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Abstract Objective This study aims to investigate the interrelationship of risk factors for suicidal behaviour and their influence on attempt severity in a sample of adolescents who have recently attempted suicide. For it a network analyse was performed. Method Data from a sample of adolescents from 12 to 17 years of age with documented suicide attempts were collected and analysed in the context of a larger study conducted in Spain called SURVIVE. Several factors were examined including age, sex, depression, trauma, impulsivity, and substance abuse. Graph analysis was performed to identify relationships and centrality measures among these factors. Results A total of 267 participants were enrolled: 233 females and 34 males with a mean age of 15.00 years (SD = 1.52). The results indicate that age and sex do not have a notable relationship with attempt severity in adolescents. Emotional and behavioural difficulties, measured by the Strengths and Difficulties Questionnaire (SDQ), have the greatest influence on other variables. Depression and childhood trauma show varying degrees of association with suicidal behaviour, as does motor impulsivity. Substance use does not appear to be strongly related to suicide attempt severity. The number of suicide attempts is strongly correlated with emotional and behavioural difficulties, depression, and childhood trauma. Lethality of suicide attempts and intensity of suicidal ideation do not show significant associations with the other variables. Conclusion This study identifies significant risk factors for adolescent suicide. Emotional and behavioural symptoms, depression, and childhood trauma are strongly linked to suicidal behaviour. However, age, sex, and substance abuse show minimal correlation. Assessing emotional difficulties and depressive symptoms using specific questionnaires could be crucial in evaluating suicidal behaviour in adolescents.

Resumen: Introducción: La comorbilidad médica en pacientes con trastornos del estado de ánimo tiende a convertirse en un problema de salud pública clínica y global cada vez más importante. Varias patologías médicas específicas están asociadas con un mayor riesgo de padecer trastornos del estado de ánimo y, por otra parte, los trastornos del estado de ánimo están asociados con un aumento de la morbilidad y mortalidad debidas a condiciones médicas comórbidas. En este artículo se revisan las comorbilidades médicas que más comúnmente se asocian a los trastornos afectivos (enfermedades cardiovasculares, obesidad y síndrome metabólico) examinando sus posibles implicaciones bidireccionales. Métodos: Se ha realizado una revisión no sistemática y búsqueda de la literatura científica sobre la asociación entre las tres enfermedades médicas más frecuentes en trastorno depresivo mayor y trastorno bipolar (enfermedades cardiovasculares, obesidad, síndrome metabólico) entre enero de 1995 y noviembre de 2019. Resultados: La evidencia sugiere que la comorbilidad entre estas tres enfermedades médicas y los trastornos del estado de ánimo es muy frecuente; la presencia de las primeras empeora significativamente el pronóstico y el manejo terapéutico de las segundas y viceversa, comparten mecanismos fisiopatológicos e implican una etiología aparentemente bidireccional. Conclusiones: La presencia de estas enfermedades médicas concurrentes en un individuo con un trastorno del estado de ánimo se asocia con una presentación de enfermedad más compleja. En muchos casos, estas comorbilidades pueden preceder a la aparición de los trastornos del estado de ánimo, aunque en la mayoría de los casos parecen seguir a la aparición de los trastornos del estado de ánimo. Para los profesionales, la evidencia apoya inequívocamente las recomendaciones para la vigilancia rutinaria de las comorbilidades según un enfoque multidisciplinar. Summary: Introduction: Medical comorbidity in patients with mood disorders tends to become an increasingly important clinical and global public health problem. On one hand, several specific medical pathologies are associated with an increased risk of mood disorders and on the other hand, mood disorders are associated with increased morbidity and mortality due to comorbid medical conditions. This article reviews the medical comorbidities that are most commonly associated with affective disorders (cardiovascular diseases, obesity and metabolic syndrome) examining their possible bidirectional implications. Methods: A non-systematic review about the association between the three most common medical diseases in major depressive disorder and bipolar affective disorder (cardiovascular diseases, obesity, metabolic syndrome) has been carried out from January 1995 to November 2019. Results: The evidence suggests that comorbidity between these three medical diseases and mood disorders is very prevalent. The presence of medical disease significantly worsens the prognosis and therapeutic management of the mood disorders and vice versa. In many cases, these comorbidities may precede the onset of mood disorders, although in most cases they appear to follow the onset of mood disorders. Conclusions: The presence of these concurrent medical diseases in an individual with a mood disorder is associated with a more complex disease presentation. For professionals, the evidence unequivocally supports recommendations for routine surveillance of comorbidities according to a multidisciplinary approach. Palabras clave: Trastorno bipolar, Trastorno depresivo mayor, Comorbilidad médica, Enfermedad cardiovascular, Obesidad, Síndrome metabólico, Keywords: Bipolar disorder, Major depressive disorder, Comorbidities, cardiovascular disease, Obesity, Metabolic syndrome

