive function. A global cognitive impairment index (GCII) was also created. Post hoc analyses were conducted to identify clinical variables that might mediate the differences observed in cognition over time between the groups. These included seizure frequency, number of antiseizure medication (ASM), number of psychotropic drugs, depression, and quality of life. RESULTS: We studied 24 patients with PNES and 24 patients with TLE. The groups performed similarly in the baseline neuropsychological tests. There was a significant time (baseline to 1-year follow-up) by group (PNES vs TLE) interaction for the GCII (p = 0.006), language (p = 0.04), and executive function (p = 0.013), with PNES patients showing improvement and TLE patients remaining stable. The time by group interaction for attention and psychomotor speed showed a trend toward significance (p = 0.056), Reduction in number of ASM was associated with improved cognition in PNES patients at 1 year. CONCLUSION: PNES patients showed improved cognition at 1 year of follow-up, particularly in language and executive functions. This finding shows the potential benefits of an early, accurate diagnosis, which range from improved cognition to better management.

Blood biomarkers predictive of epilepsy after an acute stroke event.

OBJECTIVE: Blood biomarkers have not been widely investigated in poststroke epilepsy. In this study, we aimed to describe clinical factors and biomarkers present during acute stroke and analyze their association with the development of epilepsy at long term. METHODS: A panel of 14 blood biomarkers was evaluated in patients with ischemic and hemorrhagic stroke. Biomarkers were normalized and standardized using Z-scores. Stroke and epilepsy-related variables were also assessed: stroke severity, determined by National Institutes of Health Stroke Scale (NIHSS) score, stroke type and cause, time from stroke to onset of late seizures, and type of seizure. Multiple Cox regression models were used to identify clinical variables and biomarkers independently associated with epilepsy. RESULTS: From a cohort of 1115 patients, 895 patients were included. Mean ± standard deviation (SD) age was 72.0 ± 13.1 years, and 57.8% of patients were men. Fifty-one patients (5.7%) developed late seizures, with a median time to onset of 232 days (interquartile range [IQR] 86-491). NIHSS score ≥8 (P < .001, hazard ratio [HR] 4.013, 95% confidence interval [CI] 2.123-7.586) and a history of early onset seizures (P < .001, HR 4.038, 95% CI 1.802-9.045) were factors independently associated with a risk of developing epilepsy. Independent blood biomarkers predictive of epilepsy were high endostatin levels >1.203 (P = .046, HR 4.300, 95% CI 1.028-17.996) and low levels of heat shock 70 kDa protein-8 (Hsc70) <2.496 (P = .006, HR 3.795, 95% CI 1.476-9.760) and S100B <1.364 (P = .001, HR 2.955, 95% CI 1.534-5.491). The risk of epilepsy when these biomarkers were combined increased to 17%. The area under the receiver-operating characteristic (ROC) curve of the predictive model was stronger when clinical variables were combined with blood biomarkers (74.3%, 95% CI 65.2%-83.3%) than when they were used alone (68.9%, 95% CI 60.3%-77.6%). SIGNIFICANCE: Downregulated S100B and Hsc70 and upregulated endostatin may assist in prediction of poststroke epilepsy and may provide additional information to clinical risk factors. In addition, these data are hypothesis-generating for the epileptogenic process.

Clinical predictors of adverse events during continuous video-EEG monitoring in an epilepsy unit.

Patients admitted to epilepsy monitoring units (EMUs) for diagnostic and presurgical evaluation have an increased risk of seizure-related injury, particularly in the many cases in which medication is withdrawn. The purpose of this study was to assess the prevalence of adverse events (AEs) in this setting and to analyse associated clinical factors and costs. We evaluated consecutive patients admitted to an EMU at a tertiary care hospital over a 10-year period based on a descriptive, longitudinal study. We analysed the occurrence of AEs (traumatic injury, psychiatric complications, status epilepticus, cardiorespiratory disturbances, and death), investigated potential risk factors using univariate and multivariate logistic regression analysis, and compared admission costs between patients with and without AEs. In total, 411 EMU admissions were studied corresponding to 352 patients (55% women; mean [SD] age: 41.7 [12.1] years). Twenty-five patients (6%) experienced an AE. The most common event was traumatic injury (n=9), followed by status epilepticus (n=8), psychiatric complications (n=7), and cardiorespiratory disturbances (n=1). On comparing patients with and without AEs, we observed that the former were more likely to experience generalized seizures (OR: 7.81; 95% CI: 3.51-12.23; p<0.001) or have more seizures overall during admission (OR: 3.2; 95% CI: 1.42-6.8; p=0.002). Patients with AEs also had longer EMU stays (6.91 [2.64] vs 5.08 [1.1]; p=0.004), longer hospital stays (8.45 [3.6] vs 5.18 [1.2]; p<0.001), and higher costs (€7277.71 [€2743.9] vs €5175.7 [€1182.5]; p<0.001). Patients with generalized seizures and more seizures during admission were at greater risk of AEs, which were associated with higher admission costs.

