Diagnosis Of Psychiatric Disorders Research Articles

A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample

Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.

Non-pharmacological treatment of psychiatric disorders in a nationwide population

IntroductionNon-pharmacological treatment like psychotherapy is associated with less side effects than pharmacological treatment and is often considered first-line treatment towards psychiatric disorders. The extent and variation of psychotherapy treatment offered in Danish psychiatric clinics over time has not previously been studied.ObjectivesTo examine the nationwide use of psychotherapy treatment during 2001-2020 in individuals assigned with a psychiatric disorder diagnosis at Danish psychiatric clinics.MethodsAll Danish individuals aged ≥ 3 years, who were registered with 1) a psychiatric disorder diagnosis (F10-F99) or 2) had a first psychotherapy treatment during the study period 1 January 2001 to 31 December 2020, were identified in the Danish National Patient Registry.ResultsA total of 120,916 (27 %) study participants received psychotherapy treatment during the study period, most commonly individual psychotherapy (65 %) followed by group therapy (25 %). Adults (≥18 years) were more likely to receive therapy (34 %) than children and adolescents aged 3-17 years (15 %). The proportion of treated patients was highest among women (67 %) compared with men (33 %). The median age at first psychotherapy was 25 years (ranging from 19 to 33). 59 % of patients receiving psychotherapy had filled a psychotropic prescription within one year prior to therapy onset, particularly antidepressants (44 %) and antipsychotics (22 %).ConclusionsThe use of psychotherapy for treatment of psychiatric disorders is limited among Danish patients, although national clinical guidelines recommend it as first-line treatment of common conditions such as depressive, anxiety and obsessive-compulsive disorders.DisclosureNo significant relationships.

Epidemiological profile of patients with epilepsy attended in an emergency in a psychiatric hospital in 2020

IntroductionAccording to the Global Burden of Disease study (WHO, 2010), epilepsy is ranked as the second most impacting neurological disorder worldwide, in terms of disability-adjusted life years, and is often associated with psychiatric comorbidities, stigma and high economic costs. This frequent association between epilepsy and mental disorders is a fact, however, they are often underdiagnosed and undertreated in patients with epilepsy, which further reduces the quality of life of this population and induces the demand for psychiatric emergency care.ObjectivesTo give the comorbid relevance between epilepsy and mental disorders, this study aims to identify the main psychiatric illnesses associated with patients with epilepsy treated at the emergency of a psychiatric hospital in 2020. In addition to establishing the clinical and epidemiological factors related to this association.MethodsAll patients diagnosed with epilepsy (G40), according to the International Classification of Diseases, who underwent emergency care at the HABM, São José do Rio Preto, São Paulo, in 2020. Epidemiological and clinical data were collected.ResultsThere were 7258 consultations, with only 27 as cid G40. 71.4% were male, 55% single and age between 42-49 years old. 47.6% indicated psychiatric comorbidities (cid F06). 23.8% patients with both disorders were attended by psychiatrist.ConclusionsThe concomitant occurrence of psychiatric disorders and epilepsy has significant relevance. However, it is known that the diagnosis of psychiatric disorders in epileptic patients is sometimes late, poorly conducted or even underdiagnosed. Therefore, knowing the profile of patients with epilepsy allows us to identify the factors associated with the concomitant of psychiatric disorders.DisclosureNo significant relationships.

Clinical Feasibility of Speech Phenotyping for Remote Assessment of Neurodegenerative and Psychiatric Disorders (RHAPSODY): a study protocol

IntroductionThe diagnosis of neurodegenerative and psychiatric disorders (NPDs) in primary care can suffer from inefficiencies resulting in misdiagnoses and delayed diagnosis, limiting effective treatment options. The development of speech and language-based profiling biomarkers could aid in achieving earlier motor diagnosis for PD for instance, or more accurate diagnosis of clinically similar or late presenting NPDs.ObjectivesRHAPSODY aims to investigate the feasibility of the remote administration of a battery of speech tasks in eliciting continuous narrative speech across a range of NPDs. The project also aims to determine the feasibility of using acoustic and linguistic biomarkers from speech data to support the clinical assessment and disambiguation of common NPDsMethodsAll participants (n=250) will take part in a single virtual telemedicine video conference with a researcher in which they are screened and complete a battery of speech tasks, in addition to cohort-specific screening measures. Over the following month, participants will be asked to complete a series of short, self-administered speech assessments via a smartphone application.ResultsThe speech tasks will be audio-recorded and analysed on Novoic’s technology platform. Objectives will be analysed using measures including average length of speech elicitation for speech tasks, intra- and inter-subject variance, differences in linguistic patterns, and response rates to speech assessments.ConclusionsThe analyses could help to identify and validate speech-derived clinical biomarkers to support clinicians in detecting and disambiguating between NPDs with heterogeneous presentations. This should further support earlier intervention, improved treatment options and improved quality of life.DisclosureIn terms of significant financial interest and relationships, it is emphasised that the private organisation Novoic, who aim to develop speech algorithms for diagnostic use, is the study’s sponsor and employees or former employees of this company comprise

