Despite innovative treatments, the impairment in real-life functioning in subjects with schizophrenia (SCZ) remains an unmet need in the care of these patients. Recently, real-life functioning in SCZ was associated with abnormalities in different electrophysiological indices. It is still not clear whether this relationship is mediated by other variables, and how the combination of different EEG abnormalities influences the complex outcome of schizophrenia.

The purpose of the study was to find EEG patterns which can predict the outcome of schizophrenia and identify recovered patients.

Illness-related and functioning-related variables were measured in 61 SCZ at baseline and after four-years follow-up. EEGs were recorded at the baseline in resting-state condition and during two auditory tasks. We performed Sparse Partial Least Square (SPLS) Regression, using EEG features, age and illness duration to predict clinical and functional features at baseline and follow up. Through a Linear Support Vector Machine (Linear SVM) we used electrophysiological and clinical scores derived from SPLS regression, in order to classify recovered patients at follow-up.

We found one significant latent variable (p<0.01) capturing correlations between independent and dependent variables at follow-up (RHO=0.56). Among individual predictors, age and illness-duration showed the highest scores; however, the score for the combination of the EEG features was higher than all other predictors. Within dependent variables, negative symptoms showed the strongest correlation with predictors. Scores resulting from SPLS Regression classified recovered patients with 90.1% of accuracy.

A combination of electrophysiological markers, age and illness-duration might predict clinical and functional outcome of schizophrenia after 4 years of follow-up.

Different electrophysiological indices have been investigated to identify diagnostic and prognostic markers of schizophrenia (SCZ). However, these indices have limited use in clinical practice, since both specificity and association with illness outcome remain unclear. In recent years, machine learning techniques, through the combination of multidimensional data, have been used to better characterize SCZ and to predict illness course.

The aim of the present study is to identify multimodal electrophysiological biomarkers that could be used in clinical practice in order to improve precision in diagnosis and prognosis of SCZ.

Illness-related and functioning-related variables were measured at baseline in 113 subjects with SCZ and 57 healthy controls (HC), and after four-year follow-up in 61 SCZ. EEGs were recorded at baseline in resting-state condition and during two auditory tasks (MMN-P3a and N100-P3b). Through a Linear Support Vector Machine, using EEG data as predictors, four models were generated in order to classify SCZ and HC. Then, we combined unimodal classifiers’ scores through a stacking procedure. Pearson’s correlations between classifiers score with illness-related and functioning-related variables, at baseline and follow-up, were performed.

Each EEG model produced significant classification (p < 0.05). Global classifier discriminated SCZ from HC with accuracy of 75.4% (p < 0.01). A significant correlation (r=0.40, p=0.002) between the global classifier scores with negative symptoms at follow-up was found. Within negative symptoms, blunted affect showed the strongest correlation.

Abnormalities in electrophysiological indices might be considered trait markers of schizophrenia. Our results suggest that multimodal electrophysiological markers might have prognostic value for negative symptoms.

To identify factors associated with suicide attempts using data from a large, 3-year, multinational follow-up study of schizophrenia (SOHO study).

All baseline characteristics of 8,871 adult patients with schizophrenia collected in patients included in the SOHO study were included in a GEE logistic regression post-hoc analysis comparing patients who attempted suicide during the study with those who did not.

A total of 384 (4.3%) patients attempted or committed suicide. The risk factors that resulted statistically associated with suicide attempt were a lifetime history of suicide attempts (OR 3.6 [95% CI 2.8, 4.6; p< 0.0001]), suicide attempts in the last 6 months (OR 2.5 [95% CI 1.8, 3.4; p< 0.0001]), prolactin-related side effects (OR 2.0 [95%CI 1.4, 2.9; p=0.0002]), CGI depression (OR 1.2 [95% CI 1.1, 1.3; p=0.0004]) and history of hospitalization for schizophrenia (OR 1.4 [95% CI 1.1, 1.8; p=0.009]).

In view of the observational design of the study and the post-hoc nature of the analysis, the identified risk factors should be confirmed by ad-hoc specifically designed studies.

Medication non-compliance is common in the treatment of depression, particularly in Asia.

1) To describe the frequency and factors associated with medication non-compliance. 2) To study the influence of non-compliance on treatment outcomes.

Nine hundred and nine in- and out-patients from Asia presenting with a new or first episode of major depressive disorder were enrolled in a 3-month prospective observational study. Clinical severity and quality of life were assessed, using Hamilton Depression Scale (HAMD-17), Clinical Global Impression Severity (CGI-S), and EuroQoL measures (EQ-5D and EQ-VAS). Medication compliance was also assessed by the investigator and patient. Linear and logistic multiple regression models were used to analyze the consequences of non-compliance.

