Demographic and Clinical Correlates of Seizure Frequency: Findings from the Managing Epilepsy Well Network Database

About 1.5 million community-dwelling US adults with active epilepsy reported uncontrolled seizures in the past 12 months, and seizure control varied by annual family income-National Health Interview Survey, United States 2021 and 2022

Uncontrolled seizures among people with epilepsy increase risk of adverse health and social outcomes including increased risk of death. Previous population-based studies have reported suboptimal seizure control and disparities in seizure control among U.S. adults with active epilepsy (self-reported doctor-diagnosed epilepsy and taking anti-seizure medicine or with ≥ 1 seizures in the past 12 months) by annual family income. This brief is based upon data from the 2021 and 2022 National Health Interview Survey (NHIS) to provide updated national estimates of the percentages of adults with active epilepsy with and without seizure control (0 seizures in past 12 months) vs. ≥ 1) by anti-seizure medication use and by annual family income. Annual family income was operationalized with NHIS poverty-income ratio (PIR) categories (i.e., total family income divided by the US Census Bureau poverty threshold given the family's size and number of children): PIR < 1.0, 1.0 ≤ PIR < 2.0; PIR ≥ 2.0. Among the 1.1 % of US adults with active epilepsy in 2021/2022 (estimated population about 2.9 million), 49.2 % (∼1.4 million) were taking antiseizure medication and reported no seizures (seizure control), 36.2 % (∼1.1 million) were taking antiseizure medication and reported ≥ 1 seizures (uncontrolled seizures), and 14.7 % (∼400,000) were not taking antiseizure medication and had ≥ 1 seizures (uncontrolled seizures). The prevalence of seizure control among those with active epilepsy varied substantially by annual family income, with a larger percentage of adults with PIR ≥ 2.0 reporting seizure control compared with those with PIR < 1.0. Opportunities for intervention include improving provider awareness of epilepsy treatment guidelines, enhancing access and referral to specialty care, providing epilepsy self-management supports, and addressing unmet social needs of people with epilepsy with uncontrolled seizures, especially those at lowest family income levels.

Comorbidity between major depressive disorder and physical diseases: a comprehensive review of epidemiology, mechanisms and management

Populations with common physical diseases - such as cardiovascular diseases, cancer and neurodegenerative disorders - experience substantially higher rates of major depressive disorder (MDD) than the general population. On the other hand, people living with MDD have a greater risk for many physical diseases. This high level of comorbidity is associated with worse outcomes, reduced adherence to treatment, increased mortality, and greater health care utilization and costs. Comorbidity can also result in a range of clinical challenges, such as a more complicated therapeutic alliance, issues pertaining to adaptive health behaviors, drug-drug interactions and adverse events induced by medications used for physical and mental disorders. Potential explanations for the high prevalence of the above comorbidity involve shared genetic and biological pathways. These latter include inflammation, the gut microbiome, mitochondrial function and energy metabolism, hypothalamic-pituitary-adrenal axis dysregulation, and brain structure and function. Furthermore, MDD and physical diseases have in common several antecedents related to social factors (e.g., socioeconomic status), lifestyle variables (e.g., physical activity, diet, sleep), and stressful live events (e.g., childhood trauma). Pharmacotherapies and psychotherapies are effective treatments for comorbid MDD, and the introduction of lifestyle interventions as well as collaborative care models and digital technologies provide promising strategies for improving management. This paper aims to provide a detailed overview of the epidemiology of the comorbidity of MDD and specific physical diseases, including prevalence and bidirectional risk; of shared biological pathways potentially implicated in the pathogenesis of MDD and common physical diseases; of socio-environmental factors that serve as both shared risk and protective factors; and of management of MDD and physical diseases, including prevention and treatment. We conclude with future directions and emerging research related to optimal care of people with comorbid MDD and physical diseases.

