METHODS: This cross-sectional study recruited 120 adult PWE from the Neurology Clinic of the Universiti Kebangsaan Malaysia Medical Centre (UKMMC). Consent-taking was conducted via synchronous or asynchronous approaches, followed by a phone call interview session. The interview collected socio-demographic information, epilepsy-related variables, and vaccination-related variables. Univariate analysis and multiple logistic regression analysis were done to confirm factors associated with the AEFI of COVID-19 vaccination.
RESULTS: Among all types of COVID-19 vaccines, most of the PWE received the Cominarty® COVID-19 vaccination (52.5%). Overall, local AEFI was the quickest to develop, with an average onset within a day. PWE with normal body mass index (BMI) had a higher risk of developing both local and systemic AEFI compared to those underweight and obese PWE (OR: 15.09, 95% CI 1.70-134.28, P = 0.02).
SIGNIFICANCE: COVID-19 vaccines are safe for PWE. AEFI among PWE are similar to those of the general population following COVID-19 vaccination. Therefore, clinicians should encourage PWE to take COVID-19 vaccines.
MATERIALS AND METHODS: Patients with at least one EEG recording were recruited. The EEG and clinical data were collated.
RESULTS: Two hundred and fifty patients underwent EEG and 154 (61.6%) were found to have abnormal EEG. The abnormal changes consist of theta activity (79,31.6%), delta activity (20, 8%), focal discharges (41,16.4%) and generalised discharges (14, 5.6%). Older patients had 3.481 higher risk for EEG abnormalities, p=0.001. Patients who had focal seizures had 2.240 higher risk of having EEG abnormalities, p<0.001. Low protein level was a risk for EEG abnormalities, p=0.003.
CONCLUSION: This study emphasised that an abnormal EEG remains a useful tool in determining the likelihood for seizures in a hospital setting. The risk factors for EEG abnormality in hospitalised patients were age, focal seizures and low protein level. The EEG may have an important role as part of the workup in hospitalised patients to aid the clinician to tailor their management in a holistic manner.
METHODS: A cross-sectional study was carried out where routine EEG assessment was performed in 206 consecutive acute stroke patients without seizures. The demographic data and clinical stroke assessment were collated using the National Institutes of Health Stroke Scale (NIHSS) score with neuroimaging. Associations between EEG abnormalities and clinical features, stroke characteristics, and NIHSS scores were evaluated.
RESULTS: The mean age of the study population was 64.32 ± 12 years old, with 57.28% consisting of men. The median NIHSS score on admission was 6 (IQR 3-13). EEG was abnormal in more than half of the patients (106, 51.5%), which consisted of focal slowing (58, 28.2%) followed by generalized slowing (39, 18.9%) and epileptiform changes (9, 4.4%). NIHSS score was significantly associated with focal slowing (13 vs. 5, p
METHODS: A cross-sectional study on 284 epilepsy patients was performed in a local tertiary centre. The demographic and clinical epilepsy data were collected. The Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) questionnaires were utilised to determine the quality of life and daytime hypersomnolence of epilepsy patients, respectively.
RESULTS: Poor sleep quality was reported in 78 (27.5%) patients while daytime hypersomnolence was present in 17 (6%) patients. The predictors of poor sleep quality include structural causes (OR = 2.749; 95% CI: 1.436, 5.264, p = 0.002), generalised seizures (OR = 1.959, 95% CI: 1.04, 3.689, p = 0.037), and antiseizure medications such as Carbamazepine (OR = 2.34; 95% CI: 1.095, 5.001, p = 0.028) and Topiramate (OR 2.487; 95% CI: 1.028, 6.014, p = 0.043). Females are 3.797 times more likely score higher in ESS assessment (OR 3.797; 95% CI: 1.064, 13.555 p = 0.04).
DISCUSSION: Sleep disturbances frequently coexist with epilepsy. Patients should be actively evaluated using the PSQI and ESS questionnaires. It is imperative to identify the key factors that lead to reduced sleep quality and heightened daytime sleepiness in patients with epilepsy, as this is essential to properly manage their condition.
METHOD: This cross-sectional observational study was conducted at Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan, Malaysia, from April 2021 to April 2023. This study included patients aged ≥18 years with a preliminary diagnosis of delirium. Demographic and clinical data were collected along with EEG recordings evaluated by certified neurologists to classify abnormalities and compare the associated factors between patients with delirium with or without EEG abnormalities.
