Non-binary gender, vulnerable populations and mental health during the COVID-19 pandemic: Data from the COVID-19 MEntal health inTernational for the general population (COMET-G) study

BACKGROUND

The COVID-19 pandemic has brought significant mental health challenges, particularly for vulnerable populations, including non-binary gender individuals. The COMET international study aimed to investigate specific risk factors for clinical depression or distress during the pandemic, also in these special populations.

METHODS

Chi-square tests were used for initial screening to select only those variables which would show an initial significance. Risk Ratios (RR) were calculated, and a Multiple Backward Stepwise Linear Regression Analysis (MBSLRA) was followed with those variables given significant results at screening and with the presence of distress or depression or the lack of both of them.

RESULTS

The most important risk factors for depression were female (RR = 1.59-5.49) and non-binary gender (RR = 1.56-7.41), unemployment (RR = 1.41-6.57), not working during lockdowns (RR = 1.43-5.79), bad general health (RR = 2.74-9.98), chronic somatic disorder (RR = 1.22-5.57), history of mental disorders (depression RR = 2.31-9.47; suicide attempt RR = 2.33-9.75; psychosis RR = 2.14-10.08; Bipolar disorder RR = 2.75-12.86), smoking status (RR = 1.15-5.31) and substance use (RR = 1.77-8.01). The risk factors for distress or depression that survived MBSLRA were younger age, being widowed, living alone, bad general health, being a carer, chronic somatic disorder, not working during lockdowns, being single, self-reported history of depression, bipolar disorder, self-harm, suicide attempts and of other mental disorders, smoking, alcohol, and substance use.

CONCLUSIONS

Targeted preventive interventions are crucial to safeguard the mental health of vulnerable groups, emphasizing the importance of diverse samples in future research.

LIMITATIONS

Online data collection may have resulted in the underrepresentation of certain population groups.

Somatic multicomorbidity and disability in patients with psychiatric disorders in comparison to the general population: a quasi-epidemiological investigation in 54,826 subjects from 40 countries (COMET-G study)

BACKGROUND

The prevalence of medical illnesses is high among patients with psychiatric disorders. The current study aimed to investigate multi-comorbidity in patients with psychiatric disorders in comparison to the general population. Secondary aims were to investigate factors associated with metabolic syndrome and treatment appropriateness of mental disorders.

METHODS

The sample included 54,826 subjects (64.73% females; 34.15% males; 1.11% nonbinary gender) from 40 countries (COMET-G study). The analysis was based on the registration of previous history that could serve as a fair approximation for the lifetime prevalence of various medical conditions.

RESULTS

About 24.5% reported a history of somatic and 26.14% of mental disorders. Mental disorders were by far the most prevalent group of medical conditions. Comorbidity of any somatic with any mental disorder was reported by 8.21%. One-third to almost two-thirds of somatic patients were also suffering from a mental disorder depending on the severity and multicomorbidity. Bipolar and psychotic patients and to a lesser extent depressives, manifested an earlier (15-20 years) manifestation of somatic multicomorbidity, severe disability, and probably earlier death. The overwhelming majority of patients with mental disorders were not receiving treatment or were being treated in a way that was not recommended. Antipsychotics and antidepressants were not related to the development of metabolic syndrome.

CONCLUSIONS

The finding that one-third to almost two-thirds of somatic patients also suffered from a mental disorder strongly suggests that psychiatry is the field with the most trans-specialty and interdisciplinary value and application points to the importance of teaching psychiatry and mental health in medical schools and also to the need for more technocratically oriented training of psychiatric residents.

Results of the COVID-19 mental health international for the health professionals (COMET-HP) study: depression, suicidal tendencies and conspiracism

INTRODUCTION

The current study aimed to investigate the rates of anxiety, clinical depression, and suicidality and their changes in health professionals during the COVID-19 outbreak.

MATERIALS AND METHODS

The data came from the larger COMET-G study. The study sample includes 12,792 health professionals from 40 countries (62.40% women aged 39.76 ± 11.70; 36.81% men aged 35.91 ± 11.00 and 0.78% non-binary gender aged 35.15 ± 13.03). Distress and clinical depression were identified with the use of a previously developed cut-off and algorithm, respectively.

