Zong N, Liu XY, Chen F. Risk factors for anxiety and depression in patients with epilepsy and their relationship with quality of life. World J Psychiatry 2026; 16(8): 118857 [DOI: 10.5498/wjp.118857]
Corresponding Author of This Article
Fang Chen, Associate Chief Nurse, Department of Nephrology, The Third Affiliated Hospital of Jinzhou Medical University, No. 2 Section 5, Heping Road, Linghe District, Jinzhou 121000, Liaoning Province, China. 18704162950@163.com
Research Domain of This Article
Psychology, Development
Article-Type of This Article
research-article
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
Zong N, Liu XY, Chen F. Risk factors for anxiety and depression in patients with epilepsy and their relationship with quality of life. World J Psychiatry 2026; 16(8): 118857 [DOI: 10.5498/wjp.118857]
Author contributions: Zong N performed the research and wrote the manuscript; Liu XY and Chen F reviewed and revised the manuscript; Zong N, Liu XY and Chen F designed the research study and analyzed the data; all authors have read and approved the final manuscript.
AI contribution statement: We used Grammarly and DeepL solely for English language polishing and translation assistance in this manuscript. No AI tool was used to generate any part of the main text, design the study, interpret results, or generate images.
Institutional review board statement: The research was reviewed and approved by the Medical Ethics Review Committee of the Third Affiliated Hospital of Jinzhou Medical University (approval No. JYDSY-KXYJ-IEC-2025-075).
Informed consent statement: All research participants or their legal guardians provided written informed consent prior to study registration.
Conflict-of-interest statement: No conflict of interest is associated with this work.
Data sharing statement: No other data available.
Corresponding author: Fang Chen, Associate Chief Nurse, Department of Nephrology, The Third Affiliated Hospital of Jinzhou Medical University, No. 2 Section 5, Heping Road, Linghe District, Jinzhou 121000, Liaoning Province, China. 18704162950@163.com
Received: February 10, 2026 Revised: March 10, 2026 Accepted: March 30, 2026 Published online: August 19, 2026 Processing time: 169 Days and 23.2 Hours
Abstract
BACKGROUND
Epilepsy is frequently complicated by anxiety and depression, which severely undermine patients’ quality of life (QoL). However, the independent risk factors for these psychiatric comorbidities and their combined effect on QoL in Chinese patients with epilepsy remain to be comprehensively elucidated.
AIM
To identify risk factors for epilepsy-related anxiety/depression and their QoL correlation.
METHODS
A single-center, retrospective study was conducted involving 106 adult patients with epilepsy. Demographic and clinical data were collected. Standardized self-rating scales were used to assess anxiety and depressive symptoms, and QoL was evaluated using the Quality of Life in Epilepsy-31 inventory. Univariate and multivariate logistic regression analyses identified independent risk factors. The performance of the predictive model for comorbidity was evaluated using receiver operating characteristic analysis, calibration, and decision curve analysis. Correlations between psychological symptoms and QoL domains were assessed using Pearson correlation.
RESULTS
The rates for anxiety, depression, and their co-occurrence were 38.68%, 34.91%, and 26.42%, respectively. Multivariate analysis identified unemployment and high seizure frequency (> 2/month) as independent risk factors for anxiety. For depression, unemployment, polytherapy (> 2 antiepileptic drugs), and high seizure frequency were significant risk factors. Incorporation of unemployment, polytherapy, high seizure frequency, and long duration of disease in the comorbidity model predicted outcome well (area under the receiver operating characteristic curve = 0.81) thereby indicating its good clinical utility. Patients who had comorbidity scored significantly lower on the Quality of Life in Epilepsy-31 across all domains. Anxiety and depression severity correlated significantly negatively with all domains of QoL (P < 0.05). The strongest correlation was found between depression and total QoL which had (r = -0.65, P < 0.001).
CONCLUSION
Anxiety and depression are highly prevalent in Chinese epilepsy patients, driven by unemployment, uncontrolled seizures, polytherapy, and chronicity, and profoundly impair QoL, underscoring the need for integrated care.
Core Tip: This study identifies unemployment, high seizure frequency, polytherapy, and longer disease duration as key risk factors for anxiety and depression in Chinese patients with epilepsy. A clinical prediction model based on these factors effectively identifies high-risk individuals. Critically, comorbid anxiety and depression severely impair quality of life across all domains. These findings underscore the necessity of integrated care, combining optimized seizure control, medication review, and mental health support, to improve the holistic well-being of people living with epilepsy.
