AMPAr GluA1 Phosphorylation at Serine 845 in Limbic System Is Associated with Cardiac Autonomic Tone

The epileptic heart: Cardiac comorbidities and complications of epilepsy. Atrial and ventricular structure and function by echocardiography in individuals with epilepsy - From clinical implications to individualized assessment

Epilepsy is an increasing global neurological health issue. Recently, epidemiological and mechanistic studies have raised concern about cardiac involvement in individuals with epilepsy. This has resulted in the "epileptic heart" concept. Epidemiological data linking epilepsy to cardiovascular disease indicate an increased risk for ventricular and atrial arrhythmias, myocardial infarction, heart failure, and sudden death among individuals with epilepsy. Pathways of this interaction comprise increased prevalence of traditional cardiac risk factors, genetic abnormalities, altered brain circuitry with autonomic imbalance, and antiseizure medications with enzyme-inducing and ionic channel-blocking proprieties. Pathophysiological findings in the atria and ventricles of patients with epilepsy are discussed. Echocardiographic findings and future applications of this tool are reviewed. A risk stratification model and future studies on cardiac risk assessment in individuals with epilepsy are proposed.

Detection of seizures with ictal tachycardia, using heart rate variability and patient adaptive logistic regression machine learning methods: A hospital-based validation study

OBJECTIVE

Automated seizure detection of focal epileptic seizures is needed for objective seizure quantification to optimize the treatment of patients with epilepsy. Heart rate variability (HRV)-based seizure detection using patient-adaptive threshold with logistic regression machine learning (LRML) methods has presented promising performance in a study with a Danish patient cohort. The objective of this study was to assess the generalizability of the novel LRML seizure detection algorithm by validating it in a dataset recorded from long-term video-EEG monitoring (LTM) in a Brazilian patient cohort.

METHODS

Ictal and inter-ictal ECG-data epochs recorded during LTM were analyzed retrospectively. Thirty-four patients had 107 seizures (79 focal, 28 generalized tonic-clonic [GTC] including focal-to-bilateral-tonic-clonic seizures) eligible for analysis, with a total of 185.5 h recording. Because HRV-based seizure detection is only suitable in patients with marked ictal autonomic change, patients with >50 beats/min change in heart rate during seizures were selected as responders. The patient-adaptive LRML seizure detection algorithm was applied to all elected ECG data, and results were computed separately for responders and non-responders.

RESULTS

The patient-adaptive LRML seizure detection algorithm yielded a sensitivity of 84.8% (95% CI: 75.6-93.9) with a false alarm rate of .25/24 h in the responder group (22 patients, 59 seizures). Twenty-five of the 26 GTC seizures were detected (96.2%), and 25 of the 33 focal seizures without bilateral convulsions were detected (75.8%).

SIGNIFICANCE

The study confirms in a new, independent external dataset the good performance of seizure detection from a previous study and suggests that the method is generalizable. This method seems useful for detecting both generalized and focal epileptic seizures. The algorithm can be embedded in a wearable seizure detection system to alert patients and caregivers of seizures and generate objective seizure counts helping to optimize the treatment of the patients.

How predictable is heart rate variability in Brazilian patients with drug-resistant mesial temporal lobe epilepsy?

This study aimed to compare heart rate variability (HRV) in patients with drug-resistant mesial temporal lobe epilepsy (MTLE) with healthy controls and to analyze their clinical and sociodemographic variables predictive for HRV. Thirty-nine consecutive patients with drug-resistant MTLE were included in the study. The control group included twenty-seven healthy participants matched by age and gender. Seven HRV indices (HR, RR, rMSSD, SDNN, LF, HF, and LF/HF) were compared between patients and controls. The clinical and sociodemographic variables independently associated with the HRV indices were identified by multiple linear regression. In comparison with controls, the patients with MTLE showed a significant reduction in RR, rMSSD, SDNN, LF, HF, and LF/HF indices (t value 1.97-5.97, p < 0.05). Multiple regression models showed that disease duration predicted 11-22% of the analyzed HRV indices. Time domain indices showed higher association with disease duration than coefficients in frequency domain. Patients with drug-resistant MTLE present cardiac autonomic tone dysfunction, showing a significant reduction in their HRV indices (RR, SDNN, rMSSD, LF, HF, and LF/HF). Disease duration has a negative association with all HRV indices. This study contributes to understanding the relationship between MTLE and the cardiac autonomic tone, with possible implications for sudden unexpected death in epilepsy.