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

Struck, Aaron F.; Ustun, Berk; Ruiz, Andres Rodriguez; Lee, Jong Woo; LaRoche, Suzette M.; Hirsch, Lawrence J.; Gilmore, Emily J.; Vlachy, Jan; Haider, Hiba A.; Rudin, Cynthia; Westover, M. Brandon

doi:10.1001/jamaneurol.2017.2459

Cited by 123 publications

(140 citation statements)

References 24 publications

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“…A seizure prediction model designed in neonates incorporating EEG background features with clinical data improved ES prediction over a model employing clinical variables alone . Our finding that the presence of epileptiform discharges predicted ES is also supported by other studies in critically ill children and adults . Overall, the consistency between the predictors of ES identified in this study and those identified by other groups suggest validity and generalizability of this model.…”

Section: Discussionsupporting

confidence: 85%

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Development of a model to predict electroencephalographic seizures in critically ill children

et al. 2020

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Objective Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but ES identification with continuous electroencephalography (EEG) monitoring (CEEG) is resource‐intense. We aimed to develop an ES prediction model that would enable clinicians to stratify patients by ES risk and optimally target limited CEEG resources. We aimed to determine whether incorporating data from a screening EEG yielded better performance characteristics than models using clinical variables alone. Methods We performed a prospective observational study of 719 consecutive critically ill children with acute encephalopathy undergoing CEEG in the pediatric intensive care unit of a quaternary care institution between April 2017 and February 2019. We identified clinical and EEG risk factors for ES. We evaluated model performance with area under the receiver‐operating characteristic (ROC) curve (AUC), validated the optimal model with the highest AUC using a fivefold cross‐validation, and calculated test characteristics emphasizing high sensitivity. We applied the optimal operating slope strategy to identify the optimal cutoff to define whether a patient should undergo CEEG. Results The incidence of ES was 26%. Variables associated with increased ES risk included age, acute encephalopathy category, clinical seizures prior to CEEG initiation, EEG background, and epileptiform discharges. Combining clinical and EEG variables yielded better model performance (AUC 0.80) than clinical variables alone (AUC 0.69; P < .01). At a 0.10 cutoff selected to emphasize sensitivity, the optimal model had a sensitivity of 92%, specificity of 37%, positive predictive value of 34%, and negative predictive value of 93%. If applied, the model would limit 29% of patients from undergoing CEEG while failing to identify 8% of patients with ES. Significance A model employing readily available clinical and EEG variables could target limited CEEG resources to critically ill children at highest risk for ES, making CEEG‐guided management a more viable neuroprotective strategy.

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Section: Discussionsupporting

confidence: 85%

“…47 Our finding that the presence of epileptiform discharges predicted ES is also supported by other studies in critically ill children 2,5,6,12,17 and adults. 46,48,49 Overall, the consistency between the predictors of ES identified in this study and those identified by other groups suggest validity and generalizability of this model.…”

Section: Discussionsupporting

confidence: 74%

Development of a model to predict electroencephalographic seizures in critically ill children

et al. 2020

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“…сание ЭЭГ, увеличив уровень межэкспертного согласия и провести ряд кооперативных исследований, выявивших клиническую значимость отдельных паттернов [16,17].…”

Section: рис 1 схема протокола ээг у пациента с угнетением уровня бunclassified

“…По шкале 2HELP2B, оценивающей риск развития приступа по данным ЭЭГ, кратковременные иктальные ритмичные разряды соответствуют 2 баллам; ЛПР или ЛРДА или БиПР -2; эпилептический приступ до начала регистрации ЭЭГ -1; спорадические ЭР -1; частота графоэлементов в РПП выше 2 Гц -1; наличие любого модификатора «+» -1 балл. Риск приступа для 0 баллов составляет 5%, для 1 балла -12%, 2 баллов -27%, 3 баллов -50%, 4 баллов -73%, 5 баллов -88%, 6 и 7 баллов -95% [35]. Второй способ -использование шкалы ГПР, которая включает эпилепсию в анамнезе (1 балл), фокальные изменения на ЭЭГ (2 балла), отсутствие трехфазной морфологии ПР (3 балла) [36].…”

Section: прогнозирование судорожного и бессудорожного эпилептическогоunclassified

Rhythmic and periodic EEG patterns. Classification and clinical significance.

Синкин¹,

Krylov²

2018

Z. nevrol. psikhiatr. im. S.S. Korsakova

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“…2 Depending on various waveform characteristics, acute electrographic seizures are noted in 12%-58% of patients with PPs on cEEG. 2 Above findings along with a recently proposed cEEG-based risk scoring model for seizures during hospitalization 4 have vastly improved our understanding of acute impact of PPs. However, their impact on long-term, posthospitalization clinical outcomes is largely unknown.…”

mentioning

confidence: 97%

New onset epilepsy among patients with periodic discharges on continuous electroencephalographic monitoring

et al. 2018

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Patients with LPDs on cEEG during critical illness are at least seven times more likely to develop epilepsy compared to patients with NP/NE findings. This risk is further increased if patients with LPDs have electrographic seizures. In comparison, the presence of GPDs does not seem to impact the risk for developing epilepsy. cEEG findings at the time of acute insult have potential to serve as prognostic biomarkers for epilepsy development.

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

Cited by 123 publications

References 24 publications

Development of a model to predict electroencephalographic seizures in critically ill children

Development of a model to predict electroencephalographic seizures in critically ill children

Rhythmic and periodic EEG patterns. Classification and clinical significance.

New onset epilepsy among patients with periodic discharges on continuous electroencephalographic monitoring

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