Emergency mental health care for chi̇ldren and adolescents outside of regular working hours: 7 years outcomes from a tertiary hospital.

This study aims to define the clinical characteristics and management strategies of children and adolescents presenting with psychiatric crises to the emergency department (ED) of a tertiary health care facility outside of working hours, and to identify predictors of multiple ED visits among them. From January 2012 to December 2018, retrospective records of patients presenting with psychiatric symptoms to the ED and examined by a child psychiatrist after 5p.m. on weekdays and for 24h on weekends and public holidays were analyzed. Our sample consisted of 1576 visits and 1364 patient (Female:Male=1.8:1, mean age=14.86±2.72). The most common reason for visits was self-injurious thought or behaviors (SITB), and the most common diagnosis was depression. While depression was statistically more common in girls, attention deficit hyperactivity disorder, autism and/or intellectual disability (ASD/ID), psychotic disorders, and bipolar disorder were more common in boys. The forensic evaluation was the most common reason for visits among children younger than 6 years old. Of visits, 23% transferred to hospitalization. A history of mental health contact was the lowest in depression (37.5%), psychosis (34.1%), and substance use disorders (33%). Of patients, 10.8% had multiple visits. A history of mental health contacts, conduct disorder, ASD/ID, bipolar disorder, psychotic disorder, and dissociative disorder were predictors of multiple visits to ED with psychiatric reasons. Emergency mental health care outside of regular working hours can be a critical step in the diagnosis and treatment of serious psychiatric disorders in children and adolescents.

Psychiatric Morbidity Among the Patients of First Ever Ischaemic Stroke

AimsTo evaluate psychiatric morbidity among the patients of first ever ischemic strokeMethodsThis sectional comparative study was carried out in the Department of Psychiatry, Sylhet MAG Osmani Medical College Hospital, Sylhet during the period from 1st July 2013 to 30th June 2014. Sixty six ischaemic stroke patients of first attack between 2 weeks to 2 years of stroke, aged above 18 years irrespective of sex and 66 accompanying healthy person of the patients and other patients without any kind of stroke matching age and sex fulfilling inclusion and exclusion criteria were taken in Group-A and Group-B respectively. Diagnosis of ischaemic stroke was made in these patients by the consultant neurologists reviewing the history, clinical examination and accompanying investigations reports specially CT scan of brain. Psychiatric assessment was done using General Health Questionnaire (GHQ12) as screening tool. All GHQ12 positive cases were evaluated using mental state examination and recorded in a MSE sheet. Diagnosis of psychiatric disorders of all respondents was confirmed by psychiatrist according to DSM-5 criteria.ResultsThe patients with ischaemic stroke and control subjects were similar in age [57.6 (SD ± 5.5) years vs 57.1 (SD ± 4.5) years; p > 0.130] and sex [48 (72.7%) male and 18 (27.3%) female vs 45 (68.2%) male and 21 (31.8%) female; p = 0.567]. Comorbid psychiatric disorder was found in 23 (34.8%) patients of ischaemic stroke and 9 (13.6%) control subjects. The comorbid psychiatric disorder was significantly higher in patients of ischaemic stroke than that of control g subjects (p = 0.004). Comorbid specific psychiatric disorders were generalized anxiety disorder in 9 (13.6%) and major depressive disorder in 14 (21.2%) in stroke group; while comorbid specific psychiatric disorders were generalized anxiety disorder in 2 (3.0%) and major depressive disorder in 7 (10.6%) respondents in control group (p < 0.013).ConclusionComorbid psychiatric disorders are quite common among patients with first ever ischaemic stroke in the form of major depressive disorder and generalized anxiety disorder. Therefore, attention should be paid to the anxiety and depressive symptoms in stroke units and try to relieve the patients’ emotional stress and personal suffering, which could improve their neurological outcome.

