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

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.

Clinical and psychological factors associated with resilience in patients with schizophrenia: data from the Italian network for research on psychoses using machine learning

“…We generated an algorithm based on the 85 variables extracted from the afore-mentioned assessments (online Supplementary Table S1) able to classify patients as HR or LR. With this aim, we implemented a mixed inner k-fold/outer leave-site-out (Antonucci et al, 2022b) cross-validation (CV) strategy (see online Supplementary Information, section 4) (Antonucci et al, 2020a(Antonucci et al, , 2020b(Antonucci et al, , 2020c(Antonucci et al, , 2021Koutsouleris et al, 2018Koutsouleris et al, , 2021aKoutsouleris et al, , 2021b. This type of nested CV prevents information leaking between individuals used for training and testing the models (Ruschhaupt, Huber, Poustka, & Mansmann, 2004).…”