Electrocardiographic Abnormalities and Mortality in Epilepsy Patients

Mortality in patients with epilepsy: a systematic review

BACKGROUND

Epilepsy is linked to a significantly higher risk of death, yet public awareness remains low. This study aims to investigate mortality characteristics, to reduce epilepsy-related deaths and improve prevention strategies.

METHODS

This study systematically reviews mortality data in relevant literature from PubMed and Embase up until June 2024. Data quality is assessed using the Newcastle-Ottawa Scale, and analysis includes trends, regional differences, and the economic impact of premature death. Global Burden of Disease (GBD) data are used to validate trends. In addition, a review of guidelines and expert statements on sudden unexpected death in epilepsy (SUDEP) is included to explore intervention strategies and recommendations.

RESULTS

Annual mortality rates of epilepsy have gradually declined, mainly due to improvements in low-income countries, while high-income regions have experienced an upward trend. Male patients exhibit higher mortality rates than females. Age-based analysis shows that the elderly contributes most to this increase due to chronic conditions such as cardiovascular disease and pneumonia related to epilepsy. This may be a key factor contributing to the increased mortality among epilepsy patients in aging high-income regions. Accidents and suicides are more prevalent in low-income regions. The highest mortality risks occur in the early years post-diagnosis and during prolonged, uncontrolled epilepsy. SUDEP remains a leading cause of death.

CONCLUSION

This study highlights the impact of gender, region, and disease duration on epilepsy mortality. Future research should focus on elderly epilepsy patients mortality characteristics and personalized interventions for SUDEP.

Development and Validation of a Non-Invasive Prediction Model for Glioma-Associated Epilepsy: A Comparative Analysis of Nomogram and Decision Tree

Objective

Glioma-associated epilepsy (GAE) is a common neurological symptom in glioma patients, which can worsen the condition and increase the risk of death on the basis of primary injury. Given this, accurate prediction of GAE is crucial, and this study aims to develop and validate a GAE warning recognition prediction model.

Methods

We retrospectively collected MRI scan imaging data and urine samples from 566 glioma patients at the Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science from August 2016 to December 2023. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression analysis are used to determine independent risk factors for GAE. The nomogram and decision tree GAE visualization prediction model were constructed based on independent risk factors. The discrimination, calibration, and clinical usefulness of GAE prediction models were evaluated through receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively.

Results

In the training and validation datasets, the incidence of GAE was 34.50% and 33.00%, respectively. Nomogram and decision tree were composed of five independent radiomic predictors and three differential protein molecules derived from urine. The discrimination rate of area under the curve (AUC) was 0.897 (95% CI: 0.840-0.954), slightly decreased in the validation data set, reaching 0.874 (95% CI: 8.817-0.931). The calibration curve showed a high degree of consistency between the predicted GAE probability and the actual probability. In addition, DCA analysis showed that in machine learning prediction models, decision trees have higher overall net returns within the threshold probability range.

Conclusion

We have introduced a machine learning prediction model for GAE detection in glioma patients based on multiomics data. This model can improve the prognosis of GAE by providing early warnings and actionable feedback and prevent or reduce pathological damage and neurobiochemical changes by implementing early interventions.

Association of Cardiovascular Risk Factors With Major and Minor Electrocardiographic Abnormalities: A Report From the Cross-Sectional Phase of Tehran Cohort Study

Background and Aims

In the current study, we aimed to identify the association between major and minor electrocardiographic abnormalities and cardiovascular risk factors.

Methods

We used the Tehran cohort study baseline data, an ongoing multidisciplinary, longitudinal study designed to identify cardiovascular disease risk factors in the adult population of Tehran. The electrocardiograms (ECGs) of 7630 Iranian adults aged 35 years and above were analyzed. ECG abnormalities were categorized into major or minor groups based on their clinical importance. Results were obtained by multivariable logistic regression and are expressed as odds ratios (ORs).

Results

A total of 756 (9.9%) participants had major ECG abnormalities, while minor abnormalities were detected in 2526 (33.1%). Males comprised 45.8% of the total population, and 41.8% of them had minor abnormalities. Individuals with older age, diabetes (OR = 1.35; 95% CI: 1.11-1.64), and hypertension (OR = 2.21; 95% CI: 1.82-2.68) had an increased risk of major ECG abnormalities. In contrast, intermediate (OR = 0.69; 95% CI: 0.57-0.84) and high physical activity levels (OR = 0.66; 95% CI: 0.51-0.86) were associated with a lower prevalence of major abnormalities. Male sex, older age, hypertension, and current smoking were also associated with an increased prevalence of ECG abnormalities combined (major or minor).

Conclusion

Major and minor ECG abnormalities are linked with important cardiovascular risk factors such as diabetes and hypertension. Since these abnormalities have been associated with poor outcomes, screening patients with cardiovascular risk factors with an ECG may distinguish high-risk individuals who require appropriate care and follow-up.

Investigation of Electrocardiographic Changes Caused by Antepileptic Drugs in Epilepsy Patients

BACKGROUND

One of the metabolic effects of antiepileptic drugs (AEDs) is cardiac changes.

AIM

In our study, to investigate the cardiac effects of AED use; We looked at electrocardiography (ECG) samples performed on patients. By looking at ECG variables, we tried to determine their relationship with epileptic seizure types and antiepileptic drugs.

METHODS

This prospective study was completed with a total of 50 epilepsy patients whose ECGs were recorded after exclusion criteria. The number of years the patients had epilepsy, the frequency of seizures, the duration of seizures, and the AEDs they used were recorded. Standard 12-lead ECG was applied to the patients, and QT intervals, Tp-e interval, Tp-e/QT ratio, and Tp-e/QTd ratio were measured.