Hyper-connectivity between the left motor cortex and prefrontal cortex is associated with the severity of dysfunction of the descending pain modulatory system in fibromyalgia.

IntroductionThe association between descending pain modulatory system (DPMS) dysfunction and fibromyalgia has been previously described, but more studies are required on its relationship with aberrant functional connectivity (FC) between the motor and prefrontal cortices.ObjectivesThe objective of this cross-sectional observational study was to compare the intra- and interhemispheric FC between the bilateral motor and prefrontal cortices in women with fibromyalgia, comparing responders and nonresponders to the conditioned pain modulation (CPM) test.MethodsA cross-sectional sample of 37 women (23 responders and 14 nonresponders to the CPM test) with fibromyalgia diagnosed according to the American College of Rheumatology criteria underwent a standardized clinical assessment and an FC analysis using functional near-infrared spectroscopy. DPMS function was inferred through responses to the CPM test, which were induced by hand immersion in cold water (0–1°C). A multivariate analysis of covariance for main effects between responders and nonresponders was conducted using the diagnosis of multiple psychiatric disorders and the use of opioid and nonopioid analgesics as covariates. In addition, we analyzed the interaction between the CPM test response and the presence of multiple psychiatric diagnoses.ResultsNonresponders showed increased FC between the left motor cortex (lMC) and the left prefrontal cortex (lPFC) (t = −2.476, p = 0.01) and right prefrontal cortex (rPFC) (t = −2.363, p = 0.02), even when both were considered as covariates in the regression analysis (lMC–lPFC: β = −0.127, t = −2.425, p = 0.021; lMC–rPFC: β = −0.122, t = −2.222, p = 0.033). Regarding main effects, a significant difference was only observed for lMC–lPFC (p = 0.035). A significant interaction was observed between the psychiatric disorders and nonresponse to the CPM test in lMC−lPFC (β = −0.222, t = −2.275, p = 0.03) and lMC−rPFC (β = −0.211, t = −2.2, p = 0.035). Additionally, a significant interaction was observed between the CPM test and FC in these two region-of-interest combinations, despite the psychiatric diagnoses (lMC−lPFC: β = −0.516, t = −2.447, p = 0.02; lMC−rPFC: β = −0.582, t = −2.805, p = 0.008).ConclusionsHigher FC between the lMC and the bilateral PFC may be a neural marker of DPMS dysfunction in women with fibromyalgia, although its interplay with psychiatric diagnoses also seems to influence this association.

0487 Exploration of Sleep Problems and Medication Use for Youth Residing in Juvenile Justice Facilities

Abstract Introduction Insufficient and disordered sleep are common among developing adolescents and can result in poor health and behavioral consequences. Previous studies have examined sleep and adolescent criminal behavior; however, little is known about adolescent sleep difficulties or disorders while residing in juvenile detention and treatment facilities. The current study explores psychiatric and sleep disorder diagnoses and medication use of youth under the care of the Department of Juvenile Services (DJS). Methods Participants were recruited from 11 detention and treatment facilities across Maryland. Youth (N = 67) were 13-19 years old (M = 16.8, SD = 1.2) and 84% male and 16% female. Racial Backgrounds: 55% Black, 18% White, 14% Multiracial, and 12% Other. A Healthcare staff member from each facility completed an online medical questionnaire regarding each youth’s sleep history, medical diagnoses, and current medications. Results The most common youth diagnoses were Insomnia (N = 26), ADHD (N = 26), and Anxiety (N = 12) with 72% of youth having more than one psychiatric/sleep disorder diagnosis. The most frequently used medications were melatonin (N = 23), trazodone (N = 12), and quetiapine (N=7) and 58% were on more than one sleep/psychotropic medication. Healthcare staff ordered 25 behavioral sleep studies (e.g., DJS behavioral sleep studies consists of night resident staff tracking if youth is asleep/awake while making rounds), resulting in diagnoses of insomnia (N=9) and parasomnia (N=1). Youth with sleep studies were prescribed the following medications: trazodone (N = 5), melatonin (N = 3), other (e.g., methylphenidate, clonidine, N = 11). Conclusion These preliminary findings suggest that youth are experiencing poor sleep quality while residing in juvenile justice facilities; over a third of the youth are struggling with insomnia and are prescribed Melatonin. Further data analyses will provide a better understanding of the youths’ sleep problems and the effects on their overall health and well-being. Support (If Any) This research is made possible by the American Academy of Sleep Medicine (AASM) Foundation Award #22-CS-19 and Department of Juvenile Services’ supportive collaboration.