Citation: Zong N, Liu XY, Chen F. Risk factors for anxiety and depression in patients with epilepsy and their relationship with quality of life. World J Psychiatry 2026; 16(8): 118857
Epilepsy is a popular chronic illness that occurs when an individual becomes rheumatic or has the potential to have an epileptic seizure[1]. This has to do with the neurobiological, cognitive, psychological, and social consequences of having epilepsy[1,2]. According to global epidemiological estimates, epilepsy affects about 51.7 million people worldwide, mostly in low-income and middle-income countries[3]. There are more than 13 million patients suffering from epilepsy in China[4]. For a considerable period, seizure control and optimization of antiepileptic drugs have been the focus of clinical studies[5,6]. As the approach to disease management changes from simply controlling seizures to lifelong comprehensive care, clinical research is demonstrating the higher frequency of psychiatric and psychological comorbidities[7]. Studies show that more than 20% of epilepsy patients suffer from anxiety and depressive disorders, with lifetime incidence rates of 30% or higher[8,9]. Their profound impact often outweighs that of seizure frequency, making them a paramount concern in comprehensive epilepsy care.
The complex, two-way neurobiological mechanisms between epilepsy and the affective disorders involved. Shared mechanisms in disease development may offer an explanation for this comorbidity. Shared mechanisms in disease development may offer an explanation for this comorbidity. Limitations in the monoaminergic neurotransmitter systems and limbic circuitry[10], neuroinflammatory processes taking place (e.g. interleukin-17A) which may directly lead to epilepsy-associated anxiety[11], and dysregulation of the hypothalamic-pituitary-adrenal axis linking chronic stress to hippocampal damage and mood disorders[12]. It is now clear that other psychosocial risk factors (like stigma, social isolation, and unemployment) interact with these biological risk factors to promote anxiety and depressive disorders[13]. Despite their high prevalence and devastating consequences, these psychiatric comorbidities remain substantially under-diagnosed and under-treated in routine neurology practice. This clinical gap perpetuates a vicious cycle: Untreated depression and anxiety can lead to poor medication adherence, reduced self-efficacy, and potentially worsened seizure control, further deteriorating the patient’s overall health status and prognosis[14].
Notably, depression and anxiety have been identified as a strong independent predictor of poor quality of life (QoL) in epilepsy patients, either directly or through other mechanisms[15,16]. The negative effect affects various domains of QoL, such as emotional, cognitive, social relationship, and global satisfaction. A number of studies have been undertaken to examine the risk factors for such comorbidities. However, findings regarding specific demographic and clinical factors (e.g. seizure frequency and type, duration, polytherapy) were often inconsistent or specific to small populations[17]. It is necessary to conduct a thorough investigation using powerful real-world data from a Chinese clinical cohort of patients. As a result, this will be a retrospective study that aims to evaluate the independent demographic and clinical risk factors for depression and anxiety. It also aims to quantitatively shine a light on specific links between the severity of these symptoms and the QoL dimensions. The findings are expected to furnish clinicians with a pragmatic risk-assessment instrument for the early detection of susceptible patients. This will enhance the integrated, personalized management strategies targeting neurological and psychological health and, in turn, improve overall prognosis and long-term quality-of-life outcomes of people living with epilepsy.
MATERIALS AND METHODS
Study population
This single-center, retrospective study consecutively enrolled 106 patients diagnosed with epilepsy who were admitted to the Third Affiliated Hospital of Jinzhou Medical University between January 2022 and January 2025. All patients were diagnosed in accordance with the 2017 Epilepsy Diagnostic Criteria issued by the International League Against Epilepsy, and their medical records and nursing follow-up files were complete. The study protocol was approved by the Medical Ethics Review Committee of the Third Affiliated Hospital of Jinzhou Medical University (approval No. JYDSY-KXYJ-IEC-2025-075). Due to the retrospective nature of the study, informed consent from patients was waived, but all data were de-identified to protect patient privacy, in strict compliance with the Declaration of Helsinki and relevant regulations on medical research privacy protection.
Inclusion and exclusion criteria
Inclusion criteria: (1) Aged ≥ 18 years, regardless of gender; (2) Diagnosed with epilepsy in line with the International League Against Epilepsy 2017 diagnostic criteria[18], confirmed by electroencephalogram and cranial magnetic resonance imaging; (3) Disease duration ≥ 6 months; and (4) Complete medical records and follow-up data, allowing extraction of all observation indicators required for this study.