Correlation of blood biomarkers with early-onset seizures after an acute stroke event.

INTRODUCTION: Blood biomarkers have not been widely studied in stroke-related seizures. In this study, we aimed to describe clinical factors and biomarkers present during acute stroke and to analyze their association with early-onset seizures. METHODS: We retrospectively evaluated a panel of 14 blood biomarkers in 1115 patients with ischemic and hemorrhagic stroke. Biomarkers were normalized and standardized using Z scores. We also recorded stroke and epilepsy-related variables, including stroke severity (National Institute of Health Stroke Scale [NIHSS] scores), type, and causes, time from onset of stroke to occurrence of early seizures, and type of seizure. Adjusted logistic regression models were built to identify clinical variables and biomarkers independently associated with early seizures. RESULTS: Mean ±â€¯standard deviation (SD) age was 72.3 ±â€¯13.2 years, and 56.8% of the patients were men. Thirty-eight patients (3.9%) developed early seizures with a median time to onset of 1 day (interquartile range (IQR), 0-4). A higher NIHSS score (odds ratio [OR] = 1.046; 95% confidence interval (CI): 1.001-1.094; p = 0.044) and hemorrhagic stroke (OR = 2.133; 95% CI: 1.010-4.504; p = 0.047) were independently associated with a greater risk of early seizures. Independent blood biomarkers predictive of early seizures were lower levels of tumor necrosis factor receptor 1 (TNF-R1) (<0.013) (p = 0.006; OR = 3.334; 95% CI: 1.414-7.864) and higher levels of neural cell adhesion molecule (NCAM) (>0.326) (p = 0.009; OR = 2.625; 95% CI: 1.271-5.420). The predictive power of the regression model was greater when clinical variables were combined with blood biomarkers (73.5%; 95% CI: 65.1%-81.9%) than when used alone (64%; 95% CI: 55%-72.9%). CONCLUSION: Higher NCAM and lower TNF-R1 levels may help predict the occurrence of early seizures. The combined use of these biomarkers and clinical variables could be useful for identifying patients at risk of seizures. This article is part of the Special Issue "Seizures & Stroke".

Clinical Outcome of Ischemic Stroke in Old Patients Versus Oldest-Old.

BACKGROUND: It is unclear whether very old patients benefit from stroke unit. The aim of our work was to compare the clinical outcome of patients with ischemic stroke aged either 70 or 80 (G 1) versus oldest-old greater than or equal to 81 years (G 2). METHODS: Of 1187 patients admitted with stroke during 5 years in our stroke unit, we included 252 patients with independent functional status (modified Rankin scale, [mRS] ≤ 2) before the stroke. All patients underwent clinical examination, blood test, electrocardiography, brain imaging, and cerebrovascular ultrasound. Clinical outcome was assessed with the mRS and National Institutes of Health Stroke Scale (NIHSS) at discharge. We considered favorable outcome mRS 0-2 at discharge. RESULTS: Of 252 patients included, 55% were male, 150 (59.5%) patients belonged to G1 and 102 (40.5%) G2. We detected a significant increase of atrial fibrillation, bronchoaspiration, mortality, higher NIHSS at admission, and worse functional status at discharge in G2. No significant differences in other demographic, vascular risk factors, hospital stay, NIHSS at discharge or subtype of stroke were found. NIHSS at discharge was the only independent predictor of good functional status (odds ratio 0.4; 95% confidence interval, 0.3-0.6; P < .001). CONCLUSIONS: Oldest-old patients showed similar NIHSS at discharge than younger patients despite having higher neurological severity at admission. Our results support the hypothesis that oldest-old patients have good recovery potential, and should not be excluded from the stroke unit. The worse functional status detected at discharge in these patients could be attributed to others factors and not to neurological severity.

Refractory status epilepticus: Impact of baseline comorbidity and usefulness of STESS and EMSE scoring systems in predicting mortality and functional outcome.

