Epilepsy at different ages-Etiologies in a Norwegian population

Text mining of electronic health records can validate a register-based diagnosis of epilepsy and subgroup into focal and generalized epilepsy

OBJECTIVE

Combining population-based health registries and electronic health records offers the opportunity to create large, phenotypically detailed patient cohorts of high quality. In this study, we used text mining of clinical notes to confirm International Classification of Diseases, 10th Revision (ICD-10)-registered epilepsy diagnoses and classify patients according to focal and generalized epilepsy types.

METHODS

Using the Danish National Patient Registry, we identified patients who between 2006 and 2016 received an ICD-10 diagnosis of epilepsy. To validate the epilepsy diagnosis and stratify patients into focal and generalized epilepsy types, we constructed dictionaries for text mining-based extraction of clinical notes. Two physicians manually reviewed the clinical notes for a total of 527 patients and assigned epilepsy diagnoses, which were compared with the text-mined diagnoses.

RESULTS

We identified 23 632 patients with an ICD-10 diagnosis of epilepsy, of whom 50% were registered with an unspecified epilepsy diagnosis. In total, 11 211 patients were considered likely to have epilepsy by text mining, with an F1 measure ranging from 82% to 90%. Manual review of the electronic health records for 310 patients revealed a false discovery rate of 29%. This rate was decreased to 4% by the text mining algorithm. The weighted average F1 measure for text mining-assigned epilepsy types was 79% (82% for focal and 76% for generalized epilepsy). Text mining successfully assigned a focal or generalized epilepsy type to 92% of the text mining-eligible patients registered with unspecified epilepsy.

SIGNIFICANCE

Text mining of electronic health records can be used to establish a patient cohort with much higher likelihood of having a diagnosis of epilepsy and a focal or generalized epilepsy type compared to the cohort created from ICD-10 epilepsy codes alone. We believe the concept will be essential for future genome-wide and phenome-wide association studies and subsequently the development of precision medicine for epilepsy patients.

Untargeted metabolomics profiling in pediatric patients and adult populations indicates a connection between lipid imbalance and epilepsy

Introduction

Epilepsy is a common central nervous system disorder characterized by abnormal brain electrical activity. We aimed to compare the metabolic profiles of plasma from patients with epilepsy across different etiologies, seizure frequency, seizure type, and patient age to try to identify common disrupted pathways.

Material and methods

We used data from three separate cohorts. The first cohort (PED-C) consisted of 31 pediatric patients with suspicion of a genetic disorder with unclear etiology; the second cohort (AD-C) consisted of 250 adults from the Estonian Biobank (EstBB), and the third cohort consisted of 583 adults ≥ 69 years of age from the EstBB (ELD-C). We compared untargeted metabolomics and lipidomics data between individuals with and without epilepsy in each cohort.

Results

In the PED-C, significant alterations (p-value <0.05) were detected in sixteen different glycerophosphatidylcholines (GPC), dimethylglycine and eicosanedioate (C20-DC). In the AD-C, nine significantly altered metabolites were found, mainly triacylglycerides (TAG), which are also precursors in the GPC synthesis pathway. In the ELD-C, significant changes in twenty metabolites including multiple TAGs were observed in the metabolic profile of participants with previously diagnosed epilepsy. Pathway analysis revealed that among the metabolites that differ significantly between epilepsy-positive and epilepsy-negative patients in the PED-C, the lipid superpathway (p = 3.2*10-4) and phosphatidylcholine (p = 9.3*10-8) and lysophospholipid (p = 5.9*10-3) subpathways are statistically overrepresented. Analogously, in the AD-C, the triacylglyceride subclass turned out to be statistically overrepresented (p = 8.5*10-5) with the lipid superpathway (p = 1.4*10-2). The presented p-values are FDR-corrected.

Conclusion

Our results suggest that cell membrane fluidity may have a significant role in the mechanism of epilepsy, and changes in lipid balance may indicate epilepsy. However, further studies are needed to evaluate whether untargeted metabolomics analysis could prove helpful in diagnosing epilepsy earlier.

