Longitudinal symptomatic interactions in long-standing schizophrenia: a novel five-point analysis based on directed acyclic graphs.

BACKGROUND: Recent network models propose that mutual interaction between symptoms has an important bearing on the onset of schizophrenic disorder. In particular, cross-sectional studies suggest that affective symptoms may influence the emergence of psychotic symptoms. However, longitudinal analysis offers a more compelling test for causation: the European Schizophrenia Cohort (EuroSC) provides data suitable for this purpose. We predicted that the persistence of psychotic symptoms would be driven by the continuing presence of affective disturbance. METHODS: EuroSC included 1208 patients randomly sampled from outpatient services in France, Germany and the UK. Initial measures of psychotic and affective symptoms were repeated four times at 6-month intervals, thereby furnishing five time-points. To examine interactions between symptoms both within and between time-slices, we adopted a novel technique for modelling longitudinal data in psychiatry. This was a form of Bayesian network analysis that involved learning dynamic directed acyclic graphs (DAGs). RESULTS: Our DAG analysis suggests that the main drivers of symptoms in this long-term sample were delusions and paranoid thinking. These led to affective disturbance, not vice versa as we initially predicted. The enduring relationship between symptoms was unaffected by whether patients were receiving first- or second-generation antipsychotic medication. CONCLUSIONS: In this cohort of people with chronic schizophrenia treated with medication, symptoms were essentially stable over long periods. However, affective symptoms appeared driven by the persistence of delusions and persecutory thinking, a finding not previously reported. Although our findings as ever remain hostage to unmeasured confounders, these enduring psychotic symptoms might nevertheless be appropriate candidates for directly targeted psychological interventions.