Paternal and maternal psychiatric disorders associated with offspring autism spectrum disorders: A case-control study

A family history of psychiatric diseases was suggested as one risk factor for autism spectrum disorders (ASD). Our aim was to assess the association of paternal and maternal diagnosis of psychiatric disorders with the risk of ASD in offspring in Taiwan. We conducted a population-based case-control study. Using several linked national databases, we obtained 1,000,939 singleton birth records born between 2004 and 2008. We followed these children up to 2015 for cases of ASD, using diagnostic codes in the National Health Insurance databases. There were 8,933 ASD cases and each case was matched to ten controls by sex and year of birth. We extracted their parental diagnosis of psychiatric disorders and performed conditional logistic regression models to assess the association of interest. Our sample included 8,933 cases and 89,330 controls. Eighty-six percent of the sample were boys. After adjustment for parental age, family income, and urbanization, we found that parental psychiatric diseases were significantly associated with ASD, including schizophrenic and psychotic disorders, mood, anxiety and personality disorders, with adjusted odds ratios ranging from 1.32 to 2.39. Notably, the effect estimates were all larger for maternal diagnosis than paternal diagnosis when stratified by mothers or fathers. Cases of ASD are more likely to be born to parents with psychiatric disorders than their counterparts. Maternal psychiatric diagnosis seems to have a larger influence than paternal diagnosis. Both genetics and maternal environmental factors may contribute to the association observed between parental psychiatric diseases and child ASD.

Recurrent Injecting Drug Use as a Mediator between Psychiatric Disorder and Non-Fatal Overdose

Background Unintentional drug overdose has increased markedly in the United States. Studies document an association between psychiatric disorder and unintentional overdose; we extend this research through a preliminary test of a causal model of recurrent injection drug use mediating this relationship. Methods In a cross-sectional study of 241 adults in New York City with a possible current substance use disorder, we conducted conventional and Imai’s mediation analyses to examine if psychiatric disorder is associated with increased prevalence of ever overdosing and if recurrent injection drug use mediates this association. Our cross-sectional data permit the first step of assessing causal models: testing if statistical associations are consistent with the model. Results Fifty-eight percent of the sample endorsed previous psychiatric disorder diagnosis and 35.7% reported ever overdosing. Imai’s mediation analysis showed that, adjusting for covariates, the total association between psychiatric diagnosis and ever overdosing (adjusted prevalence difference [aPD] = 0.16, 95% CI 0.04–0.28) was composed of a direct effect (aPD = 0.09, 95% CI −0.03 − 0.21, p = 0.136) and an indirect effect (aPD = 0.07, 95% CI 0.02–0.13). Recurrent injecting drug use contributed to 42% (ratio of indirect effect to total effect; 95% CI 12 − 100%, p = 0.02) of the association between psychiatric diagnosis and ever overdosing. Conventional mediation analysis produced similar results. Conclusions Our results provide a warrant for taking the necessary next step for assessing a causal model using longitudinal data, potentially providing a strong rationale for intervening on psychiatric disorders to stem overdose.

Detecting autism from picture book narratives using deep neural utterance embeddings.