Abstract Bipolar disorder (BD) involves autonomic nervous system dysfunction, detectable through heart rate variability (HRV). HRV is a promising biomarker, but its dynamics during acute mania or depression episodes are poorly understood. Using a Bayesian approach, we developed a probabilistic model of HRV changes in BD, measured by the natural logarithm of the Root Mean Square of Successive RR interval Differences (lnRMSSD). Patients were assessed three to four times from episode onset to euthymia. Unlike previous studies, which used only two assessments, our model allowed for more accurate tracking of changes. Results showed strong evidence for a positive lnRMSSD change during symptom resolution (95.175% probability of positive direction), though the sample size limited the precision of this effect (95% Highest Density Interval [−0.0366, 0.4706], with a Region of Practical Equivalence: [-0.05; 0.05]). Episode polarity did not significantly influence lnRMSSD changes.

Abstract Background One of the most relevant risk factors for suicide is the presence of previous attempts. The symptomatic profile of people who reattempt suicide deserves attention. Network analysis is a promising tool to study this field. Objective To analyze the symptomatic network of patients who have attempted suicide recently and compare networks of people with several attempts and people with just one at baseline. Methods 1043 adult participants from the Spanish cohort “SURVIVE” were part of this study. Participants were classified into two groups: single attempt group (n = 390) and reattempt group (n = 653). Different network analyses were carried out to study the relationships between suicidal ideation, behavior, psychiatric symptoms, diagnoses, childhood trauma, and impulsivity. A general network and one for each subgroup were estimated. Results People with several suicide attempts at baseline scored significantly higher across all clinical scales. The symptomatic networks were equivalent in both groups of patients (p > .05). Although there were no overall differences between the networks, some nodes were more relevant according to group belonging. Conclusions People with a history of previous attempts have greater psychiatric symptom severity but the relationships between risk factors show the same structure when compared with the single attempt group. All risk factors deserve attention regardless of the number of attempts, but assessments can be adjusted to better monitor the occurrence of reattempts.

Background Bipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes of mania and depression, which translate into altered mood, sleep and activity alongside their physiological expressions. Aims The IdenTifying dIgital bioMarkers of illnEss activity and treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers of illness activity and treatment response in bipolar disorder. Method We designed a longitudinal observational study including 84 individuals. Group A comprises people with acute episode of mania (n = 12), depression (n = 12 with bipolar disorder and n = 12 with major depressive disorder (MDD)) and bipolar disorder with mixed features (n = 12). Physiological data will be recorded during 48 h with a research-grade wearable (Empatica E4) across four consecutive time points (acute, response, remission and episode recovery). Group B comprises 12 people with euthymic bipolar disorder and 12 with MDD, and group C comprises 12 healthy controls who will be recorded cross-sectionally. Psychopathological symptoms, disease severity, functioning and physical activity will be assessed with standardised psychometric scales. Physiological data will include acceleration, temperature, blood volume pulse, heart rate and electrodermal activity. Machine learning models will be developed to link physiological data to illness activity and treatment response. Generalisation performance will be tested in data from unseen patients. Results Recruitment is ongoing. Conclusions This project should contribute to understanding the pathophysiology of affective disorders. The potential digital biomarkers of illness activity and treatment response in bipolar disorder could be implemented in a real-world clinical setting for clinical monitoring and identification of prodromal symptoms. This would allow early intervention and prevention of affective relapses, as well as personalisation of treatment.

