Using combined environmental-clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression.
Br J Psychiatry
; : 1-17, 2022 Feb 14.
Article
in En
| MEDLINE
| ID: mdl-35152923
ABSTRACT
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
Br J Psychiatry
Year:
2022
Document type:
Article
Affiliation country:
Germany