METHODS: In the present cross-sectional study conducted at a tertiary teaching hospital, we aimed to investigate the prevalence and associated risk factors of undiagnosed depression in patients with epilepsy. We recruited patients with epilepsy aged 18-65 years after excluding those with background illnesses that may have contributed to the depressive symptoms. In total, 129 participants were recruited. We collected their demographic and clinical details before interviewing them using two questionnaires-the Neurological Disorders Depression Inventory for Epilepsy and Beck's Depression Inventory-II. Subsequently, if a participant screened positive for depression, the diagnosis was confirmed using the Diagnostic and Statistical Manual of Mental Disorders questionnaire, and a psychiatric clinic referral was offered.
RESULTS: Among the 129 participants, 9.3 % had undiagnosed major depressive disorder, and there was a female preponderance (66.7 %). The risk factors for undiagnosed depression among patients with epilepsy included low socioeconomic background (p = 0.026), generalized epilepsy (p = 0.036), and temporal lobe epilepsy (p = 0.010). Other variables such as being underweight and unmarried were more common among patients diagnosed with depression than without but no statistically significant relationship was found.
CONCLUSION: The prevalence of undiagnosed depression among patients with epilepsy was higher than that in population-based studies conducted in Western countries. Although questionnaires to screen for depression are widely available, some clinicians rarely use them and, therefore, fail to identify patients who may benefit from psychosocial support and treatment that would improve their disease outcomes and quality of life. The present study indicated that clinicians should use screening questionnaires to identify undiagnosed depression in people with epilepsy.
RESULTS: A clear separation was only observed between non-organic G and organic Z, which were then selected for further investigation in the fermentation of soybeans (GF and ZF). All four groups (G, Z, GF, ZF) were analyzed using nuclear magnetic resonance (NMR) spectroscopy along with liquid chromatography-tandem mass spectrometry (LC-MS/MS). In this way a total of 41 and 47 metabolites were identified respectively, with 12 in common. A clear variation (|log1.5 FC| > 2 and P
METHODS: As a result, we devised a retrospective study to look at the prevalence of presymptomatic patients with COVID-19 from data sourced via our medical records office. Subsequently, we identify early indicators of infection through demographic information, biochemical and radiological abnormalities which would allow early diagnosis and isolation. In addition, we will look into the clinical significance of this group and their outcome; if it differs from asymptomatic or symptomatic patients. Descriptive statistics were used in addition to tabulating the variables and corresponding values for reference. Variables are compared between the presymptomatic group and others via Chi-square testing and Fisher's exact test, accepting a p value of
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: 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.
MATERIALS AND METHODS: A single-centre case-control study was conducted in which patients admitted with stroke and healthy controls were recruited with consent. EEG was performed within 48 hours of admission for stroke patients and during outpatient assessments for controls. The EEG signals were pre-processed, analysed for spectral power using MATLAB, and plotted as topoplots.
RESULTS: A total of 194 participants were included and equally divided into patients with ischemic stroke and controls. The mean age of our study cohort was 55.11 years (SD±13.12), with a median National Institute of Health Stroke Scale (NIHSS) score of 6 (IQR 4-6) and lacunar stroke was the most common subtype (49.5%). Spectral analysis, with subsequent topographic brain mapping, highlighted clustering of important channels within the beta, alpha, and gamma bands.
CONCLUSION: qEEG analysis identified significant band frequencies of interest in post-stroke patients, suggesting a role as a diagnostic and prognostic tool. Topographic brain mapping provides a precise representation that can guide interventions and rehabilitation strategies. Future research should explore the use of machine learning for stroke detection and provide individualized treatment.
METHODS: A prospective cross-sectional study was conducted at a tertiary teaching hospital in Kuala Lumpur, Malaysia. Psychological screening was done using the Neurological Disorders Depression Inventory for Epilepsy (NDDI-E) and General Anxiety Disorder (GAD-7) questionnaire. Patients screened positive were offered psychiatric referrals and given an early psychiatric clinic appointment if they agreed to the referral. The reasons for those who refused the referral were noted.
RESULTS: Out of 585 patients, 91 (15.5 %) were screened positive for depression and/or anxiety. Eighteen patients were excluded from the study due to pre-existing psychiatric disorders. Of the remaining 73 patients, 23 (31.5 %) agreed to be referred to a psychiatrist. Only 17 (23.3 %) attended the psychiatrist appointment. A total of 11 (15.1 %) and one (1.4 %) patients were subsequently diagnosed with major depressive disorder and generalized anxiety disorder, respectively. Another 50 (68.5 %) patients were not referred to a psychiatrist, predominantly (n = 43, 58.9 %) due to reluctance to be referred to a psychiatrist. The reasons included avoidance of referral likely related to stigma (n = 22, 51.2 %), self-reliance, family and caregivers' disapproval of referral, and logistic difficulty. The mean scores in NDDI-E and GAD-7 in the referred group were higher than the not-referred group but not statistically significant (NDDI-E: 17.8 ± 3.6 vs. 16.5 ± 2.5, p = 0.072; GAD-7: 12.4 ± 5.70 vs. 9.8 ± 5.4, p = 0.061).
CONCLUSION: A significant number of PWE were reluctant to receive psychiatric referrals predominantly due to self-avoidance or family and caregiver disapproval of referral likely related to stigma. An integrated epilepsy care management model is recommended.