PURPOSE: Little has been published on the prognostic value of the Status Epilepticus Severity Score (STESS) or the Epidemiology-based Mortality score in Status Epilepticus (EMSE) in refractory status epilepticus (RSE). We sought to analyze the prognostic value of STESS and EMSE and the impact of baseline comorbidities in mortality and functional outcome in RSE. METHODS: We designed an observational retrospective study of patients diagnosed with RSE between August 2013 and September 2017. For each patient, we analyzed prospectively recorded demographic, clinical, comorbidity, electroencephalographic, treatment, and hospital stay-related data and calculated STESS and EMSE. All variables were compared statistically between patients with good and poor functional outcome at discharge and between patients who died in hospital and those who were alive at discharge. RESULTS: Fourty-nine patients had RSE; 35.4% died in hospital and 88% showed functional decline at discharge. Mortality was associated with baseline chronic kidney disease (CKD) (OR 19.25, p = 0.006), baseline modified Rankin scale score (mRS) (OR 3.38, p = 0.005), non-convulsive status epilepticus (NCSE) with coma (OR 11.9, p = 0.04), STESS (OR 2, p = 0.04), and EMSE (OR 1.3, p = 0.02). Functional outcome was associated with baseline mRS (OR 13.9, p = 0.02), and EMSE (OR 1.3, p = 0.02). The optimal cutoff scores for predicting mortality were 4 for STESS and 60 for EMSE. EMSE predicted functional outcome with an optimal cutoff of 40. CONCLUSIONS: CKD, NCSE with coma and STESS were associated with mortality. mRS and EMSE were associated with mortality and functional outcome. EMSE was useful for predicting functional outcome, while EMSE and STESS were useful for predicting in-hospital mortality.

Diagnostic Yield of Routine Electroencephalography With Concurrent Video Recording in Detecting Interictal Epileptiform Discharges in Relation to Reasons for Request: A Prospective Study of 1,080 Video-Electroencephalograms.

PURPOSE: The aim of this study was to prospectively analyze the sensitivity and specificity of routine electroencephalography with concurrent video recording (vEEG) in relation to the reasons for requesting the test and to investigate when routine vEEG should be requested. METHODS: We prospectively analyzed 1,080 consecutive vEEGs performed between April 2015 and April 2016. The requests for vEEG were classified as requests with a low suspicion of epilepsy (syncope, confusion or delirium, suspicion of psychogenic nonepileptic seizures, and paroxysmal focal neurological deficit) or requests with a high suspicion of epilepsy (first clinical seizure, suspected status epilepticus, follow-up study of a patient with epilepsy, and acute symptomatic seizures). Predominant vEEG findings (ictal and interictal epileptiform activities, diffuse, or focal slowing and triphasic waves) were analyzed, and sensitivity and specificity [ZERO WIDTH SPACE][ZERO WIDTH SPACE]values calculated. RESULTS: The most common indication for vEEG was a follow-up study of patients with epilepsy (38%), followed by first clinical seizure (19.3%) and suspected status epilepticus (11%). The respective specificity and sensitivity values were 93% and 58% for 235 vEEGs performed in children/adolescents (≤18 years), 95% and 40% for 533 vEEGs performed in adults (>18 ≤ 65 years), and 93% and 39% for 312 vEEGs performed in older adults (>65 years). Twenty-four patients with false-positive paroxysms had a clinical diagnosis of confusional state or paroxysmal focal neurological deficit. Neurologists and neuropediatricians with experience in managing epilepsy had higher specificity values than general neurologists or physicians (P = 0.012). CONCLUSIONS: In our series, vEEG abnormalities were mainly observed in patients with clinical findings highly suggestive of epilepsy. In confusional states, and paroxysmal focal neurological deficit vEEG could be indicated.

Importance of neuropsychological and clinical features to predict seizure control in medically treated patients with mesial temporal epilepsy and hippocampal sclerosis.

OBJECTIVE: It is not yet understood why seizures in certain patients with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) develop resistance to antiepileptic drugs (AEDs) while others achieve good seizure control with this treatment. We analyzed clinical and neuropsychological features associated with seizure control in patients with MTLE-HS who had not undergone resective surgery. METHODS: We enrolled 40 patients with medically treated MTLE-HS and retrospectively collected the following data from prospective databases: sex, febrile seizures, central nervous system infection, history of head trauma, cognitive impairment, psychiatric disturbances, history of status epilepticus, age at onset of epilepsy, aura, seizure type and frequency, electroencephalography abnormalities, HS side, AEDs, global cognitive status, and neuropsychological functions such as cognitive processing speed, attention and executive functions, verbal and visual memory, language, and visuospatial ability. These factors were compared between patients who achieved seizure control (no seizures or a >50% reduction in seizure frequency) with AED treatment and those who continued with poor seizure control (increase or no change in frequency or <50% reduction) after starting treatment. RESULTS: The factors associated with poor seizure control in the multivariate analysis were >2seizures per month before treatment (odds ratio [OR] 3.2, 95% confidence interval [CI] 1.2-4.8, p=0.04), moderate or severe cognitive impairment (OR 2.1, 95% CI 1.8-7.6, p=0.02), and impairment of >2 neuropsychological functions (OR 2.88, 95% CI 2-6.6, p=0.04). No associations were observed between poor seizure control and specific neuropsychological function impairment. CONCLUSIONS: Poor seizure control in MTLE-HS is associated with moderate-severe cognitive impairment but not with a specific profile of impairment. Recognizing poor prognostic features such as a high frequency of monthly seizures prior to starting AED treatment could help to identify patients with medically intractable MTLE-HS who may be good candidates for early epilepsy surgery.