Electroencephalographic Findings, Antiepileptic Drugs and Risk Factors of 433 Individuals Referred to a Tertiary Care Hospital in Ethiopia

Background

Little is known about the characteristics of electroencephalogram (EEG) findings in epileptic patients in Ethiopia. The objective of this study was to characterize the EEG patterns, indications, antiepileptic drugs (AEDs), and epilepsy risk factors.

Methods

A retrospective observational review of EEG test records of 433 patients referred to our electrophysiology unit between July 01, 2020 and December 31, 2021.

Results

The age distribution in the study participants was right skewed unipolar age distribution for both sexes and the mean age of 33.8 (SD=15.7) years. Male accounted for 51.7%. Generalized tonic clonic seizure was the most common seizure type. The commonest indication for EEG was abnormal body movement with loss of consciousness (35.2%). Abnormal EEG findings were observed in 55.2%; more than half of them were Interictal epileptiform discharges, followed by focal/or generalized slowing. Phenobarbitone was the commonest AEDs. A quarter (20.1%) of the patients were getting a combination of two AEDs and 5.2% were on 3 different AEDs. Individuals taking the older AEDs and those on 2 or more AEDs tended to have abnormal EEG findings. A cerebrovascular disorder (27.4%) is the prevalent risk factor identified followed by brain tumor, HIV infection, and traumatic head injury respectively.

Conclusions

High burden of abnormal EEG findings among epileptic patients referred to our unit. The proportion of abnormal EEG patterns was higher in patients taking older generation AEDs and in those on 2 or more AEDs. Stroke, brain tumor, HIV infection and traumatic head injury were the commonest identified epilepsy risk factors.

Governance of the clinical pathway and management of the patient suffering from epilepsy and drug-resistant epilepsy

Epilepsy is a diffuse chronic neurological disease affecting around 50 million people worldwide. The diagnostic criteria by the International League against Epilepsy must be fulfilled to diagnose the disease, which is characterized by brief and transient episodes of abnormal neuronal activity involving one or both hemispheres, depending on the epilepsy type. The diagnosis of epilepsy should be properly and timely made because patients suffering from the disease are affected not only by seizure recurrence but also by epilepsy-related psychiatric and/or cognitive comorbidities that may have a huge impact with severe professional and social implications. It is of vital importance to define a specific governance model that has to be virtuously applied into the different phases of the clinical pathway of the patients with epilepsy in order to guarantee them the best model of care possible.

Cadherins and the pathogenesis of epilepsy

Epilepsy is a nervous system disease caused by abnormal discharge of brain neurons, which is characterized by recurrent seizures. The factors that induce epilepsy include genetic and environmental factors. Genetic factors are important pathogenic factors of epilepsy, such as epilepsy caused by protocadherin-19 (PCDH-19) mutation, which is an X-linked genetic disease. It is more common in female heterozygotes, which are caused by mutations in the PCDH-19 gene. Epilepsy caused by environmental factors is mainly caused by brain injury, which is commonly caused by brain tumors, brain surgery, or trauma to the brain. In addition, the pathogenesis of epilepsy is closely related to abnormalities in some signaling pathways. The Wnt/β-catenin signaling pathway is considered a new target for the treatment of epilepsy. This review summarizes these factors inducing epilepsy and the research hypotheses regarding the pathogenesis of epilepsy. The focus of this review centers on cadherins and the pathogenesis of epilepsy. We analyzed the pathogenesis of epilepsy induced by N-cadherin and PCDH-19 in the cadherin family members. Finally, we expect that in the future, new breakthroughs will be made in the study of the pathogenesis and mechanism of epilepsy at the cellular and molecular levels.

Neurodevelopmental and psychiatric comorbidities negatively affect outcome in children with unprovoked seizures-A non-systematic review

Children with epilepsy have at least one comorbidity in 80% of cases. This unstructured review provides insights into the most common comorbidities, their effects on seizure prognosis and treatment. We also review the epilepsy terminology and classifications. Neurodevelopmental and psychiatric comorbidities were common in children with seizures and had a negative effect on quality of life, and seizure freedom. Children with seizures were treated with drugs used for attention deficit hyperactivity disorder (ADHD), depression or psychosis, more often than the general population but less often than prevalence rates would suggested. CONCLUSION: Multidisciplinary teams should assess comorbidities in children with epilepsy to improve their care and outcomes.