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Increasing conceptual clarity and confounders identification: a pragmatic way to enhance prognostic precision in ENIGMA clinical high risk for psychosis (CHR-P)

Molecular Psychiatry volume 30, pages 3319–3320 (2025) Cite this article

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Zhu and colleagues [1] utilized structural magnetic resonance imaging data from the ENIGMA Clinical High-Risk for Psychosis (CHR-P) Working Group cohort (based on 21 sites) to assess the ability of machine learning to predict psychosis. The primary outcome, transiton to psychosis, occurred in 144 out of 1165 CHR-P individuals (12.36%) and the study examined whether neuroimaging data processed through machine learning could discriminate between three CHR-P subgroups (transitioned, not transitioned, unknown outcome) and healthy controls.

The classifier achieved an accuracy of 85% on the training dataset and 73% on the independent confirmatory dataset. CHR-P individuals who did not transition to psychosis were more likely to be classified as healthy controls compared to those who developed psychosis (classification rate to healthy controls: CHR-P transitioned, 30%; CHR-P not transitioned, 73%; CHR-P unknown outcome, 80%).

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Authors and Affiliations

  1. Chair of Psychiatry, Faculty of Biomedical Sciences, University of Southern Switzerland, Lugano, Switzerland

    Andrea Raballo

  2. Cantonal Sociopsychiatric Organisation, Mendrisio, Switzerland

    Andrea Raballo

  3. Department of Mental Health and Pathological Addiction, Child and Adolescent Neuropsychiatry Service, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy

    Michele Poletti

  4. Department of Neuroscience, University of Turin, Turin, Italy

    Antonio Preti

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  1. Andrea Raballo
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  2. Michele Poletti
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  3. Antonio Preti
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Correspondence to Andrea Raballo.

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Raballo, A., Poletti, M. & Preti, A. Increasing conceptual clarity and confounders identification: a pragmatic way to enhance prognostic precision in ENIGMA clinical high risk for psychosis (CHR-P). Mol Psychiatry 30, 3319–3320 (2025). https://doi.org/10.1038/s41380-025-02948-8

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