Abstract Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician’s office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen’s κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

BackgroundChild maltreatment is associated with a higher probability of mental disorders and suicidal behavior in adolescence. Therefore, accurate psychometric instruments are essential to assess this.ObjectiveTo validate the Spanish version of the Childhood Trauma Questionnaire-Short Form (CTQ-SF) in adolescents with suicide attempts.MethodsMultisite cohort study of 208 adolescents with suicide attempts using data from the following scales: Mini International Neuropsychiatric Interview (MINI), Columbia Suicide Severity Rating Scale (C-SSRS), Patient Health Questionnaire (PHQ-9), and CTQ-SF. Statistical analysis: CTQ-SF scores analyzed by descriptive statistics. Internal consistency: McDonald’s omega and Cronbach’s alpha. Concurrent validity with PHQ-9 and C-SSRS scores: Spearman correlation coefficient. Structural validity: Confirmatory factor analysis.ResultsFloor and ceiling effects: Physical abuse and neglect as well as sexual abuse demonstrated high floor effects (50.0, 35.1, and 61.1% of adolescents, respectively). No ceiling effects were found. The CTQ-SF had excellent internal consistency (McDonald’s omega = 0.94), as did the majority of its subscales (Cronbach’s alpha 0.925–0.831) except for physical neglect (0.624). Its concurrent validity was modest, and the emotional neglect subscale had the lowest Spearman correlation coefficients (0.067–0.244). Confirmatory factor analysis: Compared with alternative factor structures, the original CTQ-SF model (correlated 5-factor) exhibited a better fit [S-B χ2 = 676.653, p

BackgroundPersonal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few dozen patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MD detection. ObjectiveIn this paper, we overcame this data bottleneck and advanced the detection of acute MD episodes from wearables’ data on the back of recent advances in self-supervised learning (SSL). This approach leverages unlabeled data to learn representations during pretraining, subsequently exploited for a supervised task. MethodsWe collected open access data sets recording with the Empatica E4 wristband spanning different, unrelated to MD monitoring, personal sensing tasks—from emotion recognition in Super Mario players to stress detection in undergraduates—and devised a preprocessing pipeline performing on-/off-body detection, sleep/wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduced E4SelfLearning, the largest-to-date open access collection, and its preprocessing pipeline. We developed a novel E4-tailored transformer (E4mer) architecture, serving as the blueprint for both SSL and fully supervised learning; we assessed whether and under which conditions self-supervised pretraining led to an improvement over fully supervised baselines (ie, the fully supervised E4mer and pre–deep learning algorithms) in detecting acute MD episodes from recording segments taken in 64 (n=32, 50%, acute, n=32, 50%, stable) patients. ResultsSSL significantly outperformed fully supervised pipelines using either our novel E4mer or extreme gradient boosting (XGBoost): n=3353 (81.23%) against n=3110 (75.35%; E4mer) and n=2973 (72.02%; XGBoost) correctly classified recording segments from a total of 4128 segments. SSL performance was strongly associated with the specific surrogate task used for pretraining, as well as with unlabeled data availability. ConclusionsWe showed that SSL, a paradigm where a model is pretrained on unlabeled data with no need for human annotations before deployment on the supervised target task of interest, helps overcome the annotation bottleneck; the choice of the pretraining surrogate task and the size of unlabeled data for pretraining are key determinants of SSL success. We introduced E4mer, which can be used for SSL, and shared the E4SelfLearning collection, along with its preprocessing pipeline, which can foster and expedite future research into SSL for personal sensing.

Abstract Background The prevalence of mental health disorders has significantly increased in recent years, posing substantial challenges to healthcare systems worldwide, particularly primary care (PC) settings. This study examines trends in mental health diagnoses in PC settings in Catalonia from 2010 to 2019 and identifies associated sociodemographic, clinical characteristics, psychopharmacological treatments, and resource utilization patterns. Methods Data from 947,698 individuals without prior severe mental illness, derived from the Data Analytics Program for Health Research and Innovation (PADRIS), were analyzed for this study. Sociodemographic data, diagnoses, and resource utilization were extracted from electronic health records. Descriptive statistics, chi-square tests, Mann–Whitney tests, and a multivariate binary logistic regression were employed to analyze the data. Results Over the study period, 172,112 individuals (18.2%) received at least one mental health diagnosis in PC, with unspecified anxiety disorder (40.5%), insomnia (15.7%) and unspecified depressive disorder (10.2%) being the most prevalent. The prevalence of these diagnoses increased steadily until 2015 and stabilized thereafter. Significant associations were found between mental health diagnoses, female sex, lower socioeconomic status, higher BMI, and smoking status in a multivariate binary logistic regression. Conclusions This study highlights a growing burden of stress-related mental health diagnoses in PC in Catalonia, driven by demographic and socioeconomic factors. These findings may be indicative of broader trends across Europe and globally. Addressing this rising prevalence requires innovative approaches and collaborative strategies that extend beyond traditional healthcare resources. Engaging stakeholders is essential for implementing effective, sustainable solutions that promote mental health in Catalonia and potentially inform similar initiatives worldwide.