Exclusion criteria: (1) Complicated with other central nervous system diseases, such as brain tumors, cerebrovascular malformations, Alzheimer’s disease, etc.; (2) Complicated with severe physical diseases that may affect emotional state, including malignant tumors, severe heart, liver, and kidney failure, hyperthyroidism/hypothyroidism, etc.; (3) A history of primary mental diseases such as schizophrenia and bipolar affective disorder; (4) A history of alcohol or drug dependence; and (5) Severe cognitive impairment, unable to complete scale assessment.
Data collection
Two trained clinical researchers meticulously extracted data using a pre-piloted standard data extraction form. Before the data extraction, the two researchers had a structured training session which included: (1) Reviewing of the study protocol and data collection form in detail; and (2) Practicing data extraction on five sample patient files. Finally, a calibration meeting was held to rectify any differences in their data extraction process. The sources of data consisted of the electronic medical record, inpatient, outpatient follow-up records, laboratory test reports, imaging, and nursing assessment records. The information obtained included: (1) Demographic characteristics: Age, gender, marital status, educational level and employment status; (2) Clinical features of epilepsy: Duration of the disease, type of seizure, monthly seizure frequency and number of antiepileptic drugs; and (3) Psychological and QoL assessment: Anxiety level, depression status and QoL of the patients were assessed using standardized scales. The researchers independently reviewed a random sample of records to ensure the quality of the data. The inter-rater reliability is excellent.
Assessment instruments
Assessment of anxiety symptoms: The Self-Rating Anxiety Scale (SAS)[19] was adopted, a classic self-rating tool for anxiety symptom assessment, with good reliability and validity in Chinese epilepsy patients[20]. The scale includes 20 items, using a 4-point scoring method consistent with Self-Rating Depression Scale (SDS). The standard score is also obtained by multiplying the raw score by 1.25, with a standard score ≥ 50 points considered as having anxiety symptoms: (1) 50-59 points for mild anxiety; (2) 60-69 points for moderate anxiety; and (3) ≥ 70 points for severe anxiety.
Assessment of depression symptoms: The SDS was used[21], which is a widely used self-rating scale for evaluating the severity of depression symptoms, with good reliability and validity in the Chinese population[22]. The scale consists of 20 items, each scored on a 4-point scale (1 = rarely, 2 = sometimes, 3 = often, 4 = almost always). The total raw score is 20-80 points, and the standard score is calculated by multiplying the raw score by 1.25, with a standard score range of 25-100 points. A standard score ≥ 53 points was defined as having depression symptoms, among which 53-62 points were mild depression, 63-72 points were moderate depression, and ≥ 73 points were severe depression.
Assessment of QoL: The Quality of Life in Epilepsy-31 (QOLIE-31) was used[23], which is a specific QoL assessment scale for epilepsy patients, and its Chinese version has been verified to have good reliability and validity[24]. The scale contains 31 items, divided into 7 dimensions: (1) Worry about seizures (4 items); (2) Overall health status (3 items); (3) Emotional health (5 items); (4) Energy/fatigue (4 items); (5) Cognitive function (6 items); (6) Drug effects (4 items); and (7) Social function (5 items). Each item is scored on a 7-point scale (1 = worst, 7 = best). The score of each dimension is the average score of all items in that dimension, and the total scale score is the average score of all items, with higher scores indicating better QoL.
Statistical analysis
All statistical analyses were performed using SPSS Statistics (Version 26.0, IBM Corp., Armonk, NY, United States) and R software (version 4.2.2). Continuous data with a normal distribution were expressed as mean ± SD and compared using the independent samples t-test. Categorical data were presented as n (%) and compared using the χ2 or Fisher’s exact test. Univariate logistic regression identified candidate variables (P < 0.05) for inclusion in multivariate logistic regression models, which employed a backward stepwise method to identify independent risk factors. Results were reported as odds ratio (OR) with 95%CIs. The performance of the final model for comorbidity was assessed by the area under the receiver operating characteristic curve (AUC), calibration plot, Hosmer-Lemeshow test, and decision curve analysis. Group differences in QoL scores were compared using t-tests, and relationships between scale scores were examined using Pearson correlation analysis. A P value < 0.05 was considered statistically significant.
