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.

Perceptions of perinatal depression among low-income mothers and families in Mumbai, India.

Perinatal depression yields long-term adverse effects on maternal mental health, maternal-child interactions, and child development. Research conducted in India suggests that the risk of perinatal depression may be associated with socio-cultural factors. This warrants an assessment of cultural attitudes towards perinatal depression. Our study examined the perceptions of perinatal depression among pregnant mothers (n = 46) admitted to antenatal and postnatal care wards, as well as their accompanying relatives (n = 60), at a government hospital in Mumbai, India. We administered structured interviews to understand the awareness levels of and attitudes towards perinatal depression. We found that a strong majority of the respondents (93%) were unfamiliar with the concept of perinatal depression. Roughly half of the respondents did not believe that women could experience mental health problems during and after delivery (45% and 50% respectively). A majority of the respondents (77%) believed that a mother does not love her baby if she is depressed after delivery. We additionally report qualitative findings from our open-ended questions on perceived symptomatology, post-delivery priorities, perceived treatment needs, and attitudes towards spousal or familial support. Findings highlight an exigency for researchers, clinicians, and mental health advocates to foster increased awareness of perinatal depression among expectant mothers and their family members. Accordingly, interventions to address perinatal depression should factor in the target population's awareness levels and sociocultural perceptions. Findings helped inform the development of psychoeducation and informational materials to target this need.

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.

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.

A study on binge eating behavior in urban adolescents.

AIM: The study was aimed at studying binge eating behavior in urban adolescents. This was in keeping with the increasing prevalence of obesity and lifestyle disorders amongst Indian population especially in adolescent groups. METHODS: 2000 adolescents English speaking schools in Mumbai were administered the Binge Eating Scale and the Eating Behaviors and Pattern Questionnaire after assent and parental consent. The prevalence of Binge eating behavior was estimated along with the socio-demographic data and other data from scale which was analyzed using the Chi square test and ANOVA where appropriate. RESULTS: The mean age of the total sample was 15.05 ± 1.65 years. Females reported higher Binge eating behavior than males and majority of the sample belonging to upper and lower middle- class families reported high binge eating behavior. The prevalence of Binge eating behavior was high with 1002 (50.1 %) adolescents reporting moderate binge eating while 736 (36.8 %) reporting severe binge eating. Significantly greater adolescents in the binge eating group reported irregular menses and being overweight and obese. There was a significantly greater proportion of adolescents in the binge eating group that ate out weekly and ate more fried food. CONCLUSION: There is an increasing trend of obesity and lifestyle disorders in adolescent population that can be linked to Binge eating behavior however, the role of binge eating in context of one of the potential cause of lifestyle disorders and obesity has not been studied in Indian adolescents despite the prevalence of Binge eating and overweight being high in this population, we need further larger studies to corroborate the findings of this study.

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.

A Study of Patterns of Platelet Counts in Alcohol Withdrawal.

AIMS: This study aimed to evaluate the patterns of platelet counts during the course of alcohol withdrawal and its relationship if any with liver enzymes. METHODOLOGY: Forty consecutive patients, with alcohol dependence according to the Diagnostic and Statistical Manual of Mental Disorders-fourth edition, Text Revision criteria, willing for a 10-day inpatient detoxification program and presenting within 12 h of the last consumption of alcohol were recruited in the study. Details about the diagnosis and alcohol consumption patterns were assessed with a detailed psychiatric interview. After admission, routine investigations (complete blood counts [CBCs] and liver function tests) were sent and records were kept. CBC was sent for platelet counts on the 2nd, 4th, 6th, 8th, and the 10th day of alcohol withdrawal. RESULTS: Nearly 40% of the patients developed delirium tremens (DT group) and rest had an uncomplicated alcohol withdrawal (ND group). Platelet counts at baseline and all the 4 days of collection were significantly lower in DT group than the ND group. Platelet counts increased gradually from baseline till 10th day of alcohol withdrawal, mean increase in platelet counts being 88.61 ± 11.60% (median: 61.11%, range [23.41-391.23%]). Platelet counts in 63% of the patients showed a drop on the 4th day of withdrawal before rising till the 10th day of alcohol withdrawal. Platelet counts were not affected by liver enzymes or other alcohol consumption patterns. CONCLUSIONS: Transient thrombocytopenia and reverse thrombocytosis during alcohol withdrawal are associated with an initial drop in platelet counts. The synchrony between the drop and the onset of DT needs to be evaluated.