Abstract Background Suicidal behavior constitutes a multi-cause phenomenon that may also be present in people without a mental disorder. This study aims to analyze suicidal behavior outcomes in a sample of attempters, from a symptom-based approach. Methods The sample comprised 673 patients (72% female; M = 40.9 years) who attended a hospital emergency department due to a suicide attempt. A wide range of clinical factors (e.g., psychopathology symptoms, psychiatric diagnoses, impulsivity, acquired capability), was administered within 15 days after the index attempt. Nine psychopathology domains were explored to identify the profile of symptoms, using latent profile analysis. The relationship between the profile membership and suicide outcome (i.e., intensity of suicidal ideation, number of suicide behaviors, and medical injury derived from index attempt) was also studied, using linear and logistic regression. Results Three psychopathology profiles were identified: high-symptom profile (45.02% of participants), moderate-symptom profile (42.50%), and low-symptom profile (12.48%). High-symptom profile members were more likely to show higher risk of non-suicidal self-injury, acquired capability for suicide, and more severe suicide behavior and ideation. On the other hand, a more severe physical injury was associated with low-symptom profile membership in comparison to membership from the other profiles (OR

Objectives: The kynurenine (KYN) pathway has been attracting attention as a relevant pathway in schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). We conducted a systematic review and meta-analysis of studies examining KYN pathway metabolites from cerebrospinal fluid (CSF) samples in SZ, BD, and MDD. Methods: The PubMed and Scopus databases were systematically searched to identify peer-reviewed case-control studies published until April 2022 that assessed KYN metabolites, namely, tryptophan (TRP), KYN, kynurenic acid (KA), quinolinic acid (QA), and 3-hydroxykynurenine (3-HK), in subjects with SZ, BD, or MDD compared with healthy controls (HC). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. The random effects model method was selected for comparison of standardized mean differences (SMD) between two groups. Results: Twenty-three articles met the inclusion criteria (k = 8, k = 8, k = 11, for SZ, BD, and MDD, respectively). In SZ, KA levels were increased (SMD = 2.64, confidence interval [CI] = 1.16 to 4.13, p = 0.0005, I2 = 96%, k = 6, n=384). TRP (k = 5) and KYN (k = 4) did not differ significantly. In BD, TRP levels (k = 7) did not differ significantly. The level of KA was increased in MDD (k = 2), but the small number of studies precluded evaluation of statistical significance. Finally, in MDD, although some studies tended to show an increased level of KYN in those with remission vs. decreased levels in those with current depression, no significant difference was found in any KYN metabolite levels. Similarly, an increased level of QA was found, but the number of studies (k = 2) was small. Conclusion: KA, which has possibly neuroprotective effects, is increased in SZ. QA, which has neurotoxic effects, may be increased in MDD. There were no alterations in BD. Alterations in the KYN pathway may occur based on population characteristics and mood states. Future studies should explore the utility of these metabolites as biomarkers.

BackgroundDepressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity, alongside physiological alterations wearables can capture. ObjectiveFirstly, we explored whether physiological wearable data could predict (aim 1) the severity of an acute affective episode at the intra-individual level and (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to prior predictions, generalization across patients, and associations between affective symptoms and physiological data. MethodsWe conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded using a research-grade wearable (Empatica E4) across 3 consecutive time points (acute, response, and remission of episode). Euthymic patients and healthy controls were recorded during a single session (approximately 48 h). Manic and depressive symptoms were assessed using standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), and electrodermal activity (EDA). Invalid physiological data were removed using a rule-based filter, and channels were time aligned at 1-second time units and segmented at window lengths of 32 seconds, as best-performing parameters. We developed deep learning predictive models, assessed the channels’ individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales’ items normalized mutual information (NMI). We present a novel, fully automated method for the preprocessing and analysis of physiological data from a research-grade wearable device, including a viable supervised learning pipeline for time-series analyses. ResultsOverall, 35 sessions (1512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 healthy controls (mean age 39.7, SD 12.6 years; 6/19, 32% female) were analyzed. The severity of mood episodes was predicted with moderate (62%-85%) accuracies (aim 1), and their polarity with moderate (70%) accuracy (aim 2). The most relevant features for the former tasks were ACC, EDA, and HR. There was a fair agreement in feature importance across classification tasks (Kendall W=0.383). Generalization of the former models on unseen patients was of overall low accuracy, except for the intra-individual models. ACC was associated with “increased motor activity” (NMI>0.55), “insomnia” (NMI=0.6), and “motor inhibition” (NMI=0.75). EDA was associated with “aggressive behavior” (NMI=1.0) and “psychic anxiety” (NMI=0.52). ConclusionsPhysiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression, respectively. These findings represent a promising pathway toward personalized psychiatry, in which physiological wearable data could allow the early identification and intervention of mood episodes.

