Diagnostic stability is a controversial issue in first episode psychosis (FEP) due to heterogenous symptoms and unclear affective symptoms. Differencing affective and non-affective psychoses is important as treatment strategies are different. Initial affective symptomatology has low specificity for predicting the subsequent diagnosis of affective psychosis. Sex has proven to be relevant for clinical and functional outcomes but it remains unclear how sex may contribute to diagnosis switch of FEP.

To determine the role of sex in diagnostic stability in a sample of FEP after 1-year follow-up.

Diagnoses of FEP patients from Hospital del Mar of Barcelona were assessed at baseline and 1 year after. Univariate analyses was perfomed for all diagnoses and dichotomic variable (affective/non-affective). Logistic regression model was perfomed to know which variables predict diagnosis switch.

256 patients were enrolled. No differences were found at baseline between completers and non-completers (Table 1). No significant differences between men and women at baseline diagnosis were found, neither all diagnoses (p=0.274) nor the dichotomic variable affective/non-affective (p=0.829) (Table 2AB). Significant differences were found at 1-year follow-up between men and women, for all diagnoses (p=0.043) and the dichotomic variable (p=0.039). Sex was the only variable that predicted diagnosis switch (Figure 1), PANSS, CDSS, YMRS, GAF and cannabis did not.Table 1.

Baseline characteristics of participants

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Sex has proven to be the main predictor of switching initial diagnosis of FEP.

None Declared

More than 60% of psychiatric patients are smokers. Besides a culture of smoking allowance amongst these patients, in the last years all psychiatric services in developed countries are becoming smoking free. Several studies have found negative expectancies in staff and patients where smoking bans are planned. Nevertheless, not many studies have focused on objective measures as changes in medication and features of the admissions period.

We want to find differences in terms of clinical and treatment management in psychiatric hospitalization associated to smoking ban.

We collected data (regarding medication, socio-demographic and admission characteristics) from all patients admitted to an acute psychiatric hospital in two different time periods, before and after the smoking ban was in force. We collected data (regarding medication, sociodemographic and admission characteristics) from all patients admitted to an acute psychiatric hospital in two different time periods, before and after the smoking ban was in force.

More number of leaves of absence (p=0,020) and movement restrictions (p=0,001) during the ban period occurred in comparison to the pre-ban period. On the contrary a lack of significant differences in terms of hospital stay (duration (p=0,479), rate of involuntary admissions (p=0,371) and voluntary discharges (p=0,377)), use of sedatives and doses of antipsychotics was found (p= 0,640 and p=0,194).

More number of leaves of absence (p=0,020) and movement restrictions (p=0,001) during the ban period occurred in comparison to the pre-ban period. On the contrary a lack of significant differences in terms of hospital stay (duration (p=0,479), rate of involuntary admissions (p=0,371) and voluntary discharges (p=0,377)), use of sedatives and doses of antipsychotics was found (p= 0,640 and p=0,194).

The smoking-ban may have driven to increased grants for leave of absence that secondarily may have underpin demands for leave of absence in patients not allowed to and thus, it may have contribute to an increase in movement restrictions. Further studies with longer periods after the ban may clarify this issue. The smoking-ban may have driven to increased grants for leave of absence that secondarily may have underpin demands for leave of absence in patients not allowed to and thus, it may have contribute to an increase in movement restrictions. Further studies with longer periods after the ban may clarify this issue.

Clozapine is the only drug approved for resistant schizophrenia, but remains underused because of its side effects. Sedation is common, but its management is unclear.

To analyze factors associated with clozapine-induced sedation and the efficacy of common treatment strategies.

To determine clozapine-induced sedation factors and possible therapeutic strategies.

Using two years’ electronic records of a community cohort of resistant schizophrenia spectrum disorder cases on clozapine, we performed three analyses: a cross-sectional analysis of which factors were associated with number of hours slept (objective proxy of sedation), and two prospective analyses: which factors were associated with changes in hours slept, and the efficacy of the main pharmacological strategies for improving sedation.

One hundred and thirty-three patients were included; 64.7% slept at least 9 hours/daily. Among monotherapy patients (n = 30), only norclozapine levels (r = .367, P = .033) correlated with sleeping hours. Multiple regression analyses confirmed the findings (r = .865, P < .00001). Using the cohort prospectively assessed (n = 107), 42 patients decreased the number of hours slept between two consecutive appointments. Decreasing clozapine (40%) or augmenting with aripiprazole (36%) were the most common factors. In the efficacy analysis, these two strategies were recommended to 22 (20.6%) and 23 (21.5%) subjects, respectively. The majority (81.8% and 73.9%) did not report differences in the hours slept.