The proportion of non-compliant patients as assessed by the investigator was 16%. Sociodemographic factors and clinical severity were not associated with compliance at baseline. Regression models showed that medication non-compliance was associated with worse depression severity (difference in HAMD-17 -3.98; 95% CI -5.10, -2.87) and overall clinical severity (CGI-S difference -0.46; 95%CI -0.68, -0.24) at three months. Medication non-compliance was also associated with lower quality of life at three months (EQ-VAS difference -7.47; 95%CI -11.13, - 3.82) and EQ-5D score difference -0.08; 95%CI -0.1, -0.04)). Compliant patients had higher odds of response (odds ratio (OR) 3.18; 95% CI 1.98, 5.10) and remission (OR 3.94; 95% CI 2.42, 6.43) compared with non-compliant patients.

Patients non-compliant with medication had worse 3-month outcomes in terms of depression severity, quality of life, and response and remission rates, compared with compliant patients.

The analysis of medication discontinuation may allow the comparison of the effectiveness of different medications and may help us understand treatment patterns in depression. Clinical guidelines recommend at least six months of antidepressant maintenance treatment for major depressive disorder (MDD).

To determine the duration of antidepressant treatment in Asian patients treated with antidepressants for a major depressive episode and to understand the reasons and factors associated with discontinuation.

Nine hundred and nine in- and out-patients from Asia, of which 569 started an antidepressant medication at the baseline visit, presenting with a new or first episode of MDD were enrolled in a 3-month prospective observational study. The Kaplan-Meier method and Cox models were used to estimate discontinuation rates and factors associated with discontinuation. Survival analysis with competing risks was used to analyze the influence of different reasons for discontinuation.

Of the 569 patients included in the study, 430 (75.6%) were evaluated at three months and analyzed. Of them, 242 (56%) discontinued the treatment during the three months follow-up and 188 maintained it. Of the overall sample, half of the patients discontinued the medication within 70 days. The most frequent reason for discontinuation was inadequate response (n=155, 64%), followed by adequate response (n=62, 26%). A relatively high proportion of patients with adequate response (30% at 130 days) discontinued the medication. Country and type of antidepressant were associated with medication discontinuation.

Medication discontinuation in Asian patients with depression is high, even for patients who respond adequately to treatment.

The aims of this study were to determine the presence of painful physical symptoms (PPS) and its impact on depression outcomes in different gender and age groups.

Three hundred in- and out-patients from China presenting with a new or first episode of major depressive disorder were enrolled in a 3- month prospective observational study from Asia (N=909). Hamilton Depression Scale (HAMD-17), Clinical Global Impression Severity (CGI-S), EuroQoL and the pain-related items of the Somatic Symptom Inventory were administered. Patients were classified into three age groups (<40, n=119; =40-<60, n=133; =60, n=48). Linear and logistic regression models were fitted to assess the relationship between PPS at baseline and outcomes.

Older patients had higher HAMD-17 severity at baseline. HAMD score was 25.9 (SD 6.1) in =60 vs. 22.5 (SD 5.0) in <40 and 24.8 (SD 5.2) in =40-<60. There were no statistically significant differences in the proportion of patients with PPS across gender and age groups. During follow-up, depression severity improved. There were no statistically significant differences in the degree of improvement by gender, but there were differences by age group. Mean change in HAMD was -16.4 (95%CI -17.7;-15.1) for those <40, -19.9 (95%CI -21.1;-18.7) in 40-60 and - 20.3 (95%CI -22.6;-17.9) in >60. PPS positive patients had worse clinical and quality of life outcomes across genders and age groups.

The presence of painful physical symptoms is associated with a lower improvement in depression outcomes and a lower quality of life in patients with major depression across different gender and age groups.

To date, the proposition of recurrence as a subclinical bipolar disorder feature has not received adequate testing.

We used the Italian version of the bipolar spectrum diagnostic scale (BSDS), a self-rated questionnaire of bipolar risk, in a sample of patients with mood disorders to test its specificity and sensitivity in identifying cases and discriminating between high risk for bipolar disorder major depressive patients (HRU) and low risk (LRU) adopting as a high recurrence cut-off five or more lifetime major depressive episodes.

We included 115 patients with DSM-5 bipolar disorder (69 type I, 41 type II, and 5 NOS) and 58 with major depressive disorder (29 HRU and 29 LRU, based on the recurrence criterion). Patients filled-out the Italian version of the BSDS, which is currently undergoing a validation process.

The BSDS, adopting a threshold of 14, had 84% sensitivity and 76% specificity. HRU, as predicted, scored on the BSDS intermediate between LRU and bipolar disorder. Clinical characteristics of HRU were more similar to bipolar disorder than to LRU; HRU, like bipolar disorder patients, had more lifetime hospitalizations, higher suicidal ideation and attempt numbers, and higher rates of family history of suicide.