RESULTS

Patients most commonly use levatiracetam, valproic acid (VPA), carbamazepine, and lamotrigine, respectively. The median seizure time of the patients was 120 seconds. There was no difference regarding ECG parameters among seizure types. Among the AED groups, Tp-e interval (P = 0.028), Tp-e/QT (P = 0.007), and Tp-e/QTd (P = 0.001) values were lower in those receiving lamotrigine.

CONCLUSION

It was determined that there were differences in cardiac repolarization parameters between AEDs. Lamotrigine had the highest confidence interval due to its low effect on the ECG and low potential to cause arrhythmia.

Sudden death in epilepsy: the overlap between cardiac and neurological factors

People with epilepsy are at risk of premature death, of which sudden unexpected death in epilepsy (SUDEP), sudden cardiac death (SCD) and sudden arrhythmic death syndrome (SADS) are the primary, partly overlapping, clinical scenarios. We discuss the epidemiologies, risk factors and pathophysiological mechanisms for these sudden death events. We reviewed the existing evidence on sudden death in epilepsy. Classification of sudden death depends on the presence of autopsy and expertise of the clinician determining aetiology. The definitions of SUDEP, SCD and SADS lead to substantial openings for overlap. Seizure-induced arrhythmias constitute a minority of SUDEP cases. Comorbid cardiovascular conditions are the primary determinants of increased SCD risk in chronic epilepsy. Genetic mutations overlap between the states, yet whether these are causative, associated or incidentally present is often unclear. Risk stratification for sudden death in people with epilepsy requires a multidisciplinary approach, including a review of clinical history, toxicological analysis and complete autopsy with histologic and, preferably, genetic examination. We recommend pursuing genetic testing of relatives of people with epilepsy who died suddenly, mainly if a post-mortem genetic test contained a Class IV/V (pathogenic/likely pathogenic) gene variant. Further research may allow more precise differentiation of SUDEP, SCD and SADS and the development of algorithms for risk stratification and preventative strategies.

Exploring Autonomic Alterations during Seizures in Temporal Lobe Epilepsy: Insights from a Heart-Rate Variability Analysis

Epilepsy's impact on cardiovascular function and autonomic regulation, including heart-rate variability, is complex and may contribute to sudden unexpected death in epilepsy (SUDEP). Lateralization of autonomic control in the brain remains the subject of debate; nevertheless, ultra-short-term heart-rate variability (HRV) analysis is a useful tool for understanding the pathophysiology of autonomic dysfunction in epilepsy patients. A retrospective study reviewed medical records of patients with temporal lobe epilepsy who underwent presurgical evaluations. Data from 75 patients were analyzed and HRV indices were extracted from electrocardiogram recordings of preictal, ictal, and postictal intervals. Various HRV indices were calculated, including time domain, frequency domain, and nonlinear indices, to assess autonomic function during different seizure intervals. The study found significant differences in HRV indices based on hemispheric laterality, language dominancy, hippocampal atrophy, amygdala enlargement, sustained theta activity, and seizure frequency. HRV indices such as the root mean square of successive differences between heartbeats, pNN50, normalized low-frequency, normalized high-frequency, and the low-frequency/high-frequency ratio exhibited significant differences during the ictal period. Language dominancy, hippocampal atrophy, amygdala enlargement, and sustained theta activity were also found to affect HRV. Seizure frequency was correlated with HRV indices, suggesting a potential relationship with the risk of SUDEP.

Comparing Ictal Cardiac Autonomic Changes in Patients with Frontal Lobe Epilepsy and Temporal Lobe Epilepsy by Ultra-Short-Term Heart Rate Variability Analysis

Background and Objectives: Abnormal epileptic discharges in the brain can affect the central brain regions that regulate autonomic activity and produce cardiac symptoms, either at onset or during propagation of a seizure. These autonomic alterations are related to cardiorespiratory disturbances, such as sudden unexpected death in epilepsy. This study aims to investigate the differences in cardiac autonomic function between patients with temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE) using ultra-short-term heart rate variability (HRV) analysis around seizures. Materials and Methods: We analyzed electrocardiogram (ECG) data recorded during 309 seizures in 58 patients with epilepsy. Twelve patients with FLE and 46 patients with TLE were included in this study. We extracted the HRV parameters from the ECG signal before, during and after the ictal interval with ultra-short-term HRV analysis. We statistically compared the HRV parameters using an independent t-test in each interval to compare the differences between groups, and repeated measures analysis of variance was used to test the group differences in longitudinal changes in the HRV parameters. We performed the Tukey-Kramer multiple comparisons procedure as the post hoc test. Results: Among the HRV parameters, the mean interval between heartbeats (RRi), normalized low-frequency band power (LF) and LF/HF ratio were statistically different between the interval and epilepsy types in the t-test. Repeated measures ANOVA showed that the mean RRi and RMSSD were significantly different by epilepsy type, and the normalized LF and LF/HF ratio significantly interacted with the epilepsy type and interval. Conclusions: During the pre-ictal interval, TLE patients showed an elevation in sympathetic activity, while the FLE patients showed an apparent increase and decrease in sympathetic activity when entering and ending the ictal period, respectively. The TLE patients showed a maintained elevation of sympathetic and vagal activity in the pos-ictal interval. These differences in autonomic cardiac characteristics between FLE and TLE might be relevant to the ictal symptoms which eventually result in SUDEP.