Comorbid personality disorders and their impact on severe dissociative experiences in Mexican patients with borderline personality disorder.

Objective: To identify personality disorders comorbid with borderline personality disorder (BPD) that may confer greater risk for the presence of severe dissociative experiences. Method: Three hundred and one outpatients with a primary diagnosis of BPD were evaluated using the Structured Clinical Interview for DSM-IV Axis II personality disorders, the Borderline Evaluation of Severity Over Time (BEST) and the Dissociative Experiences Scale (DES). Results: The most frequent personality disorders comorbid to BPD were paranoid (83.2%, n = 263) and depressive (81.3%, n = 257). The mean BEST and DES total score were 43.3 (SD = 11.4, range 15-69) and 28.6 (SD = 19.8, range 0-98), respectively. We categorized the sample into patients with and without severe dissociative experiences (41% were positive). A logistic regression model revealed that Schizotypal, Obsessive-compulsive and Antisocial personality disorders conferred greater risk for the presence of severe dissociative experiences. Discussion: Our results suggest that a large proportion of patients with BPD present a high rate of severe dissociative experiences and that some clinical factors such as personality comorbidity confer greater risk for severe dissociation, which is related to greater dysfunction and suffering, as well as a worse progression of the BPD.

Automatic detection of facial expressions during the Cyberball paradigm in Borderline Personality Disorder: a pilot study.

Borderline Personality Disorder (BPD) symptoms include inappropriate control of anger and severe emotional dysregulation after rejection in daily life. Nevertheless, when using the Cyberball paradigm, a tossing game to simulate social exclusion, the seven basic emotions (happiness, sadness, anger, surprise, fear, disgust, and contempt) have not been exhaustively tracked out. It was hypothesized that these patients would show anger, contempt, and disgust during the condition of exclusion versus the condition of inclusion. When facial emotions are automatically detected by Artificial Intelligence, "blending", -or a mixture of at least two emotions- and "masking", -or showing happiness while expressing negative emotions- may be most easily traced expecting higher percentages during exclusion rather than inclusion. Therefore, face videos of fourteen patients diagnosed with BPD (26 ± 6 years old), recorded while playing the tossing game, were analyzed by the FaceReader software. The comparison of conditions highlighted an interaction for anger: it increased during inclusion and decreased during exclusion. During exclusion, the masking of surprise; i.e., displaying happiness while feeling surprised, was significantly more expressed. Furthermore, disgust and contempt were inversely correlated with greater difficulties in emotion regulation and symptomatology, respectively. Therefore, the automatic detection of emotional expressions during both conditions could be useful in rendering diagnostic guidelines in clinical scenarios.

Remote crisis intervention and suicide risk management in COVID-19 frontline healthcare workers.

Introduction: Despite the propagation of virtual mental health services for vulnerable groups during COVID-19 pandemic, the implementation and evaluation of remote evidence-based practices (EBP) to manage them in low- and middle-income countries remains scarce. In the current study, we describe and evaluate the implementation process and clinical impact of brief, remote, manualized EBP for crisis intervention and suicide risk management among healthcare workers attending patients with COVID-19 (COVID-19-HCWs) in Mexico. Methods: The implementation process comprised community engagement of volunteer mental health specialists, creation of new clinical teams with different disciplines and skills, intervention systematization through manuals and education through 4-h remote training as main strategies. Mexican COVID-19-HCWs who had used a free 24-h helpline rated their pre- and post-intervention emotional distress. Therapists recorded patients' pre-intervention diagnosis, severity, and suicide risk, the techniques used in each case, and their post-treatment perception of COVID-19-HCWs' improvement at the end of the intervention. Results: All techniques included in the intervention manual were employed at least in one case (n = 51). At the beginning of the intervention, 65.9% of the COVID-19-HCWs were considered moderately ill or worse according to Clinical Global Impression-Severity (CGI-S) scores, whereas at the end, 79.4% of them were perceived as much or very much improved according to CGI-Improvement scores (CGI-I), and their emotional distress had been significantly reduced (p < 0.001). Discussion: This prospective study provides evidence that implementation of remote EBP is feasible and useful to reduce emotional distress and suicide risk among COVID-19-HCWs from a middle-income country. However, this study was limited by lack of a control group, improvement ratings provided by therapists and non-anonymous satisfaction ratings.

Machine learning detects predictors of symptom severity and impulsivity after dialectical behavior therapy skills training group in borderline personality disorder.

