Abstract
BackgroundA retrospective, multi-site case control study was carried out to validate a set of candidate biomarkers of seizure susceptibility. The objective was to determine the robustness of these biomarkers derived from routinely collected EEG within a large cohort (both epilepsy and common alternative conditions which may present with a possible seizure, such as NEAD).MethodsThe database consisted of 814 EEG recordings from 648 subjects, collected from 8 NHS sites across the UK. Clinically non-contributory EEG recordings were identified by an experienced clinical scientist (N = 281; 152 alternative conditions, 129 epilepsy). Eight computational markers (spectral [N = 2], network-based [N = 4] and model-based [N = 2]) were calculated within each recording. Ensemble-based classifiers were developed using a two-tier cross validation approach. We used standard regression methods in order to identify whether potential confounding variables (e.g. age, gender, treatment-status, comorbidity) impacted model performance.FindingsWe found levels of balanced accuracy of 68% across the cohort with clinically noncontributory normal EEGs (sensitivity: 61%, specificity: 75%, positive predictive value: 55%, negative predictive value: 79%, diagnostic odds ratio: 4.64). Group-level analysis found no evidence suggesting any of the potential confounding variables significantly impacted the overall performance.InterpretationThese results provide evidence that the set of biomarkers could provide additional value to clinical decision-making, providing the foundation for a decision support tool that could reduce diagnostic delay and misdiagnosis rates. Future work should therefore assess the change in diagnostic yield and time to diagnosis when utilising these biomarkers in carefully designed prospective studies.
DOI
10.1101/2023.03.08.23286937
Publication Date
2023-03-12
Organisational Unit
Peninsula Medical School
Recommended Citation
Shankar, R., Tait, L., Staniaszek, L., Galizia, E., Martin-Lopez, D., Walker, M., Azeez, A., Meiklejohn, K., Allen, D., Price, C., Georgiou, S., Bagary, M., Khalsa, S., Manfredonia, F., Tittensor, P., Lawthom, C., Terry, J., & Woldman, W. (2023) 'Estimating the likelihood of epilepsy from clinically non-contributory EEG using computational analysis: A retrospective, multi-site case-control study', Available at: https://doi.org/10.1101/2023.03.08.23286937