Investigation on the Influencing Factors Related to Quality of Life of Adult Epilepsy Patients in Wenzhou, China, Based on Structural Equation Model

Objective. The aim of the study is to investigate the influencing factors of quality of life in adult patients with epilepsy in Wenzhou in China. Methods. A total of 190 patients who visited our hospital from July 2019 to February 2021 were included in the study. Demographic data and disease status were collected. Moreover, QOLIE-31, PSQI, ESS, HAMD-17, and GAD-7 were used in the questionnaire survey. Structural equation model fitting was used to analyze the influencing factors of quality of life in adult patients with epilepsy. Results. The scores of the dimension of onset worry in men were greater than those of women (P = 0.049), and the scores of the dimension of life satisfaction were lower than women (P = 0.047). The scores of cognitive function decreased with age (P = 0.047). The scores of quality of life of unemployed and drinking patients significantly decreased $\left(P<\mathit{0.05}\right)$ . The score of quality of life positively correlated with good economic status and family relations $\left(P<\mathit{0.05}\right)$ . The score of emotional health increased first and then decreased with the course of the disease. With the decrease in seizure frequency and the extension of months without a seizure, the score of quality of life gradually increased. Furthermore, the structural equation model showed that health status was directly correlated to the quality of life of patients with epilepsy. Conclusion. Male, unemployment, drinking, older age, and disease are negatively related to the quality of life in patients with epilepsy. However, good economic status, good family relations, and good colleague relationships are positively related to the quality of life.

Incidence of mental health conditions by seizure control among adults with epilepsy in the United States

BACKGROUND

Mental health conditions (MHCs) are frequent comorbidities among people with epilepsy; however, the influence of seizure control on the incidence of MHCs is not well reported. This retrospective observational cohort study based on claims data evaluated the effects of indicators of poor seizure control on the incidence of MHCs among MHC-naïve people with epilepsy. We hypothesized that poor seizure control is associated with new-onset MHC diagnoses and/or new prescription drugs for MHCs.

METHODS

This study utilized a sample of patients from HealthVerity Marketplace, which includes more than 150 US commercial, Medicare, and Medicaid payers, to identify a cohort of adults (age ≥18 years) with prevalent epilepsy. Follow-up started on day 1 (January 1) after a 1-year eligibility assessment period occurring in calendar year 2017 or 2018. Patients were followed up until the occurrence of an incident MHC event (primary outcome), defined as a mental health diagnosis or psychotropic drug prescription. Time from follow-up to incident MHC diagnosis or to a drug prescription specific to depression or anxiety disorder was analyzed as a secondary outcome. Multivariate Cox proportional hazards regressions were estimated with time-varying covariates, measured in 6-month intervals during follow-up. Time-varying covariates were based on the occurrence of 4 variables used as indicators of poor seizure control in the prior period: epilepsy-related emergent care admissions, epilepsy-related inpatient admissions, epilepsy electroencephalography referrals, and exposure to one or more new antiseizure medications (ASMs).

RESULTS

From a random sample of 40,000 people with epilepsy, 2563 (mean age 46.1 years; 50.6% male) were included in the analysis. Incident MHC events were observed in 27.7% (incidence rate 24.4 events per 100 person-years over 2,915.7 total person-years of follow-up). Mean (standard deviation [SD]) time to event was 232.7 (186.3) days. Among the 4 variables, epilepsy-related emergent care admissions were associated with an increased risk of incident MHC events in the following 6-month period (hazard ratio [HR] = 1.676, 95% confidence interval [CI]: 1.386, 2.026, p < 0.001) as were prescriptions for new ASMs in the previous period (HR = 1.702, 95% CI: 1.359, 2.132, p < 0.001). Previous epilepsy-related emergent care admissions (HR = 1.650, 95% CI: 1.347, 2.021, p < 0.001) and new ASMs (HR = 1.632, 95% CI: 1.280, 2.081, p < 0.001) also predicted an increased risk of incident depression or anxiety in the following 6-month period.

