Abstract
The clinical course of psychotic disorders is highly variable. Typically, researchers have captured different course types using broad pre-defined categories. However, whether these adequately capture symptom trajectories of psychotic disorders has not been fully assessed. Using data from AESOP-10, we sought to identify classes of individuals with specific symptom trajectories over a 10-year follow-up using a data-driven approach. AESOP-10 is a follow-up, at 10 years, of 532 incident cases with a first episode of psychosis initially identified in south-east London and Nottingham, UK. Using extensive information on fluctuations in the presence of psychotic symptoms, we fitted growth mixture models to identify latent trajectory classes that accounted for heterogeneity in the patterns of change in psychotic symptoms over time. We had sufficient data on psychotic symptoms during the follow-up on 326 incident patients. A four-class quadratic growth mixture model identified four trajectories of psychotic symptoms: (1) remitting-improving (58.5%); (2) late decline (5.6%); (3) late improvement (5.4%); (4) persistent (30.6%). A persistent trajectory, compared with remitting-improving, was associated with gender (more men), black Caribbean ethnicity, low baseline education and high disadvantage, low premorbid IQ, a baseline diagnosis of non-affective psychosis and long DUP. Numbers were small, but there were indications that those with a late decline trajectory more closely resembled those with a persistent trajectory. Our current approach to categorising the course of psychotic disorders may misclassify patients. This may confound efforts to elucidate the predictors of long-term course and related biomarkers.
Highlights
Our knowledge of the nature and predictors of the long-term course and outcomes of psychotic disorders is surprisingly limited
AESOP-10 is a follow-up at approximately 10 years of a cohort of 532 incident cases with a first episode of non-affective or affective psychosis initially identified in South East London and Nottingham, UK
There was no evidence of an association between missingness and age, gender, ethnicity, level of education, social disadvantage, premorbid Intelligence Quotient (IQ), diagnosis, DUP and mode of onset, and only weak evidence of an association between duration of untreated psychosis and missingness in timeline data over the 10-year follow-up
Summary
Our knowledge of the nature and predictors of the long-term course and outcomes of psychotic disorders is surprisingly limited. We are aware of two further studies since (Kotov et al, 2017; Secher et al, 2015) This body of research is methodologically varied, which makes direct comparisons difficult and limits any general conclusions we can make about the long-term trajectories of psychosis following a first episode and associated factors. Researchers have captured different course types using broad pre-defined categories Whether these adequately capture symptom trajectories of psychotic disorders has not been fully assessed. A four-class quadratic growth mixture model identified four trajectories of psychotic symptoms: (1) remitting-improving (58.5%); (2) late decline (5.6%); (3) late improvement (5.4%); (4) persistent (30.6%). Our current approach to categorising the course of psychotic disorders may misclassify patients This may confound efforts to elucidate the predictors of long-term course and related biomarkers
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