The evolution of depression in first-episode psychosis: A naturalistic database study

Psychopathological networks in psychosis: Changes over time and clinical relevance. A long-term cohort study of first-episode psychosis

BACKGROUND

First-episode psychosis is a critical period for early interventions to reduce the risk of poor outcomes and relapse as much as possible. However, uncertainties about the long-term outcomes of symptomatology remain to be ascertained.

METHODS

The aim of the present study was to use network analysis to investigate first-episode and long-term stages of psychosis at three levels of analysis: micro, meso and macro. The sample was a cohort of 510 patients with first-episode psychoses from the SEGPEP study, who were reassessed at the long-term follow-up (n = 243). We used the Comprehensive Assessment of Symptoms and History for their assessments and lifetime outcome variables of clinical relevance.

RESULTS

Our results showed a similar pattern of clustering between first episodes and long-term follow-up in seven psychopathological dimensions at the micro level, 3 and 4 dimensions at the meso level, and one at the macro level. They also revealed significant differences between first-episode and long-term network structure and centrality measures at the three levels, showing that disorganization symptoms have more influence in long-term stabilized patients.

CONCLUSIONS

Our findings suggest a relative clustering invariance at all levels, with the presence of two domains of disorganization as the most notorious difference over time at micro level. The severity of disorganization at the follow-up was associated with a more severe course of the psychosis. Moreover, a relative stability in global strength of the interconnections was found, even though the network structure varied significantly in the long-term follow-up. The macro level was helpful in the integration of all dimensions into a common psychopathology factor, and in unveiling the strong relationships of psychopathological dimensions with lifetime outcomes, such as negative with poor functioning, disorganization with high antipsychotic dose-years, and delusions with poor adherence to treatment. These results add evidence to the hierarchical, dimensional and longitudinal structure of psychopathological symptoms and their clinical relevance in first-episode psychoses.

Rumination mediates the relationship between childhood trauma and depressive symptoms in schizophrenia patients

Rumination and childhood trauma are related to depressive symptoms in clinical and non-clinical individuals. This is the first study aimed to test the mediating effect of rumination on the relationship between childhood trauma and depressive symptoms in schizophrenia patients. A total of 313 schizophrenia patients were recruited in the present study. The 17-item Hamilton Depression Rating Scale (HAMD-17) was adopted to evaluate depressive symptoms, the short-form Childhood Trauma Questionnaire (CTQ-SF) and the 10-item Ruminative response scale (RRS-10) were utilized to assess the childhood trauma and rumination in patients, respectively. Our results showed that 168 schizophrenia patients (53.67%) had comorbid depressive symptoms. These patients with depressive symptoms had higher levels of childhood trauma [both CTQ-SF total scores and emotional abuse (EA), emotional neglect (EN), physical neglect (PN) subscale scores] and rumination (both RRS-10 total scores and brooding, reflection subscale scores) compared to patients without depressive symptoms. The stepwise logistic regression analysis identified that EN (OR 1.196, P = 0.003), PN (OR 1.1294, P < 0.001), brooding (OR 1.291, P < 0.001) and reflection (OR 1.481, P < 0.001) could independently predict the depressive symptoms in schizophrenia patients. Moreover, RRS-10 and its subscale scores could mediate the relationship between depressive symptoms and childhood trauma, especially EA, EN and PN in schizophrenia. Our preliminary findings suggest that the rigorous assessment and psychosocial interventions of rumination are important to alleviate the influence of childhood trauma on depressive symptoms in schizophrenia patients.

Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning

During the COVID-19 pandemic, an increase in the incidence of psychiatric disorders in the general population and an increase in the severity of symptoms in psychiatric patients have been reported. Anxiety and depression symptoms are the most commonly observed during large-scale dramatic events such as pandemics and wars, especially when these implicate an extended lockdown. The early detection of higher risk clinical and non-clinical individuals would help prevent the new onset and/or deterioration of these symptoms. This in turn would lead to the implementation of public policies aimed at protecting vulnerable populations during these dramatic contingencies, therefore optimising the effectiveness of interventions and saving the resources of national healthcare systems. We used a supervised machine learning method to identify the predictors of the severity of psychiatric symptoms during the Italian lockdown due to the COVID-19 pandemic. Via a case study, we applied this methodology to a small sample of healthy individuals, obsessive-compulsive disorder patients, and adjustment disorder patients. Our preliminary results show that our models were able to predict depression, anxiety, and obsessive-compulsive symptoms during the lockdown with up to 92% accuracy based on demographic and clinical characteristics collected before the pandemic. The presented methodology may be used to predict the psychiatric prognosis of individuals under a large-scale lockdown and thus supporting the related clinical decisions.