Investigating predictive factors of dialectical behavior therapy skills training efficacy for alcohol and concurrent substance use disorders: A machine learning study.

BACKGROUND: Dialectical Behavior Therapy Skills Training (DBT-ST) as stand-alone treatment has demonstrated promising outcomes for the treatment of alcohol use disorder (AUD) and concurrent substance use disorders (SUDs). However, no studies have so far empirically investigated factors that might predict efficacy of this therapeutic model. METHODS: 275 treatment-seeking individuals with AUD and other SUDs were consecutively admitted to a 3-month DBT-ST program (in- + outpatient; outpatient settings). The machine learning routine applied (i.e. penalized regression combined with a nested cross-validation procedure) was conducted in order to estimate predictive values of a wide panel of clinical variables in a single statistical framework on drop-out and substance-use behaviors, dealing with related multicollinearity, and eliminating redundant variables. RESULTS: The cross-validated elastic net model significantly predicted the drop-out. The bootstrap analysis revealed that subjects who showed substance-use behaviors during the intervention and who were treated with the mixed setting (i.e., in- and outpatient) program, together with higher ASI alcohol scores were associated with an higher probability of drop-out. On the contrary, older subjects, higher levels of education, together with higher scores of DERS awareness subscale were negatively associated to drop-out. Similarly, lifetime co-diagnoses of anxiety, bipolar, and gambling disorders, together with bulimia nervosa negatively predicted the drop-out. The machine learning model did not identify predictive variables of substance-use behaviors during the treatment. CONCLUSIONS: The DBT-ST program could be considered a valid therapeutic approach especially when AUD and other SUDs co-occur with other psychiatric conditions and, it is carried out as a full outpatient intervention.

Neuropsychological deficits correlate with symptoms severity and cortical thickness in Borderline Personality Disorder.

BACKGROUND: Neuropsychological abnormalities have been proposed to contribute to the development and maintenance of Borderline Personality Disorder (BPD). Previous meta-analyses and reviews confirmed deficits in a broad range of cognitive domains, including attention, cognitive flexibility, memory, executive functions, planning, information processing, and visuospatial abilities, often suggested to underlie brain abnormalities. However, no study directly explored the structural neural correlates of these deficits in BPD, also accounting for the possible confounding effect of pharmacological treatments, often used as adjunctive symptom-targeted therapy in clinical setting. METHODS: In this study we compared the performance of 24 BPD patients to 24 healthy controls obtained at the neuropsychological battery "Brief Assessment and Cognition in Schizophrenia", exploring the relationship between the cognitive impairments and current symptomatology, brain grey matter volumes and cortical thickness, controlling for medications load. RESULTS: Data revealed deficits in verbal memory and fluency, working memory, attention and speed of information processing and psychomotor speed and coordination when medication load was not in the model. Correcting for this variable, only the impairment in psychomotor abilities remained significant. A multiple regression confirmed the effect of this neuropsychological domain on the severity of BPD symptomatology (Borderline Evaluation of Severity Over Time). In BPD, the performance at psychomotor speed and coordination was also directly associated to cortical thickness in postcentral gyrus. LIMITATIONS: Relatively small sample size, especially for neuroimaging. CONCLUSIONS: Our study highlighted an influence of BPD neuropsychological impairments on symptomatology, and cortical thickness, prompting the potential clinical utility of a cognitive remediation program in BPD.

A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach.

BACKGROUND: Mood disorders (major depressive disorder, MDD, and bipolar disorder, BD) are considered leading causes of life-long disability worldwide, where high rates of no response to treatment or relapse and delays in receiving a proper diagnosis (~60% of depressed BD patients are initially misdiagnosed as MDD) contribute to a growing personal and socio-economic burden. The immune system may represent a new target to develop novel diagnostic and therapeutic procedures but reliable biomarkers still need to be found. METHODS: In our study we predicted the differential diagnosis of mood disorders by considering the plasma levels of 54 cytokines, chemokines and growth factors of 81 BD and 127 MDD depressed patients. Clinical diagnoses were predicted also against 32 healthy controls. Elastic net models, including 5000 non-parametric bootstrapping procedure and inner and outer 10-fold nested cross-validation were performed in order to identify the signatures for the disorders. RESULTS: Results showed that the immune-inflammatory signature classifies the two disorders with a high accuracy (AUC = 97%), specifically 92% and 86% respectively for MDD and BD. MDD diagnosis was predicted by high levels of markers related to both pro-inflammatory (i.e. IL-1ß, IL-6, IL-7, IL-16) and regulatory responses (IL-2, IL-4, and IL-10), whereas BD by high levels of inflammatory markers (CCL3, CCL4, CCL5, CCL11, CCL25, CCL27, CXCL11, IL-9 and TNF-α). CONCLUSIONS: Our findings provide novel tools for early diagnosis of BD, strengthening the impact of biomarkers research into clinical practice, and new insights for the development of innovative therapeutic strategies for depressive disorders.

Risk Perception and Media in Shaping Protective Behaviors: Insights From the Early Phase of COVID-19 Italian Outbreak.

In the absence of target treatments or vaccination, the SARS-CoV-2 pandemic can be impeded by effectively implementing containment measures and behaviors. This relies on individuals' adoption of protective behaviors, their perceived risk, and the use and trust of information sources. During a health emergency, receiving timely and accurate information enables individuals to take appropriate actions to protect themselves, shaping their risk perception. Italy was the first western country plagued by COVID-19 and one of the most affected in the early phase. During this period, we surveyed 2,223 Italians before the national lockdown. A quarter of the sample perceived COVID-19 less threatening than flu and would not vaccinate, if a vaccine was available. Besides, most people perceived containment measures, based on social distancing or wearing masks, not useful. This perceived utility was related to COVID-19 threat perception and efficacy beliefs. All these measures were associated with the use of media and their truthfulness: participants declared to mainly use the Internet, while health organizations' websites were the most trusted. Although social networks were frequently used, they were rated lower for trustfulness. Our data differ from those obtained in other community samples, suggesting the relevance to explore changes across different countries and during the different phases of the pandemic. Understanding these phenomena, and how people access the media, may contribute to improve the efficacy of containment measures, tailoring specific policies and health communications.