Nikolaos Koutsouleris1, Michelle Worthington2, Dominic B Dwyer3, Lana Kambeitz-Ilankovic4, Rachele Sanfelici3, Paolo Fusar-Poli5, Marlene Rosen6, Stephan Ruhrmann6, Alan Anticevic2, Jean Addington7, Diana O Perkins8, Carrie E Bearden9, Barbara A Cornblatt10, Kristin S Cadenhead11, Daniel H Mathalon12, Thomas McGlashan13, Larry Seidman14, Ming Tsuang11, Elaine F Walker15, Scott W Woods13, Peter Falkai3, Rebekka Lencer16, Alessandro Bertolino17, Joseph Kambeitz6, Frauke Schultze-Lutter18, Eva Meisenzahl18, Raimo K R Salokangas19, Jarmo Hietala19, Paolo Brambilla20, Rachel Upthegrove21, Stefan Borgwardt22, Stephen Wood23, Raquel E Gur24, Philip McGuire25, Tyrone D Cannon26. 1. Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Max-Planck Institute of Psychiatry, Munich, Germany; Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom. Electronic address: nikolaos.koutsouleris@med.uni-muenchen.de. 2. Department of Psychology, Yale University, New Haven, Connecticut. 3. Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany. 4. Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany. 5. Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy; Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom. 6. Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany. 7. Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada. 8. Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina. 9. Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California. 10. Zucker Hillside Hospital, Northwell Health, Queens, New York. 11. University of California San Diego, San Diego, California. 12. Department of Psychiatry, University of California San Francisco, San Francisco, California; San Francisco VA Medical Center, San Francisco, California. 13. Department of Psychiatry, Yale University, New Haven, Connecticut. 14. Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, Massachusetts. 15. Department of Psychology and Psychiatry, Emory University, Atlanta, Georgia. 16. Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany; Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany. 17. Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy. 18. Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Germany. 19. Department of Psychiatry, University of Turku, Turku, Finland. 20. Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy. 21. Institute of Mental Health, University of Birmingham, Birmingham, United Kingdom; School of Psychology, University of Birmingham, Birmingham, United Kingdom. 22. Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany; Department of Psychiatry (Psychiatric University Hospital, UPK), University of Basel, Basel, Switzerland. 23. Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Victoria, Australia. 24. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. 25. Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom. 26. Department of Psychology, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut.
Abstract
BACKGROUND: Transition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes. METHODS: We validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation. RESULTS: After calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA-CHR|ROD and validation in NAPLS-2-UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts. CONCLUSIONS: Clinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.
BACKGROUND: Transition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes. METHODS: We validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation. RESULTS: After calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA-CHR|ROD and validation in NAPLS-2-UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts. CONCLUSIONS: Clinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.
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