Sleep-wake variations of electrodermal activity in bipolar disorder.

BACKGROUND: Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities. METHODS: We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates. RESULTS: Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not. CONCLUSION: EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.

A Cross-Sectional, Retrospective, and Comparative Study between Delirium and Non-Delirium Psychiatric Disorders in a Psychogeriatric Inpatient Population Referred to Consultation-Liaison Psychiatry Unit.

Background and objectives: Delirium is the most prevalent psychiatric disorder in inpatient older people. Its presence is associated with higher rates of institutionalization, functional disability and mortality. This study aims to evaluate delirium in a hospitalized psychogeriatric population, focusing on which factors predict the appearance of delirium, the impact it generates and the diagnostic concordance between non-psychiatric physicians and psychiatrists. Material and methods: This is an observational, cross-sectional, retrospective, and comparative study. We obtained data from a sample of 1017 patients (≥65 years) admitted to general hospital and referred from different services to the consultation-liaison psychiatry (CLP) unit. Logistic regression was performed using delirium as the dependent variable. To estimate the concordance of the diagnoses, the Kappa coefficient was used. To assess the impact of delirium, an ordinal regression, Wilcoxon median test and Fisher's test were performed. Results: Delirium is associated with a higher number of visits, OR 3.04 (95% CI 2.38-3.88), longer length of stay and mortality, OR 2.07 (95% CI, 1.05 to 4.10). The model to predict delirium shows that being >75 years old has an OR of 2.1 (95% CI, 1.59-2.79), physical disability has an OR of 1.66 (95% CI, 1.25-2.20), history of delirium has an OR of 10.56 (95% CI, 5.26-21.18) and no use of benzodiazepines has an OR of 4.24 (95% CI, 2.92-6.14). The concordance between the referring physician's psychiatric diagnosis and the psychiatrist CLP unit showed a kappa of 0.30. When analysing depression and delirium, the concordance showed Kappa = 0.46. Conclusions: Delirium is a highly prevalent psychiatric disorder, but it is still underdiagnosed, with low diagnostic concordance between non-psychiatric doctors and psychiatrists from CLP units. There are multiple risk factors associated with the appearance of delirium, which must be managed to reduce its appearance.

Clinical Profile and Length of Hospital Stay in a Sample of Psychogeriatric Patients Referred to Consultation Liaison Psychiatric Unit.

Background and objectives: There has been a recent increase in older patients admitted to general hospitals. A significant percentage of hospitalized older patients are ≥75 years old, which differ from the patients aged 65 to 74 years old in terms of functional status at patient discharge. This study aims to compare sociodemographic, clinical features, and factors associated with length of hospital stay in youngest-old and oldest-old populations of inpatients referred to the consultation liaison psychiatry unit. Material and methods: This is an observational, cross-sectional, retrospective, and comparative study. We obtained data from a sample of 1017 patients (≥65 years) admitted to a general hospital and referred from different services (medicine, surgery, etc.) to the consultation liaison psychiatry unit. The sample was divided into two groups of patients: youngest-old (65-74 years) and oldest-old (≥75 years). Psychiatric evaluations were performed while the patients were on wards at the hospital. Psychopharmacs were started as needed. A comparative analysis was carried out and predictive factors related to length of hospital stay were calculated. Results: The reference rate to consultation liaison psychiatry unit was 1.45% of the total older patients hospitalized. Our study demonstrates differences between the groups of older people: the oldest-old group were mainly female (p < 0.001), had more previous psychiatric diagnoses (p < 0.001), physical disabilities (p = 0.02), and neurocognitive disorders (p < 0.001), they used more antipsychotics (p < 0.001), and more frequently had a discharge disposition to a nursing home (p = 0.036). The presence of physical disability (beta = 0.07, p < 0.001) and logtime to referral to consultation liaison psychiatry unit (beta = 0.58, p < 0.001) were associated with increased length of hospital stay. Conclusions: Youngest-old and oldest-old people should be considered as two different types of patients when we consider clinical features. The time to referral to consultation liaison psychiatry unit seems to be a relevant factor associated with length of hospital stay.

Identifying digital biomarkers of illness activity and treatment response in bipolar disorder with a novel wearable device (TIMEBASE): protocol for a pragmatic observational clinical study.

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.

Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study.

BACKGROUND: Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity, alongside physiological alterations wearables can capture. OBJECTIVE: Firstly, 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. METHODS: We 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. RESULTS: Overall, 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). CONCLUSIONS: Physiological 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.

Assessment of psychosocial factors and predictors of psychopathology in a sample of heart transplantation recipients: a prospective 12-month follow-up.

BACKGROUND AND OBJECTIVES: In the last decades, researchers of heart transplantation (HT) programs have attempted to identify the existence of psychosocial factors that might influence the clinical outcome before and after the transplantation. The first objective of this study is the prospective description of changes in psychiatric and psychosocial factors in a sample of HT recipients through a 12-month follow-up. The second goal is to identify predictors of psychopathology 1 year after HT. METHODS: Pretransplant baseline assessment consisted of clinical form; Hospital Anxiety and Depression Scale (HADS); Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Structured Clinical Interview; Coping questionnaire (COPE); Five Factors Inventory Revised; Apgar-Family questionnaire and Multidimensional Health Locus of Control (MHLC). The assessment 1 year after HT consisted of HADS, COPE, Apgar-Family and MHLC. RESULTS: The sample included 78 recipients. During the waiting list period, 32.1% of them had a psychiatric disorder; personality factors profile was similar to the general population, and they showed adaptive coping strategies. Some changes in psychosocial factors were observed at 12 months after the surgery: lower scores of anxiety and depression, less necessity of publicly venting of feelings and a trend to an internal locus of control. Neuroticism and Disengagement pre-HT were predictors of psychopathology in the follow-up assessment. CONCLUSIONS: Pretransplant psychosocial screening is important and enables to find out markers of emotional distress like Neuroticism or Disengagement coping styles to identify patients who might benefit from psychiatric and psychological interventions. Successful HT involved some positive changes in psychosocial factors 12 months after the surgery beyond physical recovery.

Cross-sectional psychosocial evaluation of heart transplantation candidates.

INTRODUCTION AND OBJECTIVES: Heart transplantation (HT) is a potentially life-saving procedure for people with terminal cardiac disease. In the last decades researchers of HT programs have attempted to identify the existence of psychosocial factors that might influence the clinical outcome before and after the transplantation. The main objective of this study was to describe epidemiological, psychiatric and psychological features of a large sample of HT candidates. METHODS: Cross-sectional, observational and descriptive study. A psychiatric and psychological assessment of 125 adult patients was performed at the moment of being included in the HT waiting list, between 2006 and 2012. The assessment consisted in: Clinical, epidemiological and psychosocial form; Spanish version of Hospital Anxiety and Depression Scale; Structured Clinical Interview for DSM-IV axis I disorders; Coping questionnaire (COPE); Five Factors Inventory Revised (NEO-FFI-R); Apgar-Family questionnaire and the Multidimensional Health Locus of Control scale. RESULTS: Axis I diagnoses were present in a 30.4% of patients. COPE showed that this group of patients used most frequently engagement strategies. Personality factors profile of NEO-FFI-R were similar to general population and locus of control scale also presented similar scores compared with other chronic diagnostic groups. Statistically significant associations were found between personality factors and COPE scales/dimensions and psychopathology, mainly neuroticism and disengagement. CONCLUSIONS: This is the first study to assess systematically psychosocial factors in a large sample of HT candidates. We have found that around one third of these patients have a psychiatric disorder. Neuroticism and disengagement coping styles can serve as markers of emotional distress.