Insomnia in epilepsy is associated with nocturnal seizures and anxiety.

PURPOSE: The purpose of this study was to identify the factors associated with insomnia in patients with epilepsy (PWE) and provide evidence for clinical prevention and treatment. METHODS: PWE who visited our epilepsy clinic from December 2021 to December 2022 were enrolled in our study. All participants completed the Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), Self-rating Anxiety Scale (SAS), and Self-rating Depression Scale (SDS). Based on their ISI scores, they were categorized into two groups: PWE with insomnia (ISI score ≥ 10) and PWE without insomnia (ISI score < 10). Univariate analysis and stepwise logistic regression analysis were conducted to identify the factors associated with insomnia in PWE. RESULTS: A total of 196 Chinese PWE were recruited in this study(men, 39.8 %). Of these, 39 PWE(19.9 %) had insomnia.The incidence of nocturnal seizures (43.6 %vs19.7 %), depression (46.2 %vs9.6 %), anxiety (59.0 %vs11.5 %), and excessive daytime sleepiness(EDS,28.2 %vs5.7 %) in PWE with insomnia were significantly higher than in those without insomnia(all p＜0.01). Univariate regression analysis showed that seizures greater than or equal to once per month, nocturnal seizures, anxiety, depression, and EDS may associate with insomnia in PWE(all p＜0.05). Stepwise logistic regression analysis demonstrated that nocturnal seizures (OR = 2.611,95 % CI 1.040-6.478, P = 0.038) and anxiety (mild OR = 4.830,95 %CI 1.741-13.186, P = 0.002;moderate OR = 24.239,95 %CI 4.719-183.935, P＜0.001; severe OR = 37.653,95 %CI 4.931-782.741, P = 0.002) were independently associated with insomnia in PWE. CONCLUSION: PWE with insomnia are more likely to experience depression and EDS. Nocturnal seizures and anxiety are identified as independent factors associated with insomnia in PWE. Furthermore, Anxiety has a greater impact on insomnia in PWE and the likelihood of insomnia has increased significantly with the aggravation of anxiety levels.

Insomnia in patients with MRI-negative epilepsy: The associated factors and 3D-pCASL cerebral blood flow perfusion changes.

OBJECTIVE: This study aimed to identify the factors associated with insomnia in MRI-negative epilepsy and uncover the underlying pathological mechanism driving insomnia within the context of epilepsy. METHODS: We conducted a retrospective study of patients with MRI-negative epilepsy recruited consecutively from December 2021 to December 2022. All subjects completed the Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), Self-rating Anxiety Scale (SAS), and Self-rating Depression Scale (SDS). Additionally, some subjects underwent the three-dimensional pseudo continuous arterial spin labeling(3D-pCASL) imaging examination. Bilateral frontal lobe, temporal lobe, hippocampus, thalamus, amygdala, caudate nucleus and lenticular nucleus were selected as regions of interest(ROI) and cerebral blood flow(CBF) values were measured in these regions. Subjects were classified into insomnia (ISI ≥ 10) or non-insomnia (ISI < 10) groups, and univariate and stepwise logistic regression analyses were employed to identify the factors associated with insomnia. Furthermore, CBF values in each ROI were compared between the two groups to identify the brain regions potentially related to the underlying pathological mechanism of insomnia in epilepsy. RESULTS: A total of 73 patients with MRI-negative epilepsy were recruited in this study(men, 49.3 %). Among them, 14 patients(19.2 %) had insomnia. Univariate regression revealed that nocturnal seizures, number of anti-seizure medication(ASM), anxiety, use of valproic acid(VPA), depression, and excessive daytime sleepiness(EDS) may be associated with insomnia in MRI-negative epilepsy (all p＜0.05). Stepwise regression demonstrated that nocturnal seizures, anxiety, and EDS were independently associated with insomnia in MRI-negative epilepsy (OR[95 %CI]P: 14.64[2.02-106.27]0.008,49.35[3.06-796.61]0.006, 13.28[1.25-140.66]0.032, respectively). Furthermore, CBF values in the left amygdala were significantly lower in patients with MRI- negative epilepsy who had insomnia. CONCLUSION: The prevalence of insomnia in MRI-negative epilepsy is 19.2%. Nocturnal seizures, anxiety, and EDS were independently associated with insomnia in MRI-negative epilepsy. The noteworthy decrease in CBF values in the left amygdala might be connected to the underlying pathological mechanism of insomnia in epilepsy.

Derivation and validation of a new prediction model for sudden unexpected death in epilepsy based on a longitudinal prospective population-based cohort.

OBJECTIVE: We conducted a population-based, prospective cohort study with a large sample size in Ningxia Province of the Northwest, a rural area in China, by developing a model to specifically assess risk factors of sudden unexpected death in epilepsy (SUDEP) in people with convulsive epilepsy by clinical variables. METHODS: Participants with convulsive epilepsy were recruited from January 1, 2008, to April 28, 2022, in rural Northwest China. They received regular assessments and management of epilepsy at the primary healthcare level and were followed up monthly. Information on the cause of death and relevant clinical details was obtained from death certificates or neurologist-conducted verbal autopsies. Survival analysis was employed to identify potential risk factors associated with SUDEP. RESULTS: Five variables were independently associated with SUDEP: generalized tonic-clonic seizures (GTCS) with ≥1 attack during the preceding month, GTCS with >3 attacks during the preceding year, body mass index (BMI) ≥24, age of onset ≤14 years, and duration >20 years. The area under receiver operator characteristic (ROC) curve (AUC) value (95% CI) of the model was 0.789 (0.735-0.843) in the derivation dataset and 0.830 (0.758-0.902) in the validation dataset. There was agreement between the observed and predicted probabilities of SUDEP. CONCLUSIONS: This study establishes that high GTCS frequency, early age of onset, long duration of epilepsy, and being overweight are associated with an increased risk of SUDEP in individuals with convulsive epilepsy. The study also developed and validated a personalized prediction model to accurately assess the risk of SUDEP.