STATISTICAL ANALYSIS

Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses, and Factorial Analysis of Variance (ANOVA) tested relations among variables.

RESULTS

Clinical depression was detected in 13.16% with male doctors and 'non-binary genders' having the lowest rates (7.89 and 5.88% respectively) and 'non-binary gender' nurses and administrative staff had the highest (37.50%); distress was present in 15.19%. A significant percentage reported a deterioration in mental state, family dynamics, and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (24.64% vs. 9.62%; p < 0.0001). Suicidal tendencies were at least doubled in terms of RASS scores. Approximately one-third of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop clinical depression was associated with a history of Bipolar disorder (RR = 4.23).

CONCLUSIONS

The current study reported findings in health care professionals similar in magnitude and quality to those reported earlier in the general population although rates of clinical depression, suicidal tendencies, and adherence to conspiracy theories were much lower. However, the general model of factors interplay seems to be the same and this could be of practical utility since many of these factors are modifiable.

Indian Psychiatric Society multicentre study: Diagnostic patterns, comorbidity and prescription practices for patients with Dementia

BACKGROUND

There are more than 5 million people with dementia in India. Multicentre studies looking at details of treatment for people with dementia In India are lacking. Clinical audit is a quality improvement process which aims to systematically assess, evaluate, and improve patient care. Evaluating current practice is the key to a clinical audit cycle.

AIM

This study aimed to assess the diagnostic patterns and prescribing practices of psychiatrists for patients with dementia in India.

METHOD

A retrospective case file study was conducted across several centers in India.

RESULTS

Information from the case records of 586 patients with dementia was obtained. Mean age of the patients was 71.14 years (standard deviation = 9.42). Three hundred twenty one (54.8%) were men. Alzheimer's disease (349; 59.6%) was the most frequent diagnosis followed by vascular dementia (117; 20%). Three hundred fifty five (60.6%) patients had medical disorders and 47.4% patients were taking medications for their medical conditions. Eighty one (69.2%) patients with vascular dementia had cardiovascular problems. Majority of the patients (524; 89.4%) were on medications for dementia. Most frequently prescribed treatment was Donepezil (230; 39.2%) followed by Donepezil-Memantine combination (225; 38.4%). Overall, 380 (64.8%) patients were on antipsychotics. Quetiapine (213, 36.3%) was the most frequently used antipsychotic. Overall, 113 (19.3%) patients were on antidepressants, 80 (13.7%) patients were on sedatives/hypnotics, and 16 (2.7%) patients were on mood stabilizers. Three hundred nineteen (55.4%) patients and caregivers of 374 (65%) patients were receiving psychosocial interventions.

CONCLUSIONS

Diagnostic and prescription patterns in dementia which emerged from this study are comparable to other studies both nationally and internationally. Comparing current practices at individual and national levels against accepted guidelines, obtaining feedback, identifying gaps and instituting remedial measures help to improve the standard of care provided.

Improving automated diagnosis of epilepsy from EEGs beyond IEDs

Objective.Clinical diagnosis of epilepsy relies partially on identifying interictal epileptiform discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased, tedious, and can delay the diagnosis procedure. Beyond automatically detecting IEDs, there are far fewer studies on automated methods to differentiate epileptic EEGs (potentially without IEDs) from normal EEGs. In addition, the diagnosis of epilepsy based on a single EEG tends to be low. Consequently, there is a strong need for automated systems for EEG interpretation. Traditionally, epilepsy diagnosis relies heavily on IEDs. However, since not all epileptic EEGs exhibit IEDs, it is essential to explore IED-independent EEG measures for epilepsy diagnosis. The main objective is to develop an automated system for detecting epileptic EEGs, both with or without IEDs. In order to detect epileptic EEGs without IEDs, it is crucial to include EEG features in the algorithm that are not directly related to IEDs.Approach.In this study, we explore the background characteristics of interictal EEG for automated and more reliable diagnosis of epilepsy. Specifically, we investigate features based on univariate temporal measures (UTMs), spectral, wavelet, Stockwell, connectivity, and graph metrics of EEGs, besides patient-related information (age and vigilance state). The evaluation is performed on a sizeable cohort of routine scalp EEGs (685 epileptic EEGs and 1229 normal EEGs) from five centers across Singapore, USA, and India.Main results.In comparison with the current literature, we obtained an improved Leave-One-Subject-Out (LOSO) cross-validation (CV) area under the curve (AUC) of 0.871 (Balanced Accuracy (BAC) of 80.9%) with a combination of three features (IED rate, and Daubechies and Morlet wavelets) for the classification of EEGs with IEDs vs. normal EEGs. The IED-independent feature UTM achieved a LOSO CV AUC of 0.809 (BAC of 74.4%). The inclusion of IED-independent features also helps to improve the EEG-level classification of epileptic EEGs with and without IEDs vs. normal EEGs, achieving an AUC of 0.822 (BAC of 77.6%) compared to 0.688 (BAC of 59.6%) for classification only based on the IED rate. Specifically, the addition of IED-independent features improved the BAC by 21% in detecting epileptic EEGs that do not contain IEDs.Significance.These results pave the way towards automated detection of epilepsy. We are one of the first to analyze epileptic EEGs without IEDs, thereby opening up an underexplored option in epilepsy diagnosis.