CONCLUSIONS

Previous indicators of poor seizure control, including epilepsy-related emergent care admissions and new ASMs, predicted increased risk of new MHC events, including depression and anxiety, during the following 6-month interval in MHC-naïve patients with prevalent epilepsy. These data suggest that poor seizure control can increase the subsequent risk of new mental health diagnoses and treatment among people with epilepsy.

Depression and suicidality among Hispanics with epilepsy: Findings from the Managing Epilepsy Well (MEW) Network integrated database

OBJECTIVE

Although psychiatric disorders are more common among people with epilepsy,² depression and suicidal ideation among Hispanics with epilepsy remain understudied. We examined the prevalence and correlates of depression and suicidal ideation among Hispanic adults with epilepsy who participated in self-management studies in the Managing Epilepsy Well³ Network.

METHODS

This cross-sectional analysis of pooled data from ten studies used the Patient Health Questionnaire-9⁴ or Neurological Disease Depression Inventory-Epilepsy⁵ to examine the prevalence of elevated depressive symptoms (PHQ ≥ 10, NDDI-E ≥ 15) and suicidal ideation (PHQ-9 item 9 ≥ 1, NDDI-E item 4 ≥ 2). Multilevel mixed-effects logistic regression models examined associations between ethnicity, elevated depressive symptoms, and suicidal ideation among PWE. Secondary analyses examined correlates of elevated depressive symptoms and suicidal ideation among Hispanic PWE.

RESULTS

Of 559 participants, 49.6% (n = 277) were Hispanic. Elevated depressive symptoms were endorsed by 38.1% (n = 213) of all participants (32.5% of Hispanics); suicidal ideation was endorsed by 18.4% (n = 103) of all participants (16.3% of Hispanics). After adjustment for sociodemographic and health attributes, Hispanic PWE had a 44% lower prevalence of elevated depressive symptoms (OR = 0.56, CI 0.37-0.84, p = 0.0056) compared to non-Hispanics but similar rates of suicidal ideation (OR = 0.84, CI 0.45-1.58, p = 0.59). Acculturation measures were available for 256 (92.4%) of Hispanic PWE: language preference was Spanish for 62.9%, 46.1% were foreign-born. Spanish-speaking Hispanics were less likely than English-speaking Hispanics to report elevated depressive symptoms (OR = 0.43, CI 0.19-0.97, p = 0.041); however, Hispanics who reported fair or poor health status had a four-fold higher depression prevalence compared to those who reported excellent or very good health status [reference group] (OR = 4.44, CI 1.50-13.18, p = 0.0071). Of the Hispanics who provided prior 30-day seizure data, ≥1 monthly seizure was independently associated with higher depression prevalence (OR = 3.11, CI 1.29-7.45, p = 0.01). Being foreign-born was not associated with elevated depressive symptoms or suicidal ideation prevalence.

CONCLUSIONS

In a large, geographically diverse sample of PWE, elevated depressive symptoms were significantly lower in Hispanics compared to non-Hispanics. Spanish language preference was associated with a lower prevalence of elevated depressive symptoms among Hispanic PWE. Future studies should include acculturation data to better screen for depression and suicidal ideation risk and optimize interventions for Hispanic PWE.

Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit

Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs).

Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration (“Active mode”).

Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6–20 years, and 67 adult aged 21–63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different (p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89–1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87–1.73]), higher (p < 0.001) than in the adult population (0.57, CI: [0.36–0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods (p < 0.001).

Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs.