RESULTS
Prevalence of anxiety and depression in epilepsy patients
A total of 106 patients with epilepsy were included in this study. Among them, 41 (38.68%) screened positive for anxiety symptoms (SAS score ≥ 50), while 37 (34.91%) screened positive for depressive symptoms (SDS score ≥ 53). Notably, 28 patients (26.42%) were diagnosed with comorbid anxiety and depression (both SAS ≥ 50 and SDS ≥ 53). The internal consistency of the SAS and SDS scales was good, with Cronbach’s alpha values of 0.78 and 0.81, respectively. The mean scores were 48.39 ± 8.01 for SAS and 49.93 ± 9.13 for SDS. The majority of patients with anxiety or depression fell into the mild category (Table 1).
Table 1 Current situation of anxiety and depression.
Univariate analysis of anxiety, depression, and comorbid anxiety/depression
Univariate analysis results are presented in Table 2. For anxiety symptoms, statistically significant differences were observed between the anxiety-positive and anxiety-negative groups in terms of disease duration (7.61 ± 1.61 years vs 7.05 ± 1.26 years, P = 0.047), employment status (unemployed: 43.90% vs 24.62%, P = 0.038), and monthly seizure frequency (> 2 episodes: 58.54% vs 35.38%, P = 0.019). For depressive symptoms, the depression-positive group showed statistically significant differences compared with the depression-negative group in education level (high school and above: 72.97% vs 49.28%, P = 0.019), employment status (unemployed: 45.95% vs 24.64%, P = 0.025), number of antiepileptic drugs (> 2: 64.86% vs 40.58%, P = 0.017), and monthly seizure frequency (> 2 episodes: 62.16% vs 34.78%, P = 0.007). Disease duration approached statistical significance (7.62 ± 1.44 years vs 7.07 ± 1.39 years, P = 0.058). For comorbid anxiety/depression, significant differences were detected in disease duration (7.82 ± 1.54 years vs 7.06 ± 1.33 years, P = 0.015), employment status (unemployed: 50.00% vs 25.64%, P = 0.018), number of antiepileptic drugs (> 2: 67.86% vs 42.31%, P = 0.020), and monthly seizure frequency (> 2 episodes: 67.86% vs 35.90%, P = 0.003).
Table 2 Univariate analysis of anxiety and depression, n (%)/mean ± SD.
For depression, unemployment (OR = 3.16, 95%CI: 1.22-8.17, P = 0.017), use of more than two antiepileptic drugs (OR = 2.70, 95%CI: 1.08-6.71, P = 0.033), and monthly seizure frequency > 2 (OR = 3.37, 95%CI: 1.35-8.39, P = 0.009) were identified as independent risk factors (Table 4).
Independent risk factors for comorbid anxiety and depression
In the model for comorbid anxiety and depression, unemployment (OR = 3.77, 95%CI: 1.31-10.89, P = 0.014), polytherapy with > 2 antiepileptic drugs (OR = 3.14, 95%CI: 1.11-8.88, P = 0.030), higher seizure frequency (> 2 per month; OR = 5.32, 95%CI: 1.84-15.41, P = 0.002), and longer disease duration (OR = 1.53, 95%CI: 1.06-2.20, P = 0.023) were all independent risk factors (Table 5).
Table 5 Independent risk factors for anxiety/depression.
Predictive performance of the comorbidity risk model
The multivariate logistic regression model for predicting comorbid anxiety and depression demonstrated excellent discriminative ability, with an AUC of 0.81 (95%CI: 0.72-0.90, Figure 1A). The calibration curve showed good agreement between predicted and observed probabilities (Hosmer-Lemeshow test P = 0.92; Figure 1B). Decision curve analysis indicated that the model provided a net clinical benefit across a threshold probability range of 10%-60% (Figure 1C).
Figure 1 Predictive performance of the comorbidity risk model.
A: The multivariate logistic regression model for predicting comorbid anxiety and depression demonstrated excellent discriminative ability, with an area under the receiver operating characteristic curve of 0.81 (95%CI: 0.72-0.90); B: The calibration curve showed good agreement between predicted and observed probabilities (Hosmer-Lemeshow test P = 0.92); C: Decision curve analysis indicated that the model provided a net clinical benefit across a threshold probability range of 10%-60%. AUC: Area under the receiver operating characteristic curve.
Impact of comorbid anxiety and depression on QoL
Patients with comorbid anxiety and depression (n = 28) had significantly lower scores across all domains of the QOLIE-31 compared to those without either condition (n = 78; all P < 0.001). The most pronounced differences were observed in cognitive function (43.57 ± 9.72 vs 62.88 ± 12.58) and total score (51.37 ± 4.21 vs 62.97 ± 3.46, Table 6).