RESULTS: One hundred and twenty patients were recruited, with 80.0% displaying EEG abnormalities, mostly generalized slowing (moderate to severe) and primarily generalized slowing (mild to severe), and were characterized by theta activity. Age was significantly associated with EEG abnormalities, with patients aged 75 and older demonstrating the highest incidence (88.2%). The CAM scores were strongly correlated with EEG abnormalities (r = 0.639, P < 0.001) and was a predictor of EEG abnormalities (P < 0.012), indicating that EEG can complement clinical assessments for delirium. The Richmond Agitation and Sedation Scale (RASS) scores (r = -0.452, P < 0.001) and Barthel index (BI) (r = -0.582, P < 0.001) were negatively correlated with EEG abnormalities. Additionally, a longer hospitalization duration was associated with EEG abnormalities (r = 0.250, P = 0.006) and emerged as a predictor of such changes (P = 0.030).
CONCLUSION: EEG abnormalities are prevalent in patients with delirium, particularly in elderly patients. CAM scores and the duration of hospitalization are valuable predictors of EEG abnormalities. EEG can be an objective tool for enhancing delirium diagnosis and prognosis, thereby facilitating timely interventions.
PATIENTS AND METHODS: We designed a case-control study where patients admitted with PSS were recruited with consent. Controls admitted for stroke without seizure were then included. Suitability based on exclusion criteria was ensured before recording their sociodemographic and clinical data. An EEG was performed and read by two certified neurologists before the data was analyzed.
RESULTS: We recruited 180 participants, 90 cases and 90 matched controls. Gender (p=0.013), race (p=0.015), dyslipidemia (p<0.001), prior stroke (p<0.031), large artery atherosclerosis (p<0.001), small vessel occlusions (p<0.001), blood pressure on presentation (p<0.028) and thrombolysis administration (p<0.029) were significantly associated with the occurrence of PSS. An increase in odds of PSS was observed in the male gender (1.974), dyslipidemia (3.480), small vessel occlusions (4.578), and in participants with epileptiform changes on EEG (3.630). Conversely, lower odds of PSS were seen in participants with high blood pressure on presentation (0.505), large artery atherosclerosis (0.266), and those who underwent thrombolysis (0.319).
CONCLUSION: This study emphasized that identifying post-stroke seizures may be aided by EEGs and recognizing at-risk groups, which include males of Chinese descent in Asia, dyslipidemia, small vessel occlusions, those with low to normal blood pressure on presentation, and epileptiform changes in EEGs.
METHODS: In this case-control study, we analyzed data on adult patients aged 18 years and above hospitalized for COVID-19 infection with matched hospitalized controls. The demographic, clinical data and anxiety measures using the Generalized Anxiety Disorder-7 questionnaire were analyzed using univariate and multivariate analysis.
RESULTS: 86.6% in the COVID-19 group had anxiety, significantly higher than 13.4% in the control group (p = 0.001). The COVID-19 group was significantly associated with the GAD-7 severity (p = 0.001). The number of COVID-19 patients in the mild, moderate, and severe anxiety groups was 48 (84.2%), 37 (86%), and 18 (94.7%), respectively. Multiple logistic regression showed significant predictors for anxiety, including COVID-19 diagnosis and neurological symptoms. Anxiety was found 36.92 times higher in the patients with COVID-19 compared to those without COVID-19 (OR 36.92;95% CI 17.09, 79.78, p = 0.001). Patients with neurological symptoms were at risk of having anxiety (OR 2.94; 95% CI 1.03, 8.41, p = 0.044).
DISCUSSION: COVID-19 patients experience a significant disruption in psychosocial functioning due to hospitalization. The burden of anxiety is notably high, compounded by a diagnosis of COVID-19 itself and neurological symptomatology. Early psychiatric referrals are warranted for patients at risk of developing anxiety symptoms.
METHODS: This was a single-center cross-sectional study. We recruited 159 participants diagnosed with epilepsy on antiseizure medications (ASMs). We included those aged 18 years and above, excluding patients with long-term medical conditions that would affect vitamin D metabolism. Sociodemographic data and details of epilepsy were collated. Venous sampling was performed to analyze the levels of albumin-corrected calcium, phosphate, alkaline phosphatase, and 25-hydroxyvitamin D3 [25(OH)D]. Serum 25(OH)D level is defined as deficient (<20 ng/ml), insufficient (20-29 ng/ml), and sufficient (≥30 ng/ml).
