Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression
Open accessAbstract
Importance: Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives: To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants: This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures: Accuracy and generalizability of prognostic systems. Results: A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and Relevance: These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.
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- Authors
- Koutsouleris, N., Dwyer, D. B., Degenhardt, F., Maj, C., Urquijo-Castro, M. F., Sanfelici, R., Popovic, D., Oeztuerk, O., Haas, S. S., Weiske, J., Ruef, A., Kambeitz-Ilankovic, L., Antonucci, L. A., Neufang, S., Schmidt-Kraepelin, C., Ruhrmann, S., Penzel, N., Kambeitz, J., Haidl, T. K., Rosen, M., Chisholm, K., Riecher-Rössler, A., Egloff, L., Schmidt, A., Andreou, C., Hietala, J., Schirmer, T., Romer, G., Walger, P., Franscini, M., Traber-Walker, N., Schimmelmann, B. G., Flückiger, R., Michel, C., Rössler, W., Borisov, O., Krawitz, P. M., Heekeren, K., Buechler, R., Pantelis, C., Falkai, P., Salokangas, R. K. R., Lencer, R., Bertolino, A., Borgwardt, S., Noethen, M., Brambilla, P., Wood, S. J., Upthegrove, R., Schultze-Lutter, F., Theodoridou, A., Meisenzahl, E., & PRONIA Consortium
- Affiliations
- King's College London (GB)
- Ludwig-Maximilians-Universität München (DE)
- University of Bonn (DE)
- Max Planck Institute for Human Cognitive and Brain Sciences (DE)
- Max Planck Society (DE)
- Icahn School of Medicine at Mount Sinai (US)
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
- University of Bari Aldo Moro (IT)
- Heinrich Heine University Düsseldorf (DE)
- University of Cologne (DE)
- University of Birmingham (GB)
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Switzerland
- University of Basel (CH)
- University of Turku (FI)
- GE Healthcare GmbH (previously GE Global Research GmbH), Munich, Germany
- University of Münster (DE)
- Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, LVR Clinic Düsseldorf, Düsseldorf, Germany
- University of Zurich (CH)
- Department of Child and Adolescent Psychiatry and Psychotherapy, University of Zürich, Zürich, Switzerland
- University of Bern (CH)
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich, Switzerland
- The University of Melbourne (AU)
- Max Planck Institute of Psychiatry (DE)
- Institute of Human Genetics, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
- University of Milan (IT)
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
- Corresponding author
- Koutsouleris, Nikolaos
- ORCID
- Koutsouleris, Nikolaos
- Dwyer, Dominic B.
- Degenhardt, Franziska
- Maj, Carlo
- Sanfelici, Rachele
- Popovic, David
- Oeztuerk, Oemer
- Haas, Shalaila S.
- Weiske, Johanna
- Antonucci, Linda A.
- Neufang, Susanne
- Ruhrmann, Stephan
- Penzel, Nora
- Kambeitz, Joseph
- Rosen, Marlene
- Chisholm, Katharine
- Egloff, Laura
- Schmidt, André
- Andreou, Christina
- Hietala, Jarmo
- Walger, Petra
- Franscini, Maurizia
- Schimmelmann, Benno G.
- Flückiger, Rahel
- Michel, Chantal
- Borisov, Oleg
- Krawitz, Peter M.
- Heekeren, Karsten
- Buechler, Roman
- Pantelis, Christos
- Falkai, Peter
- Salokangas, Raimo K. R.
- Lencer, Rebekka
- Bertolino, Alessandro
- Borgwardt, Stefan
- Brambilla, Paolo
- Wood, Stephen J.
- Upthegrove, Rachel
- Theodoridou, Anastasia
- Meisenzahl, Eva
- Venue
- JAMA Psychiatry
- Publisher
- American Medical Association (AMA)
- Published
- 2021-02-01
- Volume
- 78
- Issue
- 2
- Pages
- 195
- ISSN
- 2168-622X (ISSN-L)
- PMC
- PMC7711566
- OpenAlex
- W3108636332
- Cited by
- 275
- References
- 74
- Citations by year
- 2026: 46 · 2025: 59 · 2024: 56 · 2023: 49 · 2022: 38 · 2021: 27
- FWCI
- 20.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- JAMA Psychiatry (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- UTUPub (University of Turku) (submittedVersion)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- Bern Open Repository and Information System (University of Bern) (submittedVersion)
- edoc (University of Basel) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Research Portal (King's College London) (submittedVersion, other-oa)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion)
- CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion, cc-by-nc-nd)
- UTUPub (University of Turku) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Funders
- Universität Basel
- University of Bern
- Università degli Studi di Milano
- Universität zu Lübeck
- Westfälische Wilhelms-Universität Münster
- Universita degli Studi di Bari Aldo Moro
- Turun Yliopisto
- Universität zu Köln
- Università degli Studi di Udine
- Universitas Airlangga
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Social Sciences › Psychology › Clinical Psychology › Psychopathy, Forensic Psychiatry, Sexual Offending
- Keywords
- psychosis prediction, recent-onset depression, multimodal learning, polygenic risk score, schizophrenia, structural MRI, neuropsychological performance, prognostic model, model generalization, psychosis spectrum, young adults, external validation
- MeSH terms
- Machine Learning, Adult, Depressive Disorder, Disease Susceptibility, Europe, Female, Follow-Up Studies, Humans, Longitudinal Studies, Male, Prognosis, Psychotic Disorders, Schizophrenia, Sensitivity and Specificity, Time Factors, Comorbidity, Young Adult, Workflow
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