Table 6 Quality of life scores of patients with comorbid anxiety and depression in each dimension, mean ± SD.
Correlations between anxiety/depression severity and QoL
Both SAS and SDS scores were significantly negatively correlated with all QOLIE-31 domains and total score. SAS showed moderate to strong negative correlations, with the strongest association observed with the total score (r = -0.53, P < 0.001; Figure 2A). Similarly, SDS was strongly negatively correlated with the total score (r = -0.65, P < 0.001), as well as with cognitive function (r = -0.55, P < 0.001) and overall health (r = -0.45, P < 0.001; Figure 2B).
Figure 2 Correlations between anxiety/depression severity and quality of life.
Both Self-Rating Anxiety Scale and Self-Rating Depression Scale scores were significantly negatively correlated with all Quality of Life in Epilepsy-31 domains and total score. A: Self-Rating Anxiety Scale showed moderate to strong negative correlations, with the strongest association observed with the total score (r = -0.53, P < 0.001). B: Self-Rating Depression Scale was strongly negatively correlated with the total score (r = -0.65, P < 0.001), as well as with cognitive function (r = -0.55, P < 0.001) and overall health (r = -0.45, P < 0.001). aP < 0.05; bP < 0.01; cP < 0.001. SAS: Self-Rating Anxiety Scale; SDS: Self-Rating Depression Scale.
DISCUSSION
The management of epilepsy has gradually shifted from merely controlling epileptic seizures to focusing on the overall long-term health outcomes of patients[25]. Against this backdrop, this study focuses on the comorbidity of anxiety and depression, which is highly prevalent among epilepsy patients, and systematically explores its risk factors, predictive models, and its profound impact on QOL. Our findings revealed that among 106 adult patients with epilepsy, the prevalence rates of anxiety, depression, and their comorbidity were as high as 38.68%, 34.91%, and 26.42%, respectively, underscoring the severity of this issue in our Chinese epilepsy cohort. Through multivariate analysis, we identified unemployment, frequent epileptic seizures (more than 2 times per month), multi-drug combination therapy (more than 2 antiepileptic drugs), and a longer disease course as independent risk factors for comorbidities of anxiety and depression. The prediction model constructed based on these factors demonstrates good discrimination, calibration and clinical practicability. Most importantly, we quantified the comprehensive detrimental effects of anxious and depressive symptoms, particularly when co-occurring, across multiple health domains using the QOLIE-31 and correlation analyses. The following sections delve deeper into these core findings.
First, the socio-economic and treatment-related risk factors uncovered in this study reflect the multi-dimensional pathophysiology underlying the comorbidity between epilepsy and affective disorders. Unemployment emerged as a powerful socio-economic predictor, significantly associated with anxiety, depression, and their comorbidity. Many previous studies have also verified this point[26,27]. Gandy et al[28] reported that unemployment is the only independent predictor of anxiety in epilepsy patients, which is similar to the results of this study. This is not merely a social phenomenon but indicative of complex bidirectional pathways. On one hand, the stigma, cognitive impairments, and unpredictable nature of seizures directly contribute to employment difficulties or job loss[29]. On the other hand, the state of unemployment implies the absence of social roles and increased economic pressure, which are all classic psychosocial stressors that induce and aggravate anxiety and depression. This is consistent with the view reported by Wang et al[30] that the economic level serves as a predictor. Similarly, multidrug therapy (> 2 antiepileptic drugs) as a proxy indicator of disease severity and treatability, its association with depression and comorbidities suggests potential neurobiological and iatrogenic mechanisms[31]. Complex drug regimens may contribute to emotional distress through drug interactions, more pronounced side effects (e.g., somnolence, cognitive dulling), and the psychological impact of being labeled as having “drug-resistant” epilepsy, consistent with previous research on the burden of antiepileptic drugs and depressive symptoms[32,33].
Second, disease activity indicators, epitomized by high seizure frequency, represent a critical bridge connecting neurobiological and psychiatric comorbidity pathways. Our study found that a seizure frequency of > 2 per month was one of the most robust risk factors for anxiety, depression, and their comorbidity, with an OR as high as 5.32 in the comorbidity model. Sun et al[34] also reported the view that the frequency of epileptic seizures can serve as a predictor of comorbid depression. The limbic system, particularly the amygdala, hippocampus, and prefrontal cortex, constitutes not only a common origin or propagation pathway for epileptic discharges in focal epilepsies but also the core circuitry for emotional regulation[7,10]. Recurrent epileptic activity may directly disrupt the stability and function of these neural networks, leading to aberrant emotional processing[35]. Furthermore, the fear of seizures, feelings of loss of control, and lifestyle restrictions imposed by frequent seizures create chronic psychological stress, potentially leading to persistent activation of the hypothalamic-pituitary-adrenal axis, aberrant cortisol levels, and further impairment of hippocampal neuroplasticity, thereby establishing a vicious cycle[36]. Consequently, high seizure frequency represents a critical nexus where biological, psychological, and social mechanisms converge.
