scholarly journals Quantitative burst suppression on serial intermittent EEG in refractory status epilepticus

Author(s):  
Joseph Peedicail ◽  
Neil Mehdiratta ◽  
Shenghua Zhu ◽  
Paulina Nedjadrasul ◽  
Marcus C. Ng
Seizure ◽  
2019 ◽  
Vol 64 ◽  
pp. 41-44 ◽  
Author(s):  
Alvin S. Das ◽  
Jong Woo Lee ◽  
Saef Izzy ◽  
Henrikas Vaitkevicius

2018 ◽  
Vol 32 (2) ◽  
pp. 228-230 ◽  
Author(s):  
Robert H. Witcher ◽  
Michelle M. Ramirez

Purpose: Drug reaction with eosinophilia and systemic symptoms (DRESS) is associated with antiepileptic drug use and is a rare but life-threatening side effect. We present a case of phenobarbital-induced DRESS in a patient who subsequently required phenobarbital and was successfully desensitized. Summary: A 5-year-old male presented with medically refractory status epilepticus (SE). He had been trialed on several antiepileptic medications without achieving burst suppression. Burst suppression was achieved with a pentobarbital infusion, and thus, phenobarbital was initiated as the pentobarbital was weaned. After five days of phenobarbital, the patient developed signs and symptoms concerning for DRESS; a punch biopsy confirmed the drug reaction. Two months later, he again developed SE unresponsive to antiepileptic infusions. Burst suppression was achieved with pentobarbital, and it was decided to transition the patient to phenobarbital. Due to concerns of phenobarbital-induced DRESS, the patient underwent a phenobarbital desensitization consisting of 6 doses sequentially administered in 10-fold increasing concentrations before achieving therapeutic dosing. Three days later, the patient achieved therapeutic phenobarbital levels, was weaned off of pentobarbital, and remained seizure-free without recurrence of DRESS. Conclusions: Graded desensitization may be an option to minimize recurrence of DRESS in patients where avoidance of the offending agent is not possible.


2017 ◽  
Vol 39 (8) ◽  
pp. 693-702 ◽  
Author(s):  
Jainn-Jim Lin ◽  
Cheng-Che Chou ◽  
Shih-Yun Lan ◽  
Hsiang-Ju Hsiao ◽  
Yu Wang ◽  
...  

Author(s):  
G Farhani ◽  
N Farhani ◽  
MC Ng

Background: Treatment of refractory status epilepticus (RSE) is often titrated to achieve EEG burst suppression. However, optimal burst suppression characteristics are largely unknown. We used an unsupervised machine learning algorithm to predict RSE outcome based on the quantitative burst suppression ratio (QBSR). Methods: We conducted principal component analysis (PCA) as a linear combination of 22 QBSR features from non-anoxic adult RSE patients at the Winnipeg Health Sciences Centre. We also determined the most predictive components that significantly differed between survivors and non-survivors. Results: Using 135,765 QBSRs from 7 survivors and 10 non-survivors, PCA identified a predominantly non-survivor cluster of 8 patients (75% non-survivors). The first 2 PCA components comprised 75% data variance. The most important first component feature was skewness of QBSR distribution in the right or left hemisphere (0.52 each). The most important second component feature was third QBSR quantile of the left hemisphere (0.49). Only right hemispheric QBSR features significantly differed between groups: QBSR skewness for the first component (Benjamini-Hochberg adjusted p=0.038) and average QBSR for the second component (0.32, Benjamini-Hochberg adjusted p=0.046). Conclusions: Our pilot study shows that RSE patient survival may be impacted by QBSR, with differential hemispheric EEG burst suppression characteristics predicting poor RSE outcome.


2016 ◽  
Vol 25 (3) ◽  
pp. 407-414 ◽  
Author(s):  
Emily L. Johnson ◽  
Nirma Carballido Martinez ◽  
Eva K. Ritzl

2015 ◽  
Vol 22 (5) ◽  
pp. 854-858 ◽  
Author(s):  
Bong Su Kang ◽  
Keun-Hwa Jung ◽  
Jeong-Won Shin ◽  
Jang Sup Moon ◽  
Jung-Ick Byun ◽  
...  

BMC Neurology ◽  
2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Kanitpong Phabphal ◽  
Suparat Chisurajinda ◽  
Thapanee Somboon ◽  
Kanjana Unwongse ◽  
Alan Geater

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