Deficits in the ability to use language in social contexts, including storytelling skills, are observed across the autism spectrum. Development in machine-learning approaches may contribute to clinical psychology and psychiatry, given its potential to support decisions concerning the diagnosis and treatment of psychiatric conditions and disorders. To evaluate the usefulness of deep neural networks for detecting autism spectrum disorder (ASD) from textual utterances, specifically from narrations produced by individuals with ASD. We examined two text encoders: Embeddings from Language Models (ELMo) and Universal Sentence Encoder (USE), and three classification algorithms: XGBoost, support vector machines, and dense neural network layer. We aimed to classify 25 participants with ASD and 25 participants with typical development (TD) based on their narrations produced during the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) picture book task. The results of computational approaches were compared with the results of standardized testing and classifications made by two psychiatrists (raters). The raters were asked to read utterances produced by a participant (without an examiner's statements and additional information) and assign a participant to one of the two groups: ASD or with typical development (TD). The computer-based models had higher sensitivity, specificity, positive predictive values and negative predictive values than the raters, and lower than the two standardized instruments: ADOS-2 and Social Communication Questionnaire (SCQ). Our findings lay the groundwork for future studies involving deep neural network-based text representation models as tools for augmenting the ASD diagnosis or screening. Both ELMo and USE text encoders provided promising specificities, sensitivities, positive predictive values and negative predictive values. Our results indicate the usefulness of page-level embeddings for utterance representation in ADOS-2 picture book task. What is already known on this subject Deficits in the use of language in social contexts, and narrative ability in particular, are observed across the autism spectrum. Most research on narrative skills has applied hand-coding methods. Hitherto, machine-learning methods were used mostly for image recognition problems and data from screening questionnaires for ASD classification. Detection of mental and developmental disorders from textual input is an emerging field for machine and deep-learning methods. What this paper adds to existing knowledge This study explored the ability of several types of deep neural network-based text representation models to detect ASD. Both ELMo and USE provided the most promising values of specificity, sensitivity, positive predictive values and negative predictive values. What are the potential or actual clinical implications of this work? Competitive accuracy, repeatability, speed and ease of operation are all advantages of computerized methods. They allow for objective and quantitative assessment of narrative ability and complex language skills. Deep neural network-based text representation models could in the future support clinicians and augment the decision-making process related to ASD diagnosis, screening and intervention planning.

Effects of the COVID‐19 pandemic on clinically diagnosed psychiatric disorders in persons with type 2 diabetes

AimsTo examine whether the incidence rates of diagnosed depression, anxiety disorders and stress reactions, as well as prescription rates of antidepressants and anxiolytics were higher during the COVID‐19 pandemic than before in persons with type 2 diabetes in Germany. Contrary to earlier studies, clinical diagnoses of psychiatric disorders (ICD classification) were used.MethodsThe German Disease Analyzer (DA) database is an outpatient database containing routine data on patients´ diseases and treatments provided by a representative panel of physician practices selected from across Germany. We assessed incidence rates of depressive disorders (ICD‐10: F32, F33), anxiety disorders (F41) and stress reactions (F43) in quarters from January 2019 to March 2021 in 95,765 people with type 2 diabetes included in the DA in 2019 (mean age 68.9 years, 58% men). Prescription rates of antidepressants and anxiolytics in quarters from January 2020 to March 2021 were compared with prescription rates from 1 year earlier.ResultsDuring the study period, the incidence rate of newly diagnosed depressive disorders in persons with type 2 diabetes declined slightly, while the incidence rates of anxiety and stress disorders remained largely constant. The rates of new prescriptions for antidepressants and anxiolytics were lower in all quarters of 2020 and in the first quarter of 2021 than in the quarters 1 year earlier. Diabetes‐related complications were more prevalent in persons with incident psychiatric disorders than in those without.ConclusionsNo increase in the incidence rates of clinically diagnosed psychiatric disorders was observed during the COVID‐19 pandemic in Germany in persons with type 2 diabetes.

Self-reporting of psychiatric illness in an online patient registry is a good indicator of the existence of psychiatric illness

Online registries offer many advantages for research, including the ability to efficiently assess large numbers of individuals and identify potential participants for clinical trials and genetic studies. Of particular interest is the validity and utility of self-endorsement of psychiatric disorders in online registries, which, while increasingly more common, remain understudied. We thus assessed the comparability of prevalence estimated from self-endorsement of psychiatric disorders in one such registry, the Brain Health Registry (BHR) to prevalence computed from large US-based epidemiological studies and the degree to which BHR participants report psychiatric disorders consistently. We also examined the concordance between self-report and clinically determined diagnoses of various DSM-5 psychiatric disorders in a subset of participants who underwent direct assessments and identified possible reasons for discordance.Rates of self-reported psychiatric disorders were closest to previously reported population prevalence rates when endorsed at multiple timepoints, and accuracy was at least 70% for all except Hoarding Disorder as compared to the clinical diagnoses. Clinical data suggested that self-endorsement of a given psychiatric diagnosis was indicative of the presence of a closely related condition, although not necessarily for the specific disorder, with the exception of major depressive disorder, panic disorder, and hoarding disorder, which had high positive predictive values (85%, 73%, 100%, respectively).We conclude that self-reporting of psychiatric conditions in an online setting is a fair indicator of psychopathology, but should be accompanied by more in-depth interviews if using data from a participant for a specific disorder.