Third, the predictive model for comorbidity developed in this study and its strong association with QOL provide a complete toolchain for clinical practice, spanning from “risk identification” to “outcome assessment”. Our multivariate logistic regression model, incorporating four readily available clinical variables – unemployment, polytherapy, high seizure frequency, and disease duration – achieved an AUC of 0.81, indicating good predictive performance. Based on these findings, our study carries clear implications for clinical practice. We strongly recommend integrating routine screening for anxiety and depression into the standard follow-up care for patients with epilepsy. For patients possessing the identified high-risk factors (e.g., unemployment, poor seizure control, polytherapy), more proactive assessment and intervention should be initiated. Regarding treatment strategies, an integrated, “patient-centered” care model should be advocated. Collaboration between neurologists, psychiatrists, psychotherapists, and social workers is essential. Efforts should be made to optimize antiepileptic drug treatment plan, aiming to simplify therapy where possible to reduce iatrogenic burden without compromising seizure control. Concurrently, for diagnosed anxiety or depression, standardized psychotherapy and/or pharmacotherapy should be diligently pursued. Moreover, psychosocial interventions, including vocational rehabilitation, strengthening social support, and patient education, are crucial for addressing root issues like unemployment.
It is important to consider the weaknesses of the present study. The single-center, retrospective design and relatively small sample size may limit the statistical power and generalizability of our findings. Larger multicenter future studies need to be prospective and validate. Secondly, the use of self-rating scales (SAS/SDS), though common, cannot substitute for clinical diagnosis through a structured psychiatric interview, and can introduce misclassification bias. Finally, we note that our study did not control for other possible confounding factors related to the individual such as dimensions of social support, coping styles, etc., as well as types or serum levels of specific antiepileptic drugs, that may affect the development of mood disorders. Fourth, the cross-sectional design of the study prevents drawing firm causal conclusions about the associations between risk factors and comorbidity. The connection between depression and unemployment is probably bi-directional. Longitudinal research design and more comprehensive biomarkers (e.g., neuroimaging, inflammatory markers) and psychosocial variables should be considered to explain the underlying mechanisms better in the future.
CONCLUSION
Based on our findings, we conclude that anxiety and depression are highly prevalent among Chinese patients with epilepsy. We identified unemployment, high seizure frequency, polytherapy, and longer disease duration as significant independent risk factors for these comorbidities. The robust predictive model developed herein demonstrates strong clinical utility for early identification of at-risk individuals. Furthermore, the severe and pervasive negative impact of comorbid anxiety and depression on QoL across all measured domains underscores the critical need for integrated care. Consequently, we strongly advocate for the routine screening of psychiatric symptoms in epilepsy management protocols. A multidisciplinary approach that combines optimized seizure control, judicious medication review, and accessible mental health support is essential to mitigate these comorbidities and ultimately improve the overall prognosis and well-being of people living with epilepsy.
Roussos S, Gerogianni G, Vasilopoulos G, Koutelekos I, Pavlatou N, Kalogianni A, Toulia G, Zartaloudi A, Polikandrioti M. Factors Associated With Anxiety and Depression in Persons With Epilepsy (PWE).Cureus. 2024;16:e56401.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 3][Reference Citation Analysis (0)]
Fisher RS, Cross JH, French JA, Higurashi N, Hirsch E, Jansen FE, Lagae L, Moshé SL, Peltola J, Roulet Perez E, Scheffer IE, Zuberi SM. Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology.Epilepsia. 2017;58:522-530.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 2812][Cited by in RCA: 2399][Article Influence: 266.6][Reference Citation Analysis (0)]
Lozano-García A, González-Bono E, Cano-López I. Impact of interaction among antiseizure medication polytherapy, clinical, sociodemographic and psychological factors on quality of life in patients with epilepsy: A systematic review.Epilepsy Behav. 2025;162:110170.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 12][Reference Citation Analysis (0)]