BackgroundMany people attending primary care (PC) have anxiety-depressive symptoms and work-related burnout compounded by a lack of resources to meet their needs. The COVID-19 pandemic has exacerbated this problem, and digital tools have been proposed as a solution. ObjectiveWe aimed to present the development, feasibility, and potential effectiveness of Vickybot, a chatbot aimed at screening, monitoring, and reducing anxiety-depressive symptoms and work-related burnout, and detecting suicide risk in patients from PC and health care workers. MethodsHealthy controls (HCs) tested Vickybot for reliability. For the simulation study, HCs used Vickybot for 2 weeks to simulate different clinical situations. For feasibility and effectiveness study, people consulting PC or health care workers with mental health problems used Vickybot for 1 month. Self-assessments for anxiety (Generalized Anxiety Disorder 7-item) and depression (Patient Health Questionnaire-9) symptoms and work-related burnout (based on the Maslach Burnout Inventory) were administered at baseline and every 2 weeks. Feasibility was determined from both subjective and objective user-engagement indicators (UEIs). Potential effectiveness was measured using paired 2-tailed t tests or Wilcoxon signed-rank test for changes in self-assessment scores. ResultsOverall, 40 HCs tested Vickybot simultaneously, and the data were reliably transmitted and registered. For simulation, 17 HCs (n=13, 76% female; mean age 36.5, SD 9.7 years) received 98.8% of the expected modules. Suicidal alerts were received correctly. For the feasibility and potential effectiveness study, 34 patients (15 from PC and 19 health care workers; 76% [26/34] female; mean age 35.3, SD 10.1 years) completed the first self-assessments, with 100% (34/34) presenting anxiety symptoms, 94% (32/34) depressive symptoms, and 65% (22/34) work-related burnout. In addition, 27% (9/34) of patients completed the second self-assessment after 2 weeks of use. No significant differences were found between the first and second self-assessments for anxiety (t8=1.000; P=.34) or depressive (t8=0.40; P=.70) symptoms. However, work-related burnout scores were moderately reduced (z=−2.07, P=.04, r=0.32). There was a nonsignificant trend toward a greater reduction in anxiety-depressive symptoms and work-related burnout with greater use of the chatbot. Furthermore, 9% (3/34) of patients activated the suicide alert, and the research team promptly intervened with successful outcomes. Vickybot showed high subjective UEI (acceptability, usability, and satisfaction), but low objective UEI (completion, adherence, compliance, and engagement). Vickybot was moderately feasible. ConclusionsThe chatbot was useful in screening for the presence and severity of anxiety and depressive symptoms, and for detecting suicidal risk. Potential effectiveness was shown to reduce work-related burnout but not anxiety or depressive symptoms. Subjective perceptions of use contrasted with low objective-use metrics. Our results are promising but suggest the need to adapt and enhance the smartphone-based solution to improve engagement. A consensus on how to report UEIs and validate digital solutions, particularly for chatbots, is required.

