RESUMO
Background: Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG. Methods: We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool. Results: We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures. Conclusion: The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG. Significance: We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.
RESUMO
Background: Type I interferons are cytokines involved in innate immunity against viruses. Genetic disorders of type I interferon regulation are associated with a range of autoimmune and cerebrovascular phenotypes. Carriers of pathogenic variants involved in genetic disorders of type I interferons are generally considered asymptomatic. Preliminary data suggests, however, that genetically determined dysregulation of type I interferon responses is associated with autoimmunity, and may also be relevant to sporadic cerebrovascular disease and dementia. We aim to determine whether functional variants in genes involved in type I interferon regulation and signalling are associated with the risk of autoimmunity, stroke, and dementia in a population cohort. Methods: We will perform a hypothesis-driven candidate pathway association study of type I interferon-related genes using rare variants in the UK Biobank (UKB). We will manually curate type I interferon regulation and signalling genes from a literature review and Gene Ontology, followed by clinical and functional filtering. Variants of interest will be included based on pre-defined clinical relevance and functional annotations (using LOFTEE, M-CAP and a minor allele frequency <0.1%). The association of variants with 15 clinical and three neuroradiological phenotypes will be assessed with a rare variant genetic risk score and gene-level tests, using a Bonferroni-corrected p-value threshold from the number of genetic units and phenotypes tested. We will explore the association of significant genetic units with 196 additional health-related outcomes to help interpret their relevance and explore the clinical spectrum of genetic perturbations of type I interferon. Ethics and dissemination: The UKB has received ethical approval from the North West Multicentre Research Ethics Committee, and all participants provided written informed consent at recruitment. This research will be conducted using the UKB Resource under application number 93160. We expect to disseminate our results in a peer-reviewed journal and at an international cardiovascular conference.