(1240) Utilizing Rapid Response EEG in Alcohol Withdrawal Patients

Introduction: A significant concern during alcohol withdrawal is the risk of seizures, which can be life-threatening and necessitate immediate medical intervention. Timely detection and monitoring of seizures are crucial, however, conventional EEG (cEEG) may not always be accessible during off-hours leading to potential delays in diagnosis and treatment. This study explores the feasibility and efficacy of utilizing Rapid Response EEG (RR-EEG) as an alternative method to detect seizures in alcohol withdrawal patients when cEEG services are not available. RR-EEG (Ceribell Inc) offers rapid application of continuous monitoring that aids in the early detection of NCS and highly epileptiform patterns (HEP), such as lateralized periodic discharges (LPD), generalized periodic discharges (GPD), and triphasic waves (TP).

Methods: Automated detection of seizure burden (calculated as the percentage of ten-second epochs with detected seizure activity in any 5-min segment) was performed using the proprietary Claritγ algorithm. A calculated seizure burden of ≥ 90% suggested NCS activity and treatment was administered accordingly. 10-90 % burden prompted immediate on-call epileptologist review. < 10 % seizure burden suggested against NCS and thus interval epileptology reading was followed.

Results: A total of 11 RR-EEGs were performed on patients admitted to ICU with alcohol withdrawal for an average duration of 8.0 (+/- 0.2) hours. Patients had an average age of 53 (+/- 20) years and 36% were male. Average APACHE III scores and acute physiologic scores were 50 (+/-24) and 40 (+/- 17) respectively. Reasons for EEG included: unexplained acute encephalopathy in 82 %, seizure-like activity in 64 %, and subtle automatisms in 27 %. On RR-EEG, NCS was detected in 1 patient (9 %), LPD in 1 (9 %), GPD in 0, and TP in 1 (9 %). RR-EEG was followed by cEEG in all patients with following readings: NCS in 1 (9 %), LPD in 4 (36 %), GPD in 5 (45 %), and TP in 11 (100 %).

Conclusions: Our preliminary results indicate that RR-EEG is feasible in detecting seizures in alcohol withdrawal. The device successfully identified seizure activity in real-time, enabling rapid intervention. Although our findings are encouraging, further large-scale studies are warranted to validate the efficacy and reliability of RR-EEG in alcohol withdrawal.