The BSDS showed satisfactory sensitivity and sensitivity. Splitting the unipolar sample into HRU and LRU, on the basis of the at least 5 lifetime major depressive episodes criterion, yielded distinct unipolar subpopulations that differ on outcome measures and BSDS scores.

The authors have not supplied their declaration of competing interest.

The use of Performance and Image-Enhancing Drugs (PIEDs) is on the increase and appears to be associated with several psychopathological disorders, whose prevalence in unclear.

We aimed to evaluate the differences–if any–in the prevalence of body image disorders (BIDs) and eating disorders (EDs) in PIEDs users athletes vs. PIEDs nonusers ones.

We enrolled 84 consecutive professional and amateur athletes (35.8% females; age range = 18–50), training in several sports centers in Italy. They underwent structured interviews (SCID I/SCID II) and completed the Body Image Concern Inventory (BICI) and the Sick, Control, One, Fat, Food Eating Disorder Screening Test (SCOFF). Mann-Whitney U test and Fisher's exact test were used for comparisons.

Of the 84 athletes, 18 (21.4%) used PIEDs. The most common PIEDs were anabolic androgenic steroids, amphetamine-like substances, cathinones, ephedrine, and caffeine derivatives (e.g. guarana). The two groups did not differ in socio-demographic characteristics, but differed in anamnestic and psychopathological ones, with PIEDs users athletes being characterized by significantly (P-values < 0.05) higher physical activity levels, consuming more coffee, cigarettes, and psychotropic medications (e.g. benzodiazepines) per day, presenting more SCID diagnoses of psychiatric disorders, especially Substance Use Disorders, Eating Disorders, Body Dysmorphic Disorder (BDD), and General Anxiety Disorders, showing higher BICI scores, which indicate a higher risk of BDD, and higher SCOFF scores, which suggest a higher risk of BIDs and EDs.

In PIEDs users athletes body image and eating disorders, and more in general psychopathological disorders, are more common than in PIEDs nonusers athletes.

The authors have not supplied their declaration of competing interest.

The aim of this analysis was to evaluate the economic consequences of a new treatment approach in the treatment of schizophrenia in the Italian setting. In terms of direct costs, in Italy was estimated that the main driver were represented by hospitalization and residential cost (71% of total direct cost per patient), followed by semi-residential services (13%), anti-psychotic and other drugs (8%) and ambulatory services (8).

A probabilistic cost consequence model was developed to estimate the potential cost reductions derived from an early treatment with atypical long-acting injectable anti-psychotics (aLAIs) drugs. A systematic literature review was carried out to identify direct and indirect costs associated to the management of schizophrenic patients in Italy. The model projects a scenario analysis in order to estimate potential cost reductions applying a new model management (MoMa) based on patient recovery and early aLAIs treatment.

Overall, the total economic burden associated with schizophrenia was estimated at €2.7 billion per year. A total of 50.5% of the economic burden was related to indirect costs and 49.5% to direct costs. Drug costs correspond to 10% of the total expenditure in terms of direct costs, while hospitalization and residential costs accounts for 81%. Scenario analysis demonstrate a potential cost reduction between 200 million and 300 million based on the effects of MoMa over the reduction of hospitalization and residential costs.

This analysis was the first attempt to translate clinical management aspects in economic consequences and will be a useful instruments for decision maker.

The authors have not supplied their declaration of competing interest.

The increased use of the MATRICS Consensus Cognitive Battery (MCCB) to investigate cognitive dysfunctions in schizophrenia fostered interest in its sensitivity in the context of family studies. As various measures of the same cognitive domains may have different power to distinguish between unaffected relatives of patients and controls, the relative sensitivity of MCCB tests for relative–control differences has to be established. We compared MCCB scores of 852 outpatients with schizophrenia (SCZ) with those of 342 unaffected relatives (REL) and a normative Italian sample of 774 healthy subjects (HCS). We examined familial aggregation of cognitive impairment by investigating within-family prediction of MCCB scores based on probands’ scores.

Multivariate analysis of variance was used to analyze group differences in adjusted MCCB scores. Weighted least-squares analysis was used to investigate whether probands’ MCCB scores predicted REL neurocognitive performance.

SCZ were significantly impaired on all MCCB domains. REL had intermediate scores between SCZ and HCS, showing a similar pattern of impairment, except for social cognition. Proband's scores significantly predicted REL MCCB scores on all domains except for visual learning.

In a large sample of stable patients with schizophrenia, living in the community, and in their unaffected relatives, MCCB demonstrated sensitivity to cognitive deficits in both groups. Our findings of significant within-family prediction of MCCB scores might reflect disease-related genetic or environmental factors.