The effect of different degrees of lockdown and self-identified gender on anxiety, depression and suicidality during the COVID-19 pandemic: Data from the international COMET-G study

INTRODUCTION

During the COVID-19 pandemic various degrees of lockdown were applied by countries around the world. It is considered that such measures have an adverse effect on mental health but the relationship of measure intensity with the mental health effect has not been thoroughly studied. Here we report data from the larger COMET-G study pertaining to this question.

MATERIAL AND METHODS

During the COVID-19 pandemic, data were gathered with an online questionnaire from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Anxiety was measured with the STAI, depression with the CES-D and suicidality with the RASS. Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively.

STATISTICAL ANALYSIS

It included the calculation of Relative Risk (RR), Factorial ANOVA and Multiple backwards stepwise linear regression analysis RESULTS: Approximately two-thirds were currently living under significant restrictions due to lockdown. For both males and females the risk to develop clinical depression correlated significantly with each and every level of increasing lockdown degree (RR 1.72 and 1.90 respectively). The combined lockdown and psychiatric history increased RR to 6.88 The overall relationship of lockdown with severity of depression, though significant was small.

CONCLUSIONS

The current study is the first which reports an almost linear relationship between lockdown degree and effect in mental health. Our findings, support previous suggestions concerning the need for a proactive targeted intervention to protect mental health more specifically in vulnerable groups.

Suicide among Health Care Professionals—An Indian Perspective

Suicide is a global phenomenon that claims a person’s life every 40 s. The suicide-mortality rate in India is higher than the worldwide average for health care professionals (HCP). The treatment gap for mental health care is alarming, more than 80% in India which has improved compared to a decade. Among the methods chosen by HCPs for dying by suicide, violent suicide methods are more common. Hanging is the most common means, followed by lethal injection and jumping from a building. Among the medical students and professionals in India, academic stress is the leading cause of suicides, followed by mental illness and harassment. Stressfully long working hours, starvation for long hours, inadequate diet, sleep deprivation, inadequate rest, high levels of personal expectations, knowledge of lethal suicide methods, easy access to potentially fatal drugs, apathy, and fearlessness towards death are some of the contributing factors. Primary preventive measures to minimize suicides in HCPs would be to conduct stress-management workshops at an institutional level, routine mental health check-ups in healthcare institutions, mental-health screening for students enrolling into healthcare courses, and prompt referrals to mental healthcare facilities. In addition, telehealth services or mental health services for medical professionals of India are the need of the hour.

Results of the COVID-19 mental health international for the general population (COMET-G) study

INTRODUCTION

There are few published empirical data on the effects of COVID-19 on mental health, and until now, there is no large international study.

MATERIAL AND METHODS

During the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively.

STATISTICAL ANALYSIS

Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables.

RESULTS

Probable depression was detected in 17.80% and distress in 16.71%. A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed.

CONCLUSIONS

The final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them.

Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis

Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free versus IED EEGs. In this study, we evaluate features that may provide reliable IED detection and EEG classification. Specifically, we investigate the IED detector based on convolutional neural network (ConvNet) with different input features (temporal, spectral, and wavelet features). We explore different ConvNet architectures and types, including 1D (one-dimensional) ConvNet, 2D (two-dimensional) ConvNet, and noise injection at various layers. We evaluate the EEG classification performance on five independent datasets. The 1D ConvNet with preprocessed full-frequency EEG signal and frequency bands (delta, theta, alpha, beta) with Gaussian additive noise at the output layer achieved the best IED detection results with a false detection rate of 0.23/min at 90% sensitivity. The EEG classification system obtained a mean EEG classification Leave-One-Institution-Out (LOIO) cross-validation (CV) balanced accuracy (BAC) of 78.1% (area under the curve (AUC) of 0.839) and Leave-One-Subject-Out (LOSO) CV BAC of 79.5% (AUC of 0.856). Since the proposed classification system only takes a few seconds to analyze a 30-min routine EEG, it may help in reducing the human effort required for epilepsy diagnosis.

Multi-Center Validation Study of Automated Classification of Pathological Slowing in Adult Scalp Electroencephalograms Via Frequency Features

Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose three automated approaches to detect slowing in EEG: Threshold-based Detection System (TDS), Shallow Learning-based Detection System (SLDS), and Deep Learning-based Detection System (DLDS). These systems are evaluated on channel-, segment-, and EEG-level. The three systems perform prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level. We evaluate the systems through Leave-One-Subject-Out (LOSO) cross-validation (CV) and Leave-One-Institution-Out (LOIO) CV on four datasets from the US, Singapore, and India. The DLDS achieved the best overall results: LOIO CV mean balanced accuracy (BAC) of 71.9%, 75.5%, and 82.0% at channel-, segment- and EEG-level, and LOSO CV mean BAC of 73.6%, 77.2%, and 81.8% at channel-, segment-, and EEG-level. The channel- and segment-level performance is comparable to the intra-rater agreement (IRA) of an expert of 72.4% and 82%. The DLDS can process a 30 min EEG in 4 s and can be deployed to assist clinicians in interpreting EEGs.

Automated Adult Epilepsy Diagnostic Tool Based on Interictal Scalp Electroencephalogram Characteristics: A Six-Center Study

The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential. In this study, we propose a system to classify EEG as epileptic or normal based on multiple modalities extracted from the interictal EEG. The ensemble system consists of three components: a Convolutional Neural Network (CNN)-based IED detector, a Template Matching (TM)-based IED detector, and a spectral feature-based classifier. We evaluate the system on datasets from six centers from the USA, Singapore, and India. The system yields a mean Leave-One-Institution-Out (LOIO) cross-validation (CV) area under curve (AUC) of 0.826 (balanced accuracy (BAC) of 76.1%) and Leave-One-Subject-Out (LOSO) CV AUC of 0.812 (BAC of 74.8%). The LOIO results are found to be similar to the interrater agreement (IRA) reported in the literature for epileptic EEG classification. Moreover, as the proposed system can process routine EEGs in a few seconds, it may aid the clinicians in diagnosing epilepsy efficiently.

Post-Traumatic Stress Disorder (Ptsd) Following Burn Injuries: A Comprehensive Clinical Review

Burns are a global public health problem with a large number of psychosocial and psychological problems that may ensue after burn injuries. One of the commonest psychological problems seen after burn injuries is depression, anxiety and post-traumatic stress disorder (PTSD). The following paper reviews the existing literature on PTSD following burn injuries. The concept of PTSD is explained for the non-psychiatrist involved in burn rehabilitation. The various psychosocial factors that determine the causation and course of PTSD following burn injuries are discussed. PTSD following burn injuries in special populations like women, children and adolescents, intentional burns and self immolation are also discussed. The role of cultural factors in the development of PTSD are elucidated and future research needs are laid out. PTSD is a very common occurrence after burn injuries and needs a multidisciplinary team evaluation for its management.

