Early EEG Increases the Rate of Detection of Epileptiform Abnormalities After First Seizure
Abstract number :
2.017
Submission category :
3. Neurophysiology / 3C. Other Clinical EEG
Year :
2022
Submission ID :
2204075
Source :
www.aesnet.org
Presentation date :
12/4/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:23 AM
Authors :
Alice-Ann Sullivan, MBBS – RBWH; Rebecca Kerr, MBBS – GP with Special Interest in Epilepsy, Neurology Department, RBWH; Xiaohua Chen, MBBS – Neurologist, Neurology Department, RBWH; Fred Tremayne, B.Sc – Neurophysiologist, Neurology Department, RBWH; Soumen Ghosh, M.Tech IT – University Queensland; David Reutens, MBBS, MD – Director Epilepsy Services, Director Centre for Advanced Imaging, Neurology, RBWH, University Queensland
Rationale: A first unprovoked seizure may be the initial presentation of epilepsy, and, in this setting, a diagnosis of epilepsy can be made if epileptiform abnormalities are present on EEG. Epileptiform EEG features can also help define an epilepsy syndrome and inform appropriate treatment choices. However, routine EEG has notoriously low sensitivity, with epileptiform changes occurring in 10-27% of adults following a first seizure. A negative EEG after first seizure usually generates subsequent EEG testing, which increases costs and risks to the patient. There is some evidence to suggest that an early EEG may increase sensitivity, but evidence has been conflicting. Our hospital's first seizure clinic pathway allows early (same day or next working day) EEG followed by clinic review 2-4 weeks later. Our study aimed to determine whether a shorter time between the first seizure and EEG increases the likelihood of detecting epileptiform EEG abnormalities.
Methods: We retrospectively reviewed patients over a 2-year period who were referred from the Emergency Department to our first seizure clinic and were assessed to have had a first unprovoked seizure. We collected data on age, gender, seizure type, EEG findings and clinical diagnosis. Time from first seizure to the EEG was calculated from seizure onset, based on history and ambulance notes, to the EEG recording time. EEG findings were coded as epileptiform or non-epileptiform. We defined the diagnostic yield as a percentage; the number of epileptiform EEGs divided by the total number of EEGs, and performed logistic regression to variables of time delay to EEG, age and sex.
Results: A total of 105 patients (62 males, 44 females) were included in the study. Age range was from 16 years-76 years, with a median age of 30 years. The median time from seizure to EEG was 22 hours (range 6-44 hours). Epileptiform abnormalities were seen in 32 (30%) of patients. Of these, 56% were generalized, and 34% were focal. Two factors were found to be significant in detecting epileptiform abnormalities: time delay to EEG and age. There was a median delay of 16 hours for EEGs with epileptiform abnormalities compared to 24 hours for EEGs without such changes (p=0.004, Mann-Whitney U test). The percentage of EEGs with epileptiform abnormalities in EEGs performed earlier than 12 hours, within 12 to < 24 hours, within 24 to < 48 hours, and 48 hours or more after a seizure were 48.0%, 31.4%, 28.6 % and 9.1 % respectively (p=0.004, χ2 test). Figure 1 shows the distribution of epileptiform EEGs over time. The likelihood of an epileptiform EEG was also related to age, with 64% of patients aged under 20 years having an epileptiform EEG versus 30 % of those aged 20-39 years, 14% of those aged 40-59 years, and 7% aged 60 and above (p< 0.001, χ2 test). Using a multivariate logistic regression model, the odds of finding an epileptiform EEG fell by 18% for every 6 hours of delay to EEG and by 44% for every 10 years of patient age (Figure 2).
Conclusions: Reducing the time delay to EEG after first seizure increases diagnostic yield and is likely to reduce the need for subsequent diagnostic EEG testing. In addition, epileptiform changes are more likely in younger patients.
Funding: None
Neurophysiology