Association of Psychiatric Disorders With Incidence of SARS-CoV-2 Breakthrough Infection Among Vaccinated Adults

Psychiatric disorders may be associated with an increased risk for SARS-CoV-2 breakthrough infection after vaccination, but no studies have tested this hypothesis. To evaluate whether past diagnoses of psychiatric disorders are associated with an increased incidence of SARS-CoV-2 breakthrough infection among fully vaccinated individuals. This retrospective cohort study included data from the administrative and electronic health records of US Department of Veterans Affairs (VA) patients from February 20, 2020, to November 16, 2021. Participants included 263 697 patients who accessed VA health care during the study period, had at least 1 SARS-CoV-2 test recorded in the electronic health record, had no record of SARS-CoV-2 infection prior to vaccination, and had completed a full SARS-CoV-2 vaccination regimen 14 days or more prior. Psychiatric disorder diagnoses in the past 5 years, including depressive, posttraumatic stress, anxiety, adjustment, alcohol use, substance use, bipolar, psychotic, attention-deficit/hyperactivity, dissociative, and eating disorders. SARS-CoV-2 breakthrough infections, defined as positive SARS-CoV-2 tests, among fully vaccinated individuals. Of 263 697 fully vaccinated VA patients (239 539 men [90.8%]; mean [SD] age, 66.2 [13.8] years), 135 481 (51.4%) had at least 1 psychiatric disorder diagnosis, and 39 109 (14.8%) developed a breakthrough infection. A diagnosis of any psychiatric disorder was associated with increased incidence of breakthrough infection, both in models adjusted for potential confounders (adjusted relative risk [aRR], 1.07; 95% CI, 1.05-1.09) and additionally adjusted for medical comorbidities and smoking (aRR, 1.03; 95% CI, 1.01-1.05). Most specific psychiatric disorder diagnoses were associated with an increased incidence of breakthrough infection, with the highest relative risk observed for adjustment disorder (aRR, 1.13; 95% CI, 1.10-1.16) and substance use disorders (aRR, 1.16; 95% CI, 1.12-1.21) in fully adjusted models. Stratifying the sample at 65 years of age revealed that associations between psychiatric diagnoses and incident breakthrough infection were present in both age groups but were stronger and robust to adjustment for medical comorbidities and smoking among older patients. This cohort study suggests that psychiatric disorder diagnoses were associated with an increased incidence of SARS-CoV-2 breakthrough infection among VA patients, with the strongest associations observed for older individuals. Individuals with psychiatric disorders may be at heightened risk for contracting COVID-19 even after vaccination, suggesting the need for targeted prevention efforts.

Comparing Online and On-Site Cognitive Behavior Therapy in Major Depressive Disorder: Protocol for a Noninferiority Randomized Controlled Trial.

BackgroundThe incidence of mental health disorders in Canada is increasing with costs of CAD $51 billion (US $40 billion) per year. Depression is the most prevalent cause of disability while cognitive behavioral therapy (CBT) is the best validated behavioral depression treatment. CBT, when combined with mindfulness meditation (CBT-M), has strong evidence for increased efficacy. While randomized controlled trials (RCTs) have demonstrated online CBT-M efficacy, comparisons with in-office delivery are lacking.ObjectiveThe aim of this research is to assess whether online group CBT-M (with standard psychiatric care) is non-inferior in efficacy and more cost-effective than office-based, on-site group CBT-M at post-intervention and 6-months follow-up in major depressive disorder. The study will also assess whether digitally recorded data (ie, online workbooks completed, Fitbit step count, and online text messages) predict depression symptom reduction in online participants.MethodsThis single-center, two-arm, noninferiority RCT employs assessor-blinded and self-report outcomes and economic evaluation. The research site is the Centre for Addiction and Mental Health (Toronto), a research-based psychiatry institution where participants will be identified from service wait lists and through contacts with other Toronto clinics. Inclusion criteria are as follows: (1) aged 18-60 years, any ethnicity; (2) Beck Depression Inventory-II (BDI-II) of mild severity (score ≥14) with no upper severity limit; (3) Mini-International Neuropsychiatric Interview-confirmed, psychiatric major depressive disorder diagnosis; (4) fluent in English. All patients are diagnosed by staff psychiatrists. Exclusion criteria are as follows: (1) receipt of weekly structured psychotherapy; (2) observation of Diagnostic and Statistical Manual of Mental Disorders (5th Edition) criteria for severe alcohol or substance use disorder (in past 3 months), borderline personality disorder, schizophrenia (or other primary psychotic disorder), bipolar disorder, or obsessive-compulsive disorder; (3) clinically significant suicidal ideation (imminent intent or attempted suicide in the past 6 months); and (4) treatment-resistant depression. All participants receive standard psychiatric care, experimental participants receive online group CBT-M, and controls receive standard care in-office group CBT-M. The online group program (in collaboration with NexJ Health, Inc) combines smartphone and computer-accessed workbooks with mental health phone counselling (16 hours in 16 weeks) that coordinates software interactions (eg, secure text messaging and Fitbit-tracked walking). The primary outcome is BDI-II, and secondary outcomes are anxiety (Beck Anxiety Inventory), depression (ie, Quick Inventory of Depressive Symptomatology and 17-item Hamilton Depression Rating Scale), mindfulness (Five-Facet Mindfulness Questionnaire), quality of life (European Quality of Life Five Dimension), and pain (Brief Pain Inventory).ResultsBased on prior studies with the BDI-II and 80% power to reject an inferiority hypothesis with a 1-sided type I error rate of 5%, a sample of 78 per group is adequate to detect small-to-medium–effect sizes.ConclusionsThis study assesses online CBT-M efficacy and noninferiority in relation to in-person CBT, and the cost-effectiveness of both interventions.Trial RegistrationClinicalTrials.gov NCT04825535; https://www.clinicaltrials.gov/ct2/show/NCT04825535International Registered Report Identifier (IRRID)DERR1-10.2196/29726