Alcohol Withdrawal Cases As a Consequence of COVID-19: A Clinical Analysis

Background: The COVID-19 pandemic has been followed by the shutting down of bars and liquor shops. This condition has led to the acute unavailability of alcohol, and subsequently increasing the number of cases of alcohol withdrawal.  Objectives: This paper reports the clinical profile of cases of alcohol withdrawal presented to the psychiatry department following the non-availability of alcohol due to the COVID-19 lockdown. Methods: The patients were referred to the psychiatry department from the emergency medicine department and some of them directly to the psychiatry department. Thirty-two patients with alcohol use disorder and alcohol withdrawal were included in the study. The study data were collected using a semi-structured proforma and then were tabulated. The obtained data were assessed by the Chi-square test and unpaired t-test where appropriate. Results: The Mean±SD age of the study patients were 38.84±11.64 years. The Mean±SD years of consumption of alcohol was 13.50±7.8 years in the sample (range 1-30 years) and Mean±SD days of last consumption of alcohol was 3.88±1.8 days (range 2-10 days). There were no significant differences between stockers and non-stockers in various parameters.  Conclusion: Alcohol and substance withdrawal have increased in the wake of the lockdown and COVID-19 pandemic and there is a need for non-COVID-19 setups to be created to cater to the needs of these patients.

Risperidone-induced Enuresis in a 12-year-old Child

Risperidone has been documented to be effective in the management of behavior problems, aggression, and conduct disorder in children. While metabolic side effects like weight gain and obesity have been attributed to Risperidone use in children, side effects of the drug related to the urinary bladder are rare. We present a case of Risperidone-induced enuresis in a 12-year-old boy with conduct disorder that resolved completely after stopping the medication.

Paranoia in patients with gender dysphoria: A clinical exploration

BACKGROUND

Gender identity disorder (GID) is a distressing disorder characterized by a persistent unhappiness with one's own gender and a desire to be of the opposite gender as well as seeking sex reassignment surgery for the same. The aim of the study was to assess the Minnesota Multiphasic Personality Inventory (MMPI) profiles in patients with GID and compare with healthy normal population and also examine differences in the profiles based on original gender of the patients.

MATERIALS AND METHODS

A total of 56 patients with GID that fulfilled the Diagnostic and Statistical Manual of Mental Disorders 5 criteria for the same were participants of the study, and there were 54 control participants. They were administered the MMPI, and the scores across various scales were statistically analyzed.

RESULTS

It was seen that apart from masculinity feminity (Mf) scale, other scales such as Paranoia (Pa, P < 0.01), Schizophrenia (Sc, P = 0.01), and Psychopathic deviate (Pd, P < 0.01) were also elevated in many patients. Male patients seeking surgery to become female showed higher scores on Pa and Sc scales than female patients. On detailed inquiry, it was found that there was no evidence of psychosis clinically, and in fact, their paranoia was reality based.

CONCLUSION

MMPI profiles in patients with GID needs to interpreted with caution and clinicians must keep in mind that elevated Pa and Sc scales on the MMPI in these patients need not indicate a psychotic profile.

Executive function in patients with schizophrenia based on socio-occupational impairment: A cross-sectional study

BACKGROUND

Executive dysfunction deficit is the functionally most important cognitive deficit noted in schizophrenia. There is a dearth of Indian literature on the subject. The current study aimed at studying these executive functions in patients with schizophrenia in remission.

METHODOLOGY

Sixty outpatients with a diagnosis of schizophrenia as per international classification of diseases-10 criteria; in remission as measured by Positive and Negative Syndrome Scale scores were divided into two groups using the personal and social performance scale. The patients with and without socio-occupational impairment formed the two groups. All patients were administered the Wisconsin Card Sorting Test (WCST), Stroop test, Color Trails Test 1 and 2, Phonemic Fluency (Controlled Oral Word Association Test), and category fluency (animal names test) tests and the tower of London test to ascertain deficits in executive functions. The data obtained were subjected to statistical analysis.

RESULTS

The two groups were well matched. The group with socio-occupational impairment showed a lesser number of categories completed (P = 0.001), more perseverative errors (P = 0.001), and greater percentage of the same (P = 0.001) on the WCST. Statistically significant differences between both groups were observed for scores on phonemic fluency (P = 0.012) and category fluency (P = 0.049) tests as well as the Tower of London test (P = 0.021). They also showed differences on the Stroop test and Color Trail tests, but this was not statistically significant.

CONCLUSIONS

Performance on executive function tests is significantly correlated with functional outcome. It is important that future studies explore the role of these tests as a marker of socio-occupational impairment in schizophrenia.

