Correlation of Spike-wave Index (SWI) and Clinical Outcome in Encephalopathy Related to Status Epilepticus During Sleep (ESES)
Abstract number :
2.13
Submission category :
3. Neurophysiology / 3C. Other Clinical EEG
Year :
2024
Submission ID :
979
Source :
www.aesnet.org
Presentation date :
12/8/2024 12:00:00 AM
Published date :
Authors :
Author: Alberto Cossu, MD – Centro di Ricerca per Epilessia in età Pediatrica (CREP), Verona, Italy
Mette Schou Larsen, Research Nurse – Department of Paediatrics, Danish Epilepsy Centre Filadelfia, Dianalund, Denmark
Tomasz Mieszczanek, MD – Department of Paediatrics, Danish Epilepsy Centre Filadelfia, Dianalund, Denmark
Marina Nikanorova, MD – Department of Paediatrics, Danish Epilepsy Centre Filadelfia, Dianalund, Denmark
Margarethe Kolmel, MD – Department of Paediatrics, Danish Epilepsy Centre Filadelfia, Dianalund, Denmark
Daniella Terney, MD – Department of Clinical Neurophysiology, Danish Epilepsy Centre Filadelfia, Dianalund, Denmark
Guido Rubboli, MD – Danish Epilepsy Center, member of the European Reference Center EpiCARE; University of Copenhagen, Copenhagen, Denmark
Kern Olofsson, MD – Department of Paediatrics, Danish Epilepsy Centre Filadelfia, Dianalund, Denmark
Presenting Author: Elena Gardella, MD, PhD – Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Centre, Dianalund, Denmark
Rationale: Encephalopathy related to Status Epilepticus during Sleep (ESES), recently named Epileptic Encephalopathy with Spike and Wave Activation during Sleep (EE-SWAS), is characterised by the coexistence of a cognitive/behavioural regression and the activation of interictal epileptiform discharges (IEDs) during sleep, which ultimately is responsible for the cognitive deficits. The most used parameter for quantifying epileptiform activity is the Spike-wave Index (SWI), defined as the percentage of the total duration of NREM sleep occupied by IEDs. This study investigates the relationship between SWI changes and global clinical outcome in EE-SWAS.
Methods: We conducted a chart review of children with EE-SWAS at the Danish Epilepsy Centre (Dianalund, Denmark). NREM SWI was routinely calculated and extracted from EEG reports. We calculated the differential in absolute NREM-SWI (diff-NREM-SWI) between each time point and the following one. A modified clinical global impression (CGI) score was extracted using a 3-point scale (1 – improved, 2 – unchanged, 3 – worse), considering different non-seizure outcomes.
A Receiver Operating Characteristic (ROC) analysis was performed using diff-NREM-SWI as test results on both possible CGI score outcomes (Improved and Worsened), accepting curves with an AUC ³ 0.7.
Based on these thresholds, we classified time points as Concordant if the diff-NREM-SWI and CGI-score changed consistently or Discordant if the two parameters showed inconsistent results. Discordant EEG/clinical recordings were further classified as Divergent if the diff-NREM-SWI and the CGI changed in opposite directions (e.g. the diff-NREM-SWI was increased and the CGI showed an improvement, or vice versa) or Convergent (with either Unchanged CGI or Stable EEG). The flowchart depicting the classification of patients based on these criteria is reported in Fig.1.
Results: We enrolled 80 patients (50 males) and collected 24-hour EEG recordings at 335 different time points. The mean age at recording was 9.7 years (IQR 7.4 – 11.9). The mean diff-NREM-SWI was -4.9% (IQR -16% - 6%). We found a significant difference in the diff-NREM-SWI pair-wise comparisons among the three CGI-score groups (p< 0.05). The ROC analysis yielded a threshold of a diff-NREM-SWI equal to or below -9.5% (sensitivity: 60%, specificity: 25%) for detecting a clinical improvement and 5.5% (sensitivity 55%, specificity 22.5%) for detecting a clinical worsening (Fig.2).
Among all the control recordings, 52.2% (134/255) had Concordant electro-clinical findings, and 47.8% (121/255) had Discordant electro-clinical findings. Among these, 79% (96/121) had an Unchanged CGI. We found no significant impact of the period between recordings, age at recording, or number of concomitant anti-seizure medications.
Conclusions: This study reports thresholds in differential-SWI associated with a significantly changed EEG in a cohort of patients with EE-SWAS. This classification allowed us to explore the usefulness of the SWI, confirming its reliability as a clinical tool in managing EE-SWAS.
Funding: This research received support from Neurocrine
Neurophysiology