Cumulative burden of psychiatric disorders and self-harm across 26 adult cancers

Cancer is a life-altering event causing considerable psychological distress. However, information on the total burden of psychiatric disorders across all common adult cancers and therapy exposures has remained scarce. Here, we estimated the risk of self-harm after incident psychiatric disorder diagnosis in patients with cancer and the risk of unnatural deaths after self-harm in 459,542 individuals. Depression was the most common psychiatric disorder in patients with cancer. Patients who received chemotherapy, radiotherapy and surgery had the highest cumulative burden of psychiatric disorders. Patients treated with alkylating agent chemotherapeutics had the highest burden of psychiatric disorders, whereas those treated with kinase inhibitors had the lowest burden. All mental illnesses were associated with an increased risk of subsequent self-harm, where the highest risk was observed within 12 months of the mental illness diagnosis. Patients who harmed themselves were 6.8 times more likely to die of unnatural causes of death compared with controls within 12 months of self-harm (hazard ratio (HR), 6.8; 95% confidence interval (CI), 4.3–10.7). The risk of unnatural death after 12 months was markedly lower (HR, 2.0; 95% CI, 1.5–2.7). We provide an extensive knowledge base to help inform collaborative cancer-psychiatric care initiatives by prioritizing patients who are most at risk.

Association of chronic non-cancer pain status and buprenorphine treatment retention among individuals with opioid use disorder: Results from electronic health record data

BackgroundAlthough chronic non-cancer pain (CNCP) is common among individuals with opioid use disorder (OUD), its impact on buprenorphine treatment retention is unclear. The goal of this study was to use electronic health record (EHR) data to examine the association of CNCP status and 6-month buprenorphine retention among patients with OUD. MethodsWe analyzed EHR data of patients with OUD who received buprenorphine treatment in an academic healthcare system between 2010 and 2020 (N = 676). We used Kaplan-Meier curves and Cox proportional hazards regression to estimate risk of buprenorphine treatment discontinuation (≥90 days between subsequent prescriptions). We used Poisson regression to estimate the association of CNCP and the number of buprenorphine prescriptions over 6 months. ResultsCompared to those without CNCP, a higher proportion of patients with CNCP were of older age and had comorbid diagnoses for psychiatric and substance use disorders. There were no differences in the probability of buprenorphine treatment continuation over 6 months by CNCP status (p = 0.15). In the adjusted cox regression model, the presence of CNCP was not associated with time to buprenorphine treatment discontinuation (HR = 0.90, p = 0.28). CNCP status was associated with a higher number of prescriptions over 6 months (IRR = 1.20, p < 0.01). ConclusionsThese findings suggest that the presence of CNCP alone cannot be reliably associated with buprenorphine retention in patients with OUD. Nonetheless, providers should be aware of the association between CNCP and greater psychiatric comorbidity among patients with OUD when developing treatment plans. Research on the influence of additional characteristics of CNCP on treatment retention is needed.