RESULTS: The study reported that 73 (45.9%) participants had vitamin D deficiency, 38 (23.9%) had vitamin D insufficiency, and 48 (30.2%) patients had sufficient vitamin D levels. The predictors identified were PWE aged 18 to 44 years old (p = 0.001), female gender (OR 3.396, p = 0.002), and ethnicity (p
METHOD: This study included a total of 44 participants without subjective olfactory disturbances. Lavender and normal saline were used as the olfactory stimulant and control. Electroencephalogram was recorded and power spectra were analysed by the spectral analysis for each alpha, beta, delta, theta and gamma bandwidth frequency upon exposure to lavender and normal saline independently.
RESULTS: The oscillatory brain activities in response to the olfactory stimulant indicated that the lavender smell decreased the beta activity in the left frontal (F7 electrode) and central region (C3 electrode) with a reduction in the gamma activity in the right parietal region (P4 electrode) (p < 0.05).
CONCLUSION: Olfactory stimulants result in changes of electrical brain activities in different brain regions, as evidenced by the topographical brain map and spectra analysis of each brain wave.
METHODS: A 2-year retrospective cohort study was employed, in which adults with a history of admission for traumatic brain injury (TBI) in 2019 and 2020 were contacted. Three hundred one individuals agreed to participate, with a median follow-up time of 30.75 months. The development of epilepsy was ascertained using a validated tool and confirmed by our neurologists during visits. Clinical psychologists assessed the patients' cognitive performance.
RESULTS: The 2-year cumulative incidence of PTE was 9.3% (95% confidence interval [CI] 5.9-12.7). The significant predictors of PTE were identified as a previous history of brain injury [hazard ratio [HR] 4.025, p = .021], and intraparenchymal hemorrhage (HR: 2.291, p = .036), after adjusting for other confounders. TBI patients with PTE performed significantly worse on the total ACE-III cognitive test (73.5 vs 87.0, p = .018), CTMT (27.5 vs 33.0, p = .044), and PSI (74.0 vs 86.0, p = .006) than TBI patients without PTE. A significantly higher percentage of individuals in the PTE group had cognitive impairment, compared to the non-PTE group based on ACE-III (53.6% vs 46.4%, p = .001) and PSI (70% vs 31.7%, p = .005) scores at 2 years post-TBI follow-up.
SIGNIFICANCE: This study emphasizes the link between TBI and PTE and the chance of developing cognitive impairment in the future. Clinicians can target interventions to prevent PTE by identifying specific predictors, which helps them make care decisions and develop therapies to improve patients' quality of life.
MATERIALS AND METHODS: We conducted a cross-sectional study of epilepsy patients from the neurology clinic, Hospital Canselor Tuanku Muhriz, Kuala Lumpur. The dental assessment included the decayed, missing and filled teeth (DMFT) criteria, as well as the plaque and periodontal status by dentists.
RESULTS: A total of 151 patients were recruited. The median age of onset of epilepsy was 16 (IQR 7-30) years, with generalised seizures at 59.6% and focal seizures in 40.4% of patients. Fair or poor oral health was present in 59 (39.1%) and gingivitis was seen in 65 (43%). The median DMFT decayed (D), missing (M) and filled teeth (FT) was 3 (IQR 1- 7). The median age of patients with fair or poor oral health was older (40 years, IQR 31-51) than the patients with excellent or good oral health (33 years, IQR 26-45), (p=0.014). Multivariate logistic regression analysis showed that carbamazepine (Odds Ratios, OR: 3.694; 95% Confidence Intervals, 95%CI: 1.314, 10.384) and hypertension (OR 6.484; 95%CI: 1.011, 41.594) are the risk factors for fair or poor oral health. Phenytoin use is 4.271 times more likely to develop gingivitis (OR 4.271; 95% CI: 1.252, 14.573).
CONCLUSION: Factors that contribute to fair or poor oral health include age, antiseizure medications like phenytoin and carbamazepine, and hypertension. Effective preventive strategies should be implemented to maintain oral health in epilepsy patients.