Only 50% of the patients with Borderline Personality Disorder (BPD) respond to psychotherapies, such as Dialectical Behavioral Therapy (DBT), this might be increased by identifying baseline predictors of clinical change. We use machine learning to detect clinical features that could predict improvement/worsening for severity and impulsivity of BPD after DBT skills training group. To predict illness severity, we analyzed data from 125 patients with BPD divided into 17 DBT psychotherapy groups, and for impulsiveness we analyzed 89 patients distributed into 12 DBT groups. All patients were evaluated at baseline using widely self-report tests; ∼70% of the sample were randomly selected and two machine learning models (lasso and Random forest [Rf]) were trained using 10-fold cross-validation and compared to predict the post-treatment response. Models' generalization was assessed in ∼30% of the remaining sample. Relevant variables for DBT (i.e. the mindfulness ability "non-judging", or "non-planning" impulsiveness) measured at baseline, were robust predictors of clinical change after six months of weekly DBT sessions. Using 10-fold cross-validation, the Rf model had significantly lower prediction error than lasso for the BPD severity variable, Mean Absolute Error (MAE) lasso - Rf = 1.55 (95% CI, 0.63-2.48) as well as for impulsivity, MAE lasso - Rf = 1.97 (95% CI, 0.57-3.35). According to Rf and the permutations method, 34/613 significant predictors for severity and 17/613 for impulsivity were identified. Using machine learning to identify the most important variables before starting DBT could be fundamental for personalized treatment and disease prognosis.

Facial and Vocal Expressions During Clinical Interviews Suggest an Emotional Modulation Paradox in Borderline Personality Disorder: An Explorative Study.

Videotape recordings obtained during an initial and conventional psychiatric interview were used to assess possible emotional differences in facial expressions and acoustic parameters of the voice between Borderline Personality Disorder (BPD) female patients and matched controls. The incidence of seven basic emotion expressions, emotional valence, heart rate, and vocal frequency (f0), and intensity (dB) of the discourse adjectives and interjections were determined through the application of computational software to the visual (FaceReader) and sound (PRAAT) tracks of the videotape recordings. The extensive data obtained were analyzed by three statistical strategies: linear multilevel modeling, correlation matrices, and exploratory network analysis. In comparison with healthy controls, BPD patients express a third less sadness and show a higher number of positive correlations (14 vs. 8) and a cluster of related nodes among the prosodic parameters and the facial expressions of anger, disgust, and contempt. In contrast, control subjects showed negative or null correlations between such facial expressions and prosodic parameters. It seems feasible that BPD patients restrain the facial expression of specific emotions in an attempt to achieve social acceptance. Moreover, the confluence of prosodic and facial expressions of negative emotions reflects a sympathetic activation which is opposed to the social engagement system. Such BPD imbalance reflects an emotional alteration and a dysfunctional behavioral strategy that may constitute a useful biobehavioral indicator of the severity and clinical course of the disorder. This face/voice/heart rate emotional expression assessment (EMEX) may be used in the search for reliable biobehavioral correlates of other psychopathological conditions.

Construct validity and parent-child agreement of the six new or modified disorders included in the Spanish version of the Kiddie Schedule for Affective Disorders and Schizophrenia present and Lifetime Version DSM-5 (K-SADS-PL-5).

Changes to the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5) incorporate the inclusion or modification of six disorders: Autism Spectrum Disorder, Social Anxiety Disorder, Intermittent Explosive Disorder, Disruptive Mood Dysregulation Disorder, Avoidant/Restrictive Food Intake Disorder and Binge Eating Disorder. The objectives of this study were to assess the construct validity and parent-child agreement of these six disorders in the Spanish language Schedule for Affective Disorders and Schizophrenia for School Age Children Present and Lifetime Version (K-SADS-PL-5) in a clinical population of children and adolescents from Latin America. The Spanish version of the K-SADS-PL was modified to integrate changes made to the DSM-5. Clinicians received training in the K-SADS-PL-5 and 90% agreement between raters was obtained. A total of 80 patients were recruited in four different countries in Latin America. All items from each of the six disorders were included in a factor analysis. Parent-child agreement was calculated for every item of the six disorders, including the effect of sex and age. The factor analysis revealed 6 factors separately grouping the items defining each of the new or modified disorders, with Eigenvalues greater than 2. Very good parent-child agreements (r>0.8) were found for the large majority of the items (93%), even when considering the sex or age of the patient. This independent grouping of disorders suggests that the manner in which the disorders were included into the K-SADS-PL-5 reflects robustly the DSM-5 constructs and displayed a significant inter-informant reliability. These findings support the use of K-SADS-PL-5 as a clinical and research tool to evaluate these new or modified diagnoses.