Using combined environmental-clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression.

Antonucci, Linda A; Penzel, Nora; Sanfelici, Rachele; Pigoni, Alessandro; Kambeitz-Ilankovic, Lana; Dwyer, Dominic; Ruef, Anne; Sen Dong, Mark; Öztürk, Ömer Faruk; Chisholm, Katharine; Haidl, Theresa; Rosen, Marlene; Ferro, Adele; Pergola, Giulio; Andriola, Ileana; Blasi, Giuseppe; Ruhrmann, Stephan; Schultze-Lutter, Frauke; Falkai, Peter; Kambeitz, Joseph; ... (2022). Using combined environmental-clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression. (In Press). The British journal of psychiatry, pp. 1-17. Cambridge University Press 10.1192/bjp.2022.16

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BACKGROUND

Clinical high-risk states for psychosis (CHR) are associated with functional impairments and depressive disorders. A previous PRONIA study predicted social functioning in CHR and recent-onset depression (ROD) based on structural magnetic resonance imaging (sMRI) and clinical data. However, the combination of these domains did not lead to accurate role functioning prediction, calling for the investigation of additional risk dimensions. Role functioning may be more strongly associated with environmental adverse events than social functioning.

AIMS

We aimed to predict role functioning in CHR, ROD and transdiagnostically, by adding environmental adverse events-related variables to clinical and sMRI data domains within the PRONIA sample.

METHOD

Baseline clinical, environmental and sMRI data collected in 92 CHR and 95 ROD samples were trained to predict lower versus higher follow-up role functioning, using support vector classification and mixed k-fold/leave-site-out cross-validation. We built separate predictions for each domain, created multimodal predictions and validated them in independent cohorts (74 CHR, 66 ROD).

RESULTS

Models combining clinical and environmental data predicted role outcome in discovery and replication samples of CHR (balanced accuracies: 65.4% and 67.7%, respectively), ROD (balanced accuracies: 58.9% and 62.5%, respectively), and transdiagnostically (balanced accuracies: 62.4% and 68.2%, respectively). The most reliable environmental features for role outcome prediction were adult environmental adjustment, childhood trauma in CHR and childhood environmental adjustment in ROD.

CONCLUSIONS

Findings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > University Psychiatric Services > University Hospital of Child and Adolescent Psychiatry and Psychotherapy > Research Division
04 Faculty of Medicine > University Psychiatric Services > University Hospital of Child and Adolescent Psychiatry and Psychotherapy

UniBE Contributor:

Schultze-Lutter, Frauke

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1472-1465

Publisher:

Cambridge University Press

Language:

English

Submitter:

Pubmed Import

Date Deposited:

21 Feb 2022 12:22

Last Modified:

05 Dec 2022 16:09

Publisher DOI:

10.1192/bjp.2022.16

PubMed ID:

35152923

Uncontrolled Keywords:

Machine learning PRONIA personalised psychiatry psychosis role functioning

BORIS DOI:

10.48350/165820

URI:

https://boris.unibe.ch/id/eprint/165820

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