Sedationis common and involves pharmacological and non-pharmacological factors. The only correlation was a weak correlation between norclozapine plasma levels and total sleeping hours. Reducing clozapine and aripiprazole augmentation were the most successful strategies to ameliorate sedation, although both strategies were effective only in a limited numbers of subjects.

The authors have not supplied their declaration of competing interest.

Schizophrenia is a developmental disorder that includes non-psychiatric abnormalities [2]. Metabolic abnormalities prior to antipsychotic treatment exist. The clozapine metabolic profile causes clozapine underuse in resistant schizophrenia [1].

To correlate metabolic profile with psychiatric severity and compare the correlations between clozapine/non-clozapine patients.

To determine possible contributory factors to metabolic abnormalities in schizophrenia.

We cross-sectionally analyzed all patients from a Spanish long-term mental care facility (n = 139). Schizophrenic/schizoaffective patients were selected (n = 118). N = 31 used clozapine. We paired clozapine and non-clozapine patients by sex and age and assessed metabolic and psychopathologic variables.

We compared psychopathologic variables between patients with/without cardiometabolic treatment and the differences between clozapine/non-clozapine groups.

We analyzed: 27 clozapine/29 non-clozapine patients. A total of 67,9% males with a mean age of 51.3 (SD 9.6) years. In the whole sample TG negatively correlated with Negative-CGI (r: −0,470, P: 0.049) and HDL-cholesterol correlates with Global-CGI(r: 0,505, P: 0.046). Prolactin correlated with the number of antipsychotics (r: 0.581, P: 0.023) and IMC (r: 0.575, P: 0.025). Clozapine group took less antipsychotics [Fisher (P: 0.045)] and had higher scores in total BRPS scale [t-Student (P: 0.036)]. They did not use more cardiometabolic treatment. There were no psychopathological differences between cardiometabolic treated/non-treated patients. In the non-cardiometabolic treated group (n = 35/62,5%), IMC negatively correlated with positive and total BPRS, positive, cognitive and global-CGI. We found negative correlations between metabolic parameters and psychopathology in clozapine (40%) and non-clozapine subgroups (60%). In the cardiometabolic treated group (n = 21/37,5%), we did not find these correlations in either of clozapine (61.9%) or non-clozapine (38.1%) subgroups.

Severity [2], prolactine [3] and treatment [1] could play a role in metabolic parameters. In our sample we found negative correlations between psychopathological and metabolic parameters.

References not available.

The authors have not supplied their declaration of competing interest.

For resistant schizophrenia, the only approved treatment is clozapine. However, clozapine is underused, mainly due to its wide range of side-effects. Secondary effects differ amongst antipsychotics (Leucht et al., 2009). Despite that there is no good evidence that combined antipsychotics offer any advantage over the use of a single antipsychotic, combination increases the frequency of adverse events (Maudsley guidelines).

To compare the side-effect profile between clozapine and non-clozapinepatients.

To provide evidence that clozapine patients do not show a worse side-effects profile.

We cross-sectionally analysed all patients from a Spanish long-term mental care facility (n = 139). Schizophrenic/schizoaffective patients were selected (n = 118) and their treatment was assessed, 31 patients used clozapine. We paired clozapine and non-clozapine patients by sex and age and assessed antipsychotic side effects and possible confounder variables.

Our sample was 27 clozapine patients and 29 non-clozapine patients. 67,9% were male with a mean age of 51.3 (SD 9.6) years. For continuous variables: age, BMI, waist/hip, cholesterol, TG, glucose, prolactin, heart-rate, blood pressure, sleeping hours, the only statistical differences found were lower heart-rate (P = 0.001) in clozapine group and higher salivation subscale of SAS (P = 0.002) in clozapine group. For discrete variables: monotherapy, obesity, overweight, metabolic syndrome or possible confounders as propranolol, laxative, diet, antiglycemiant or insulin, fibrates or statins, antihypertensive or anticholinergic, no statistical differences were found.

We did not find differences in cardiometabolic parameters, which are the main barrier to prescribing clozapine, probably due to the concomitant use of other drugs in both groups.

The authors have not supplied their declaration of competing interest.