Associating executive dysfunction with behavioral and socioemotional problems in children with epilepsy. A systematic review

As children with epilepsy may have a number of learning and behavioral problems, it is important that insight into the underlying neurocognitive differences in these children, which may underlie these areas of challenge is gained. Executive function (EF) problems particularly are associated with specific learning abilities as well as behavioral problems. We aim to review systematically the current status of empirical studies on the association between EF problems and behavior and socioemotional problems in children with epilepsy. After search, 26 empirical studies were identified, most of them of moderate quality. Overall, attention problems were the most reported cognitive deficit in test assessment and the most reported problem by parents. In 54% of the studies, children with epilepsy scored below average compared to controls/normative samples on different aspects of EF. Most studies reported behavior problems, which ranged from mild to severe. Forty-two percent of the studies specifically reported relationships between EF deficits and behavioral problems. In the remaining studies, below average neuropsychological functioning seemed to be accompanied by above average reported behavioral problems. The association was most pronounced for cognitive control and attention in relation to externalizing behavior problems. This cognitive control is also associated with social functioning. Relevant epilepsy variables in this relationship were early age at onset and high seizure frequency.Future research should distinguish specific aspects of EF and take age into account, as this provides more insight on the association between EF and behavior in pediatric epilepsy, which makes it possible to develop appropriate and early intervention.

The Managing Epilepsy Well (MEW) network database: Lessons learned in refining and implementing an integrated data tool in service of a national U.S. Research Collaborative

Epilepsy self-management (ESM) is the summative set of behaviors that people with epilepsy use to cope with seizures and optimize health. This report describes the implementation and evolution of the Managing Epilepsy Well Network Database (MEW DB), an integrated data resource intended to advance knowledge on ESM. The MEW DB utilizes a three-tiered (Tier 1-3) system of data organization, with tiers of data generally increasing in ascending complexity or collection burden. A MEW DB Steering Committee (SC) establishes consensus on planned analyses using a standardized new analysis request template. The data management structure facilitates harmonization and integration of additional data, or to update the database as new data become available. The current MEW DB comprises 1,563 people with epilepsy. Mean age was 39.9 years, 64.9% women (N = 1006), 12.8% African American (N = 170), 22.2% Hispanic (N = 306). On average, individuals have lived with epilepsy since their early 20s and are prescribed between 1 and 2 antiepileptic drugs. The MEW DB spans multiple socio-ecological levels to provide a robust multi-tiered framework for studying ESM. A total of 41 common data elements have been identified through iterative consensus. This integrated database takes advantage of an extensive collective background of archival evidence in ESM and brings together engaged investigators to build a dataset that represents diverse types of individuals with epilepsy, targets health domains important to ESM, and facilitates analyses that would not be possible with sites operating independently. Overall, the MEW DB serves the greater mission of this research collaborative and has potential to advance ESM research.

A New Method Based on CEEMD Combined With Iterative Feature Reduction for Aided Diagnosis of Epileptic EEG

In the clinical diagnosis of epileptic diseases, the intelligent diagnosis of epileptic electroencephalogram (EEG) signals has become a research focus in the field of brain diseases. In order to solve the problem of time-consuming and easily influenced by human subjective factors, artificial intelligence pattern recognition algorithm has been applied to EEG signals recognition. However, at present, the common empirical mode decomposition (EMD) signal decomposition algorithm does not consider the problem of mode aliasing. The EEG features obtained by feature extraction may be mixed with some unimportant features that affect the classification accuracy. In this paper, we proposed a new method based on complementary ensemble empirical mode decomposition (CEEMD) combined with iterative feature reduction for aided diagnosis of epileptic EEG. First of all, the evaluation indexes of decomposing and reconstructing signals by several methods were compared. The CEEMD was selected as the decomposition method of the signals. Then, the support vector machine recursive elimination (SVM-RFE) was used to reduce 9 features extracted from EEG data. The support vector classification of the gray wolf optimizer (GWO-SVC) recognition model was established for different feature subsets. By comparing the classification accuracy of training set and test set of different feature subsets, and considering the complexity of the model reflected by the number of features selected by SVM-RFE, the analysis showed that the 6 feature subsets with fewer features and higher classification accuracy could reflect the key information of epileptic EEG. The accuracy of the training set classification was 99.38% and the test set was as high as 100%. The recognition time was only 1.6551 s. Finally, in order to verify the reliability of the algorithm proposed in this paper, the proposed algorithm compared with the classification model established by the raw EEG signals and the optimization model established by other intelligent optimization algorithms. It is found that the algorithm used in this paper has higher classification accuracy and faster recognition time than other processing methods. The experimental results show that CEEMD combined with SVM-RFE is feasible for rapid and accurate recognition of EEG signals, which provides a theoretical basis for the aided diagnosis of epilepsy.