The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes

SignificanceHuman sleep phenotypes are diversified by genetic and environmental factors, and a quantitative classification of sleep phenotypes would lead to the advancement of biomedical mechanisms underlying human sleep diversity. To achieve that, a pipeline of data analysis, including a state-of-the-art sleep/wake classification algorithm, the uniform manifold approximation and projection (UMAP) dimension reduction method, and the density-based spatial clustering of applications with noise (DBSCAN) clustering method, was applied to the 100,000-arm acceleration dataset. This revealed 16 clusters, including seven different insomnia-like phenotypes. This kind of quantitative pipeline of sleep analysis is expected to promote data-based diagnosis of sleep disorders and psychiatric disorders that tend to be complicated by sleep disorders.

Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry: Scoping Review.

BackgroundMental health disorders are a leading cause of medical disabilities across an individual’s lifespan. This burden is particularly substantial in children and adolescents because of challenges in diagnosis and the lack of precision medicine approaches. However, the widespread adoption of wearable devices (eg, smart watches) that are conducive for artificial intelligence applications to remotely diagnose and manage psychiatric disorders in children and adolescents is promising.ObjectiveThis study aims to conduct a scoping review to study, characterize, and identify areas of innovations with wearable devices that can augment current in-person physician assessments to individualize diagnosis and management of psychiatric disorders in child and adolescent psychiatry.MethodsThis scoping review used information from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive search of several databases from 2011 to June 25, 2021, limited to the English language and excluding animal studies, was conducted. The databases included Ovid MEDLINE and Epub ahead of print, in-process and other nonindexed citations, and daily; Ovid Embase; Ovid Cochrane Central Register of Controlled Trials; Ovid Cochrane Database of Systematic Reviews; Web of Science; and Scopus.ResultsThe initial search yielded 344 articles, from which 19 (5.5%) articles were left on the final source list for this scoping review. Articles were divided into three main groups as follows: studies with the main focus on autism spectrum disorder, attention-deficit/hyperactivity disorder, and internalizing disorders such as anxiety disorders. Most of the studies used either cardio-fitness chest straps with electrocardiogram sensors or wrist-worn biosensors, such as watches by Fitbit. Both allowed passive data collection of the physiological signals.ConclusionsOur scoping review found a large heterogeneity of methods and findings in artificial intelligence studies in child psychiatry. Overall, the largest gap identified in this scoping review is the lack of randomized controlled trials, as most studies available were pilot studies and feasibility trials.

Psychiatric Outcomes in ICU Patients With Family Visitation: A Population-Based Retrospective Cohort Study

Lack of family visitation in the ICU can have long-term consequences on patients in the ICU after discharge. The effect of family visitation on the incidence of patient psychiatric disorders is unknown. What is the association between family visitation in the ICU and incidence of psychiatric outcomes in patients in the ICU 1 year after hospital discharge? This study assessed a population-based retrospective cohort of adult patients admitted to the ICU from January 1, 2014, through May 30, 2017, surviving to hospital discharge with ICU length of stay of≥ 3days. To be eligible, patients needed to have minimum of 5 years of administrative data before ICU admission and a minimum of 1 year of follow-up data after hospital discharge. An internally validated algorithm that interpreted natural language in health records determined patients with or without in-person family (ie, relatives, friends) visitation during ICU stay. The primary outcome was risk of an incidence of psychiatric disorder (composite outcome), including anxiety, depressive, trauma- and stressor-related, psychotic, and substance use disorders, identified using coding algorithms for administrative databases. Propensity scores were used in inverse probability weighted logistic regression models, and average treatment effects were converted to risk ratios (RRs) with 95%CIs. Secondary outcomes were incidences of diagnoses by type of psychiatric disorder. We included 14,344 patients with (96%[n= 13,771]) and without (4.0%[n= 573]) in-person family visitation who survived hospital discharge. More than one-third of patients received a diagnosis of any psychiatric disorder within 1 year after discharge (34.9%; 95%CI, 34.1%-35.6%). Patients most often received diagnoses of anxiety disorders (17.5%; 95%CI, 16.9%-18.1%) and depressive disorders (17.2%; 95%CI, 16.6%-17.9%). After inverse probability weighting of 13,731 patients, in-person family visitation was associated with a lower risk of received a diagnosis of any incident psychiatric disorder within 1 year after discharge (RR, 0.79; 95%CI, 0.68-0.92). ICU family visitation is associated with a decreased risk of psychiatric disorders in critically ill patients up to 1 year after hospital discharge.