Bipolar depression remains a clinical challenge with a quarter of patients failing to respond to initial conventional treatments. Although ketamine has been extensively studied in unipolar depression, its role in bipolar disorder remains inconclusive. The aim of our scoping review was to comprehensively synthesize the current clinical literature around ketamine use in bipolar depression. A total of 10 clinical studies (5 randomized controlled trials and 5 open label studies) were selected. The preliminary evidence, albeit weak, suggests that ketamine is a promising treatment and calls for further interest from the research community. Overall, ketamine treatment appeared to be tolerable with minimal risk for manic/hypomanic switching and showed some effectiveness across parameters of depression and suicidality. Moreover, ketamine is a potential treatment agent in patients with treatment-resistant bipolar depression with promising data extracted from extant controlled trials and real-world effectiveness studies. Future studies are needed to identify ketamine’s role in acute and maintenance treatment phases of bipolar depression. Moreover, future researchers should study the recurrence prevention and anti-suicidal effects of ketamine in the treatment of bipolar depression.

Background: The coronavirus disease 2019 (COVID-19) pandemic and lockdown might increase anxiety and depressive symptoms in most individuals. Health bodies recommend several coping behaviors to protect against such symptoms, but evidence on the relationship between these behaviors and symptoms mostly comes from cross-sectional studies in convenience samples. We will conduct a prospective longitudinal study of the associations between coping behaviors and subsequent anxiety and depressive symptoms during the COVID-19 pandemic in a representative sample of the Spanish general adult population.Methods: We will recruit 1,000 adult participants from all autonomous communities of Spain and with sex, age, and urbanicity distributions similar to those of their populations and assess anxiety and depressive symptoms and coping behaviors using fortnightly questionnaires and real-time methods (ecological momentary assessments) for 1 year. The fortnightly questionnaires will inquire about anxiety and depressive symptoms [General Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9)] and the frequency of 10 potential coping behaviors (e.g., follow a routine) during the past 2 weeks. In addition, we will collect several variables that could confound or moderate these associations. These will include subjective well-being [International Positive and Negative Affect Schedule Short Form (I-PANAS-SF) and Satisfaction with Life Scale (SWLS)], obsessive-compulsive symptoms [Obsessive Compulsive Inventory-Revised (OCI-R)], personality and emotional intelligence [International Personality Item Pool (IPIP) and Trait Emotional Intelligence Questionnaire Short Form (TEIQue-SF)], sociodemographic factors (e.g., work status, housing-built environment), and COVID-19 pandemic-related variables (e.g., hospitalizations or limitations in social gatherings). Finally, to analyze the primary relationship between coping behaviors and subsequent anxiety and depressive symptoms, we will use autoregressive moving average (ARMA) models.Discussion: Based on the study results, we will develop evidence-based, clear, and specific recommendations on coping behaviors during the COVID-19 pandemic and lockdown. Such suggestions might eventually help health bodies or individuals to manage current or future pandemics.

Introduction: Early recognition of bipolar disorder improves the prognosis and decreases the burden of the disease. However, there is a significant delay in diagnosis. Multiple risk factors for bipolar disorder have been identified and a population at high-risk for the disorder has been more precisely defined. These advances have allowed the development of risk calculators to predict individual risk of conversion to bipolar disorder. This review aims to identify the risk calculators for bipolar disorder and assess their clinical applicability. Methods: A systematic review of original studies on the development of risk calculators in bipolar disorder was performed. The studies’ quality was evaluated with the Newcastle-Ottawa Quality Assessment Form for Cohort Studies and according to recommendations of the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis Initiative. Results: Three studies met the inclusion criteria; one developed a risk calculator of conversion from major depressive episode to bipolar disorder; one of conversion to new-onset bipolar spectrum disorders in offspring of parents with bipolar disorder; and the last one of conversion in youths with bipolar disorder not-otherwise-specified. Conclusions: The calculators reviewed in this article present good discrimination power for bipolar disorder, although future replication and validation of the models is needed.

Personalized treatment is defined as choosing the “right treatment for the right person at the right time.” Although psychiatry has not yet reached this level of precision, we are on the way thanks to recent technological developments that may aid to detect plausible molecular and genetic markers. At the moment there are some models that are contributing to precision psychiatry through the concept of staging. While staging was initially presented as a way to categorize patients according to clinical presentation, course, and illness severity, current staging models integrate multiple levels of information that can help to define each patient's characteristics, severity, and prognosis in a more precise and individualized way. Moreover, staging might serve as the foundation to create a clinical decision-making algorithm on the basis of the patient's stage. In this review we will summarize the evolution of the bipolar disorder staging model in relation to the new discoveries on the neurobiology of bipolar disorder. Furthermore, we will discuss how the latest and future progress in psychiatry might transform current staging models into precision staging models.