Effects of a remotely delivered group-format epilepsy self-management program on adverse health outcomes in vulnerable people with epilepsy: A causal mediation analysis

BACKGROUND

People with epilepsy frequently experience negative health events (NHEs), such as emergency room visits or hospitalizations for epilepsy-related complications despite significant advances in care. We developed a novel remotely delivered group-format epilepsy self-management program ("Self-management for people with epilepsy and a history of negative health events"; SMART). In a 6-month randomized controlled trial (RCT), SMART participants had significant decreases in NHEs, as well changes in attitudes and behaviors compared to a wait-list (Sajatovic et al., 2018). This secondary analysis from the RCT characterizes the indirect causal effects of SMART on NHE improvements that may be mediated by specific improvements in self-management, self-efficacy, social support, quality of life, and depression symptom severity.

METHODS

Participants were adults with epilepsy and a NHE in the prior 6 months. There were 60 participants in each RCT arm (SMART versus wait-list) and assessments were conducted at baseline, 10 weeks and 24 weeks. The outcome was a binary variable measuring NHE improvement at week 24. A counterfactual-based mediation framework was used to determine whether improvements or changes in attitudes and behaviors at specific time points or across the study period, mediated the impact of SMART on NHE improvements.

RESULTS

At week 24, SMART contributed to significant improvements in NHEs compared to those in wait-list (odds ratio = 3.2, p = 0.015). SMART was significantly associated with improvements and changes in aspects of self-management, self-efficacy, quality of life, and depression symptom severity at week 10, and significant improvements between baseline and week 24 in overall self-management and quality of life. Mediation analyses demonstrated that a portion of the effect (∼20-30 %; p < 0.05) of SMART on NHE improvement was also indirectly mediated by early improvements in depression symptom severity and quality of life.

CONCLUSIONS

This mediation analysis of the SMART intervention demonstrates that in addition to its direct effect on improving NHEs in people with epilepsy, early improvements in depression symptom severity and quality of life indirectly mediated ∼20-30 % of the intervention's effect. These results demonstrate the promise of self-management approaches as a key component of an optimal healthcare model for people with epilepsy, particularly those with a recent history of NHEs.

The Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE-BD) project: Understanding older-age bipolar disorder by combining multiple datasets

OBJECTIVE

There is a dearth of research about the aging process among individuals with bipolar disorder (BD). One potential strategy to overcome the challenge of interpreting findings from existing limited older-age bipolar disorder (OABD) research studies is to pool or integrate data, taking advantage of potential overlap or similarities in assessment methods and harmonizing or cross-walking measurements where different measurement tools are used to evaluate overlapping construct domains. This report describes the methods and initial start-up activities of a first-ever initiative to create an integrated OABD-focused database, the Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE-BD) project.

METHODS

Building on preliminary work conducted by members of the International Society for Bipolar Disorders OABD taskforce, the GAGE-BD project will be operationalized in four stages intended to ready the dataset for hypothesis-driven analyses, establish a consortium of investigators to guide exploration, and set the stage for prospective investigation using a common dataset that will facilitate a high degree of generalizability.

RESULTS

Initial efforts in GAGE-BD have brought together 14 international investigators representing a broad geographic distribution and data on over 1,000 OABD. Start-up efforts include communication and guidance on meeting regulatory requirements, establishing a Steering Committee to guide an incremental analysis strategy, and learning from existing multisite data collaborations and other support resources.

DISCUSSION

The GAGE-BD project aims to advance understanding of associations between age, BD symptoms, medical burden, cognition and functioning across the life span and set the stage for future prospective research that can advance the understanding of OABD.