کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
340531 548323 2015 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Development and validation of a seizure prediction model in critically ill children
ترجمه فارسی عنوان
توسعه و اعتبار یک مدل پیش بینی تشنج در کودکان مبتلا به بیماری های شدید
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب رفتاری
چکیده انگلیسی


• Aimed to predict electrographic seizures in critically ill children.
• We developed and validated an electrographic seizure prediction model.
• Model has fair to good discrimination ability and overall performance.
• At the optimal cut-off point, sensitivity of 59% and a specificity of 81%.
• Varied cut-off points could optimize sensitivity or specificity.

PurposeElectrographic seizures are common in encephalopathic critically ill children, but identification requires continuous EEG monitoring (CEEG). Development of a seizure prediction model would enable more efficient use of limited CEEG resources. We aimed to develop and validate a seizure prediction model for use among encephalopathic critically ill children.MethodWe developed a seizure prediction model using a retrospectively acquired multi-center database of children with acute encephalopathy without an epilepsy diagnosis, who underwent clinically indicated CEEG. We performed model validation using a separate prospectively acquired single center database. Predictor variables were chosen to be readily available to clinicians prior to the onset of CEEG and included: age, etiology category, clinical seizures prior to CEEG, initial EEG background category, and inter-ictal discharge category.ResultsThe model has fair to good discrimination ability and overall performance. At the optimal cut-off point in the validation dataset, the model has a sensitivity of 59% and a specificity of 81%. Varied cut-off points could be chosen to optimize sensitivity or specificity depending on available CEEG resources.ConclusionDespite inherent variability between centers, a model developed using multi-center CEEG data and few readily available variables could guide the use of limited CEEG resources when applied at a single center. Depending on CEEG resources, centers could choose lower cut-off points to maximize identification of all patients with seizures (but with more patients monitored) or higher cut-off points to reduce resource utilization by reducing monitoring of lower risk patients (but with failure to identify some patients with seizures).

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Seizure - Volume 25, February 2015, Pages 104–111
نویسندگان
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