Neutrophil-lymphocyte Ratio and C-reactive Protein Level in Patients with Major Depressive Disorder Before and After Pharmacotherapy

BACKGROUND

Neutrophil-lymphocyte ratio (NLR) and C-reactive protein (CRP) level are useful biomarkers of inflammation. This study aimed to assess NLR and CRP level in patients with major depressive disorder before and after pharmacotherapy to determine whether NLR or CRP could be used as biomarkers of severity of major depression and whether there was any sex difference.

METHODOLOGY

Patients with major depression who received no pharmacotherapy 1 month prior to the study were included. Their haemoglobin, total white blood cell count, neutrophil and lymphocyte counts, NLR, and CRP levels were evaluated at baseline and 12 weeks post pharmacotherapy, as were the Montgomery Asberg Rating Scale for Depression, the Scale for Impact of Suicidality Management and Assessment and Planning of Care (SIS-MAP), and the Clinical Global Impression Scale - Severity.

RESULTS

24 male and 26 female patients were included. At 12 weeks after pharmacotherapy, males had a higher haemoglobin level (p = 0.025), higher total white blood cell count (p = 0.018), and lower percentage of neutrophils (p = 0.019) than females. There was no sex difference in NLR or CRP. From baseline to 12 weeks, males had no significant change in any blood parameter, but females had a significantly greater increase in the percentage of neutrophils (p = 0.0001) and decrease in the percentage of lymphocytes (p = 0.012), resulting in a significantly increased NLR (p = 0.001). Both males and females had significant improvement on all 3 scales (p < 0.001). At 12 weeks, in males, the increase in NLR positively correlated with CRP as well as the Montgomery-Asberg Depression Rating Scale and the SIS-MAP, but not the Clinical Global Impression-Severity Scale. In females, the increase in NLR did not correlate with CRP or any of the scales.

CONCLUSION

In female patients, the NLR increased in response to antidepressant therapy while CRP remained unchanged. This indicated that inflammation has a role in the pathogenesis of major depression.

Psychosis in a Case of Dandy-Walker Syndrome: A Case Report

Dandy Walker Syndrome (DWS) is a congenital malformation with brain abnormalities, intellectual disabilities, epilepsy and visible structural changes in particular brain structures. We present here a case of psychosis in an 18-year-old male with DWS, epilepsy and intellectual disability. The purpose of this paper is to discuss the clinically relevant issues, psychopharmacological issues, neuropsychiatric manifestations and consultation liaison issues involved.

Excessive Daytime Sleepiness in Schizophrenia: A Naturalistic Clinical Study

INTRODUCTION

Excessive Daytime Sleepiness (EDS) and sleep problems are common in patients with schizophrenia. The symptom of EDS in schizophrenia can be attributed to various causes including neurobiological changes, sleep disorders, medication or as a symptom of schizophrenia itself. EDS as a symptom in schizophrenia has been understudied.

AIM

To assess the prevalence of EDS and to the study the same in patients with first episode and chronic schizophrenia.

MATERIALS AND METHODS

In this cross-sectional study 100 patients suffering from schizophrenia as per International Classification of Diseases (ICD-10) criteria were evaluated for sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and EDS using the Epworth Sleepiness Scale (ESS). The severity of illness was assessed by Positive and Negative Symptom Scale for Schizophrenia (PANSS) while cognition was assessed using the Frontal Assessment Battery (FAB) and the Trail Making Test A and B. The data was statistically analysed.

RESULTS

A total of 100 patients (72 male and 28 female) aged 18 to 64years (mean age 30.63 years) were studied. Poor sleep quality (PSQI > 6) was exhibited by 83% of patients. Excessive daytime sleepiness (ESS > 7) was found in 32% of patients. There was no statistically significant difference in various parameters according to the age, duration of illness or gender. However, first episode patients differed in having better sleep quality than patients with chronic schizophrenia (p=0.0002). Cognition was not affected by sleep quality.

CONCLUSION

A high prevalence of sleepiness and poor sleep quality was noted in the entire sample but it did not have any correlation with age and gender. It also did not affect the cognitive test scores. Further research in this area is warranted.