Dataset Information

Rethinking the course of psychotic disorders: modelling long-term symptom trajectories.

ABSTRACT:

Background

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.

Method

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.

Results

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.

Conclusion

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.

SUBMITTER: Morgan C

PROVIDER: S-EPMC9647538 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Rethinking the course of psychotic disorders: modelling long-term symptom trajectories.

Morgan Craig C Dazzan Paola P Lappin Julia J Heslin Margaret M Donoghue Kim K Fearon Paul P Jones Peter B PB Murray Robin M RM Doody Gillian A GA Reininghaus Ulrich U

Psychological medicine 20210204 13

<h4>Background</h4>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.<h4>Method</h4>AESOP-10 is a follow-up, at 10 y ...[more]

PMID: 33536092

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Project description:ObjectiveSocial impairment is a long-recognized core feature of schizophrenia and is common in other psychotic disorders. Still, to date the long-term trajectories of social impairment in psychotic disorders have rarely been studied systematically.MethodsData came from the Suffolk County Mental Health Project, a 20-year prospective study of first-admission patients with psychotic disorders. A never-psychotic comparison group was also assessed. Latent class growth analysis was applied to longitudinal data on social functioning from 485 respondents with schizophrenia spectrum disorders and psychotic mood disorders, and associations of the empirically derived trajectories with premorbid social adjustment, diagnosis, and 20-year outcomes were examined.ResultsFour mostly stable trajectories of preserved (N=82; 59th percentile of comparison group sample distribution), moderately impaired (N=148; 17th percentile), severely impaired (N=181; 3rd percentile), and profoundly impaired (N=74; 1st percentile) functioning best described the 20-year course of social functioning across diagnoses. The outcome in the group with preserved functioning did not differ from that of never-psychotic individuals at 20 years, but the other groups functioned significantly worse. Differences among trajectories were already evident in childhood. The two most impaired trajectories started to diverge in early adolescence. Poorer social functioning trajectories were strongly associated with other real-world outcomes at 20 years. Multiple trajectories were represented within each disorder. However, more participants with schizophrenia spectrum disorders had impaired trajectories, and more with mood disorders had better functioning trajectories.ConclusionsThe results highlight substantial variability of social outcomes within diagnoses-albeit overall worse social outcomes in schizophrenia spectrum disorders-and show remarkably stable long-term impairments in social functioning after illness onset across all diagnoses.

Project description:AimsCOVID-19 has long-term impacts on public mental health, while few research studies incorporate multidimensional methods to thoroughly characterise the psychological profile of general population and little detailed guidance exists for mental health management during the pandemic. This research aims to capture long-term psychological profile of general population following COVID-19 by integrating trajectory modelling approaches, latent trajectory pattern identification and network analyses.MethodsLongitudinal data were collected from a nationwide sample of 18 804 adults in 12 months after COVID-19 outbreak in China. Patient Health Questionnaire-9, Generalised Anxiety Disorder-7 and Insomnia Severity Index were used to measure depression, anxiety and insomnia, respectively. The unconditional and conditional latent growth curve models were fitted to investigate trajectories and long-term predictors for psychological symptoms. We employed latent growth mixture model to identify the major psychological symptom trajectory patterns, and ran sparse Gaussian graphical models with graphical lasso to explore the evolution of psychopathological network.ResultsAt 12 months after COVID-19 outbreak, psychological symptoms generally alleviated, and five psychological symptom trajectories with different demographics were identified: normal stable (63.4%), mild stable (15.3%), mild-increase to decrease (11.7%), mild-decrease to increase (4.0%) and moderate/severe stable (5.5%). The finding indicated that there were still about 5% individuals showing consistently severe distress and approximately 16% following fluctuating psychological trajectories, who should be continuously monitored. For individuals with persistently severe trajectories and those with fluctuating trajectories, central or bridge symptoms in the network were mainly 'motor abnormality' and 'sad mood', respectively. Compared with initial peak and late COVID-19 phase, aftermath of initial peak might be a psychologically vulnerable period with highest network connectivity. The central and bridge symptoms for aftermath of initial peak ('appetite change' and 'trouble of relaxing') were totally different from those at other pandemic phases ('sad mood').ConclusionsThis research identified the overall growing trend, long-term predictors, trajectory classes and evolutionary pattern of psychopathological network of psychological symptoms in 12 months after COVID-19 outbreak. It provides a multidimensional long-term psychological profile of the general population after COVID-19 outbreak, and accentuates the essentiality of continuous psychological monitoring, as well as population- and time-specific psychological management after COVID-19. We believe our findings can offer reference for long-term psychological management after pandemics.

Dataset Information

Rethinking the course of psychotic disorders: modelling long-term symptom trajectories.

Background

Method

Results

Conclusion

Publications

Rethinking the course of psychotic disorders: modelling long-term symptom trajectories.

Similar Datasets

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