Welcome to the EPA 2021 Interactive Programme
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S0169 - Recovery in Schizophrenia: A Network Analysis of Inter-relationships Among Disease-related Variables, Personal Resources, Context-related Factors and Real-life Functioning
Central to recovery-oriented approaches in schizophrenia are treatment integration and personalization, targeting key variables beyond symptom reduction. The Italian network for research on psychoses conducted a study demonstrating, using network analysis, the central role of community activities in bridging the effects of symptoms, cognition, functional capacity and service engagement on real-word functioning. A 4-year follow-up study was recently completed and the presentation will illustrate the findings.
Network analysis was used to test whether relationships among all variables at baseline were similar at follow-up. In addition, the network structure was compared between subjects classified as recovered or non-recovered at follow-up.
Six hundred eighteen subjects were assessed at both baseline and 4-year follow-up.
Results showed that the network structure was stable from baseline to follow-up, and the overall strength of the connections among variables did not significantly change. Functional capacity and everyday life skills were the most central variables in the network at both baseline and follow-up, while psychopathological variables were more peripheral. The network structure of non-recovered patients was similar to the one observed in the whole sample, but very different from that of recovered subjects, showing few connections among the different nodes.
These data strongly suggest that connections among symptoms/dysfunctions tend to maintain over time, contributing to poor outcome in schizophrenia.
Early treatment plans, targeting variables with high centrality, might prevent the emergence of self-reinforcing networks of symptoms and dysfunctions in people with schizophrenia.
S0170 - Symptom-specific Assessment of Treatment Efficacy: The Potential of Network Estimation Techniques
Most studies on the efficacy of psychiatric treatments consider overall scale scores as outcome measures. A focus on individual symptoms would, however, result in a more precise assessment of treatment efficacy and has potential in improving our understanding of the working mechanisms of treatment. Such an approach may also help in improving the identification of patients who are -based on their pretreatment symptomatology- the most likely to benefit from a particular treatment.
To show the potential of network estimation techniques in a) unraveling the diverse symptom-specific responses to various depression treatments and b) improving the identification of patients who are the most likely to benefit from these treatments.
First, we combined patient-level data of multiple trials considering various depression treatments, such as antidepressant medication and (internet-based) cognitive-behavioral therapy. Network estimation techniques were used to determine the complex patterns in which symptom-specific responses to treatment were related.
Individual clinical symptoms differed substantially in their responses to treatment and these symptom-specific responses were related in a complex manner. Patients suffering from symptoms that were directly affected by a particular treatment were -by definition- the most likely to benefit from that treatment.
Network estimation techniques were able to unravel the diverse symptom-specific responses to treatment and could help in improving our understanding of the chain of events leading to a clinical response. Information from the networks could also help in improving the identification of patients who were the most likely to benefit from a particular treatment.