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Deficits in social cognition (SC) are significantly related to community functioning in schizophrenia (SZ). Few studies investigated longitudinal changes in SC and its impact on recovery. In the present study, we aimed: (a) to estimate the magnitude and clinical significance of SC change in outpatients with stable SZ who were assessed at baseline and after 4 years, (b) to identify predictors of reliable and clinically significant change (RCSC), and (c) to determine whether changes in SC over 4 years predicted patient recovery at follow-up.
The reliable change index was used to estimate the proportion of true change in SC, not attributable to measurement error. Stepwise multiple logistic regression models were used to identify the predictors of RCSC in a SC domain (The Awareness of Social Inference Test [TASIT]) and the effect of change in TASIT on recovery at follow-up.
In 548 participants, statistically significant improvements were found for the simple and paradoxical sarcasm of TASIT scale, and for the total score of section 2. The reliable change index was 9.8. A cut-off of 45 identified patients showing clinically significant change. Reliable change was achieved by 12.6% and RCSC by 8% of participants. Lower baseline TASIT sect. 2 score predicted reliable improvement on TASIT sect. 2. Improvement in TASIT sect. 2 scores predicted functional recovery, with a 10-point change predicting 40% increase in the probability of recovery.
The RCSC index provides a conservative way to assess the improvement in the ability to grasp sarcasm in SZ, and is associated with recovery.
Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR).
SCZ from the Italian Network for Research on Psychoses (N = 598 in the Discovery sample, N = 298 in the Validation sample) underwent historical, clinical, psychological, environmental and resilience assessments. A Support Vector Machine algorithm (based on 85 variables extracted from the above-mentioned assessments) was built in the Discovery sample, and replicated in the Validation sample, to classify between HR and LR patients, within a nested, Leave-Site-Out Cross-Validation framework. We then investigated whether algorithm decision scores were associated with the cognitive and clinical characteristics of patients.
The algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% (p < 0.0001) in the Discovery sample, and 80.2% in the Validation sample. Higher self-esteem, larger social network and use of adaptive coping strategies were the variables most frequently chosen by the algorithm to generate decisions. Correlations between algorithm decision scores, socio-cognitive abilities, and symptom severity were significant (pFDR < 0.05).
We identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia.
Subthreshold hypomania during a major depressive episode challenges the bipolar-unipolar dichotomy. In our study we employed a cross-diagnostic cluster analysis - to identify distinct subgroups within a cohort of depressed patients.
A k-means cluster analysis— based on the domain scores of the Mood Spectrum Self-Report (MOODS-SR) questionnaire—was performed on a data set of 300 adults with either bipolar or unipolar depression. After identifying groups, between-clusters comparisons were conducted on MOODS-SR domains and factors and on a set of sociodemographic, clinical and psychometric variables.
Three clusters were identified: one with intermediate depressive and poor manic symptomatology (Mild), one with severe depressive and poor manic symptomatology (Moderate), and a third one with severe depressive and intermediate manic symptomatology (Mixed). Across the clusters, bipolar patients were significantly less represented in the Mild one, while the DSM-5 “Mixed features” specifier did not differentiate the groups. When compared to the other patients, those of Mixed cluster exhibited a stronger association with most of the illness-severity, quality of life, and outcomes measures considered. After performing pairwise comparisons significant differences between “Mixed” and “Moderate” clusters were restricted to: current and disease-onset age, psychotic ideation, suicidal attempts, hospitalization numbers, impulsivity levels and comorbidity for Cluster B personality disorder.
In the present study, a clustering approach based on a spectrum exploration of mood symptomatology led to the identification of three transdiagnostic groups of patients. Consistent with our hypothesis, the magnitude of subthreshold (hypo)manic symptoms was related to a greater clinical severity, regardless of the main categorical diagnosis.
Highlighting the relationship between obsessive–compulsive disorder (OCD) and tic disorder (TD), two highly disabling, comorbid, and difficult-to-treat conditions, Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) acknowledged a new “tic-related” specifier for OCD, ie, obsessive–compulsive tic-related disorder (OCTD). As patients with OCTD may frequently show poor treatment response, the aim of this multicenter study was to investigate rates and clinical correlates of response, remission, and treatment resistance in a large multicenter sample of OCD patients with versus without tics.
A sample of 398 patients with a DSM-5 diagnosis of OCD with and without comorbid TD was assessed from 10 different psychiatric departments across Italy. For the purpose of the study, treatment response profiles in the whole sample were analyzed comparing the rates of response, remission, and treatment-resistance as well as related clinical features. Multivariate logistic regressions were performed to identify possible factors associated with treatment response.
