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

Linda A. Antonucci, Nora Penzel, Rachele Sanfelici, Alessandro Pigoni, Lana Kambeitz-Ilankovic, Dominic Dwyer, Anne Ruef, Mark Sen Dong, Ömer Faruk Öztürk, Theresa Haidl, Marlene Rosen, Adele Ferro, Giulio Pergola, Ileana Andriola, Giuseppe Blasi, Stephan Ruhrmann, Frauke Schultze-Lutter, Peter Falkai, Joseph Kambeitz, Rebekka LencerUdo Dannlowski, Rachel Upthegrove, Raimo K.R. Salokangas, Christos Pantelis, Eva Meisenzahl, Stephen J. Wood, Paolo Brambilla, Stefan Borgwardt, Alessandro Bertolino, Nikolaos Koutsouleris

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)229–245
Number of pages17
JournalBritish Journal of Psychiatry
Volume220
Issue number4
Early online date14 Feb 2022
DOIs
Publication statusPublished - 20 Apr 2022

Bibliographical note

Funding
This work was supported by the EU-FP7-HEALTH grant for the project ‘PRONIA’ (Personalized Prognostic Tools for Early Psychosis Management; PI: N.K., agreement number: 602152) and by the Structural European Funding of the Italian Minister of Education and Research (Attraction and International Mobility – AIM - action, grant agreement No 1859959). The AIM action also funds L.A.A.’s salary.

Declaration of interest
A.B. has received lecture fees from Otsuka, Janssen and Lundbeck; and consultant fees from Biogen. N.K. has received honoraria for talks presented at education meetings organised by Otsuka/Lundbeck. N.K. and E.M. hold commercial patents that are related to the present work (https://patents.google.com/patent/US20160192889/). C.P. participated in advisory boards for Janssen-Cilag, AstraZeneca, Lundbeck and Servier; received honoraria for talks presented at educational meetings organized by AstraZeneca, Janssen-Cilag, Eli Lilly, Pfizer, Lundbeck and Shire; and was supported by National Health and Medical Research Council Senior Principal Research Fellowship (grants 628386 and 1105825) and European Union–National Health and Medical Research Council (grant 1075379). G.P.'s position is funded by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement number 798181. L.A.A.'s salary is funded from the Structural European Funding of the Italian Minister of Education (Attraction and International Mobility – AIM - action, grant agreement number 1859959). L.K.-I., D.D., F.S.-L. and R.U. are members of the BJPsych editorial board and did not take part in the review or decision-making process of this paper. No other disclosures were reported.

Keywords

  • Machine learning
  • personalised psychiatry
  • PRONIA
  • psychosis
  • role functioning

ASJC Scopus subject areas

  • Psychiatry and Mental health

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