Background: Patients with schizoaffective disorder (SAD) suffer from cognitive impairment, which negatively influences their functionality. Cognitive remediation (CR) interventions have been shown to be effective in patients with schizophrenia (SZ) and bipolar disorder (BD), but evidence in SAD is limited so far. The aim of this study is to systematically review the published data on CR interventions, either in neurocognition or social cognition, in patients with SAD.Methods: We conducted a comprehensive, computerized literature search using terms related to CR interventions in psychotic and affective disorders, and particularly in SAD. Pubmed, Embase, and Web of Knowledge databases were used up to February 28th, 2018 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The search returned 2672 articles of which four were finally selected meeting the inclusion criteria.Results: Cognitive Enhancement Therapy, computerized Cognitive Remediation Therapy and Cognitive Training showed positive results in subsamples of patients with SAD regarding neurocognition and functioning in comparable terms to patients with schizophrenia as well as in a greater extent in quality of life. Benefits in social cognition were also described when Social Cognition Interaction Training was considered in patients with SAD.Conclusions: CR interventions seem to improve neurocognition and social cognition in patients with SAD as well as functioning and quality of life. However, further randomized controlled trials on CR interventions with an optimized design focusing on selected sample of patients with SAD are imperative.

Objectives:Staging models for medical diseases are widely used to guide treatment and prognosis. Bipolar disorder (BD) is a chronic condition and it is among the most disabling disorders in medicine. The staging model proposed by Kapczinski in 2009 presents four progressive clinical stages of BD. Our aim was to evaluate pharmacological maintenance treatment across these stages in patients with BD.Methods:One hundred and twenty-nine subjects who met DSM-IV criteria for BD were recruited from the Bipolar Disorders Program at Hospital de Clínicas de Porto Alegre, Brazil. All patients were in remission. The subjects were classified according to the staging model: 31 subjects were classified as stage I, 44 as stage II, 31 as stage III, and 23 as stage IV.Results:Patterns of pharmacological treatment differed among the four stages (p = 0.001). Monotherapy was more frequent in stage I, and two-drug combinations in stage II. Patients at stages III and IV needed three or more medications or clozapine. Impairment in functional status (Functioning Assessment Short Test [FAST] scale scores) correlated positively with the number of medications prescribed.Conclusions:This study demonstrated differences in pharmacological treatment in patients with stable BD depending on disease stage. Treatment response can change with progression of BD. Clinical guidelines could consider the staging model to guide treatment effectiveness.

We describe a case report of an 80-year-old woman who presented with symptomatology compatible with an episode of major depression with catatonia. After psychiatric admission, electroconvulsive therapy (ECT) was applied, but symptoms progressed with cognitive impairment, bradykinesia, widespread stiffness, postural tremor, and gait disturbance. After compatible magnetic resonance imaging (MRI), diffusion changes, and electroencephalogram (EEG) findings the case was reoriented to Creutzfeldt-Jakob disease (CJD). The genetic study found a methionine/valine heterozygosity at codon 129 of the prion protein gene PrPSc. On followup, a significant clinical recovery turned out. For this reason, EEG and MRI were repeated and confirmed the findings. The patient subsequently demonstrated progressive clinical deterioration and died 21 months later. The diagnosis was verified postmortem by neuropathology. The vCJD subtype MV2 is indeed characterized by early and prominent psychiatric symptoms and a prolonged disease duration however no frank clinical recovery has before been reported.

This article reviews the most common non-psychiatric comorbidities associated with affective disorders, examining the implications of their possible bidirectional link. A narrative review was conducted on the association among the three most common non-psychiatric diseases in major depressive disorder and bipolar disorder (obesity, metabolic syndrome, and cardiovascular diseases) in articles published from January 1994 to April 2020. The evidence suggests that obesity, metabolic syndrome, and cardiovascular diseases are highly prevalent in patients diagnosed with affective disorders. The presence of non-psychiatric comorbidities significantly worsens the therapeutic management and prognosis of affective disorders and vice versa. In many cases, these comorbidities may precede the onset of affective disorders, although in most cases they appear after it. The presence of these concurrent non-psychiatric diseases in an individual diagnosed with an affective disorder is associated with a more complex disease presentation and management. For professionals, the evidence unequivocally supports routine surveillance of comorbidities from a multidisciplinary approach.