The remission group was associated with later ages of onset of TD and OCD. Moreover, significantly higher rates of psychiatric comorbidities, TD, and lifetime suicidal ideation and attempts emerged in the treatment-resistant group, with larger degrees of perceived worsened quality of life and family involvement.
Although remission was associated with later ages of OCD and TD onset, specific clinical factors, such as early onset and presence of psychiatric comorbidities and concomitant TD, predicted a worse treatment response with a significant impairment in quality of life for both patients and their caregivers, suggesting a worse profile of treatment response for patients with OCTD.
Previous researches highlighted among patients with schizophrenia spectrum disorders (SSD) a significant presence of autistic traits, which seem to influence clinical and functional outcomes. The aim of this study was to further deepen the investigation, evaluating how patients with SSD with or without autistic traits may differ with respect to levels of functioning, self-esteem, resilience, and coping profiles.
As part of the add-on autism spectrum study of the Italian Network for Research on Psychoses, 164 outpatients with schizophrenia (SCZ) were recruited at eight Italian University psychiatric clinics. Subjects were grouped depending on the presence of significant autistic traits according to the Adult Autism Subthreshold Spectrum (AdAS Spectrum) instrument (“AT group” vs “No AT group”). Other instruments employed were: Autism Spectrum Quotient (AQ), Specific Levels of Functioning (SLOF), Self-Esteem Rating scale (SERS), Resilience Scale for Adults (RSA), and brief-COPE.
The “AT group” reported significantly higher scores than the “No AT group” on SLOF activities of community living but significantly lower scores on work skills subscale. The same group scored significantly lower also on SERS total score and RSA perception of the self subscale. Higher scores were reported on COPE self-blame, use of emotional support and humor domains in the AT group. Several correlations were found between specific dimensions of the instruments.
Our findings suggest the presence of specific patterns of functioning, resilience, and coping abilities among SSD patients with autistic traits.
Patients with severe mental illness (SMI), such as schizophrenia or bipolar disorders, are more frequently affected by metabolic syndrome and cardiovascular (CV) diseases than the general population, with a significant reduction in life expectancy. Beyond metabolic syndrome, quantifying the risk of CV morbidity in the long-term may help clinicians to put in place preventive strategies. In this study, we assessed 10-year CV risk in patients with SMI and healthy individuals using an algorithm validated on the Italian general population.
Patients aged 35–69 years diagnosed with SMI were consecutively recruited from psychiatric acute care units. Single CV risk factors were assessed, and 10-year CV risk calculated by means of the CUORE Project 10-year CV risk algorithm, based on the combination of the following risk factors: age, systolic blood pressure, total and high-density lipoprotein cholesterol, diabetes, smoking habit, and hypertensive treatment. Patients’ data were compared with those from the general population. The 10-year CV risk was log-transformed, and multivariable linear regression was used to estimate mean ratios, adjusting for age, and education.
Three hundred patients and 3,052 controls were included in the analysis. Among men, the 10-year CV risk score was very similar between patients with SMI and the general population (mean ratio [MR]: 1.02; 95%CI 0.77–1.37), whereas a 39% increase in 10-year CV risk was observed in women with SMI compared to the general population (MR: 1.39; 95%CI 1.16–1.66).
In our study, women with SMI were consistently more at risk than the general population counterpart, even at younger age.
Schizophrenia is a leading cause of disability. People living with schizophrenia (PLWS) present unemployment, social isolation, excess mortality and morbidity, and poor quality of life. Early recognition and appropriate treatment reduce the risk of chronicity and comorbidity. Personalization and integration of pharmacological and psychosocial interventions, as well as accurate identification and management of psychiatric and somatic comorbidities, can significantly improve mental and physical health of PLWS, promoting recovery.
A three-step Delphi approach was used to explore consensus on the essential components of early recognition and intervention, personalization, and integration of care to improve schizophrenia outcome, and on barriers and challenges to close treatment gaps. The consensus involved 8 Italian experts of schizophrenia, 100 psychiatrists from academic and nonacademic settings, including representatives of Italian Society of Psychiatry, and 65 trainees in psychiatry.
A strong consensus (from mostly agree to totally agree) emerged on the importance of early diagnosis (97%), standardized assessments (91%), correct management of somatic and psychiatric comorbidities (99%), and personalization and integration of care (94%). Lack of time, human resources, and training were identified as the main barriers and challenges to the translation of knowledge into clinical practice.
The results of this Delphi study demonstrated a strong consensus on main components of schizophrenia care, as well as on unmet needs to promote best practice and gaps between knowledge and clinical practice. The involvement of a large group of professionals and trainees in this in-depth consensus process might contribute to raise awareness and stimulate innovative strategies to improve the outcome of PLWS.