Título : |
StatNet electroencephalogram: a fast and reliable option to diagnose nonconvulsive status epilepticus in emergency setting |
Tipo de documento : |
documento electrónico |
Autores : |
Lady Diana Ladino Malagón, |
Fecha de publicación : |
2015 |
Títulos uniformes : |
Canadian Journal of Neurological Sciences
|
Idioma : |
Inglés (eng) |
Palabras clave : |
disposable EEG electrodes emergency department emergent EEG intensive care unit nonconvulsive status epilepticus stat EEG |
Resumen : |
Background: The StatNet electrode set is a system that can be applied by a non-electroencephalogram (EEG) technologist after minimal training. The primary objectives of this study are to assess the quality and reliability of the StatNet recordings in comparison to the conventional EEG. Methods: Over 10 months, 19 patients with suspected nonconvulsive status epilepticus were included from university hospital emergency settings. Each patient received a StatNet EEG by a trained epilepsy fellow and a conventional EEG by registered technologists. We compared the studies in a blinded fashion, for the timeframe from EEG order to the setup time, start of acquisition, amount of artifact, and detection of abnormalities. The nonparametric Mann-Whitney two-sample t test was used for comparisons. The kappa score was used to assess reliability. Results: Mean age of patients was 61±16.3 (25-93) years. The inter-observer agreement for detection of abnormal findings was 0.83 for StatNet and 0.75 for conventional EEG. Nonconvulsive status epilepticus was detected in 10% (2/19) in both studies. The delay from the time of EEG requisition to acquisition was shorter in the StatNet (22.4±2.5 minutes) than the conventional EEG (217.7±44.6 minutes; p |
Mención de responsabilidad : |
Lady Diana Ladino, Alexandra Voll, Dianne Dash, Wes Sutherland, Lizbeth Hernández-Ronquillo, Jose Francisco Téllez-Zenteno, Farzad Moien-Afshari |
Referencia : |
Can J Neurol Sci. 2016 Mar;43(2):254-60. |
DOI (Digital Object Identifier) : |
10.1017/cjn.2015.391 |
PMID : |
26864547 |
En línea : |
https://www.cambridge.org/core/journals/canadian-journal-of-neurological-science [...] |
Enlace permanente : |
https://hospitalpablotobon.cloudbiteca.com/pmb/opac_css/index.php?lvl=notice_display&id=4580 |
StatNet electroencephalogram: a fast and reliable option to diagnose nonconvulsive status epilepticus in emergency setting [documento electrónico] / Lady Diana Ladino Malagón, . - 2015. Obra : Canadian Journal of Neurological SciencesIdioma : Inglés ( eng) Palabras clave : |
disposable EEG electrodes emergency department emergent EEG intensive care unit nonconvulsive status epilepticus stat EEG |
Resumen : |
Background: The StatNet electrode set is a system that can be applied by a non-electroencephalogram (EEG) technologist after minimal training. The primary objectives of this study are to assess the quality and reliability of the StatNet recordings in comparison to the conventional EEG. Methods: Over 10 months, 19 patients with suspected nonconvulsive status epilepticus were included from university hospital emergency settings. Each patient received a StatNet EEG by a trained epilepsy fellow and a conventional EEG by registered technologists. We compared the studies in a blinded fashion, for the timeframe from EEG order to the setup time, start of acquisition, amount of artifact, and detection of abnormalities. The nonparametric Mann-Whitney two-sample t test was used for comparisons. The kappa score was used to assess reliability. Results: Mean age of patients was 61±16.3 (25-93) years. The inter-observer agreement for detection of abnormal findings was 0.83 for StatNet and 0.75 for conventional EEG. Nonconvulsive status epilepticus was detected in 10% (2/19) in both studies. The delay from the time of EEG requisition to acquisition was shorter in the StatNet (22.4±2.5 minutes) than the conventional EEG (217.7±44.6 minutes; p |
Mención de responsabilidad : |
Lady Diana Ladino, Alexandra Voll, Dianne Dash, Wes Sutherland, Lizbeth Hernández-Ronquillo, Jose Francisco Téllez-Zenteno, Farzad Moien-Afshari |
Referencia : |
Can J Neurol Sci. 2016 Mar;43(2):254-60. |
DOI (Digital Object Identifier) : |
10.1017/cjn.2015.391 |
PMID : |
26864547 |
En línea : |
https://www.cambridge.org/core/journals/canadian-journal-of-neurological-science [...] |
Enlace permanente : |
https://hospitalpablotobon.cloudbiteca.com/pmb/opac_css/index.php?lvl=notice_display&id=4580 |
| |