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  Towards clinical application of prediction models for transition to psychosis: A systematic review and external validation study in the PRONIA sample

Rosen, M., Betz, L. T., Schultze-Lutter, F., Chisholm, K., Haidl, T. K., Kambeitz-Ilankovic, L., et al. (2021). Towards clinical application of prediction models for transition to psychosis: A systematic review and external validation study in the PRONIA sample. NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS, 125, 478-492. doi:10.1016/j.neubiorev.2021.02.032.

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 Creators:
Rosen, Marlene, Author
Betz, Linda T., Author
Schultze-Lutter, Frauke, Author
Chisholm, Katharine, Author
Haidl, Theresa K., Author
Kambeitz-Ilankovic, Lana, Author
Bertolino, Alessandro, Author
Borgwardt, Stefan, Author
Brambilla, Paolo, Author
Lencer, Rebekka, Author
Meisenzahl, Eva, Author
Ruhrmann, Stephan, Author
Salokangas, Raimo K. R., Author
Upthegrove, Rachel, Author
Wood, Stephen J., Author
Koutsouleris, Nikolaos1, Author           
Kambeitz, Joseph, Author
Affiliations:
1Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society, ou_3318615              

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 Abstract: A multitude of prediction models for a first psychotic episode in individuals at clinical high-risk (CHR) for psychosis have been proposed, but only rarely validated. We identified transition models based on clinical and neuropsychological data through a registered systematic literature search and evaluated their external validity in 173 CHRs from the Personalised Prognostic Tools for Early Psychosis Management (PRONIA) study. Discrimination performance was assessed with the area under the receiver operating characteristic curve (AUC), and compared to the prediction of clinical raters. External discrimination performance varied considerably across the 22 identified models (AUC 0.40-0.76), with two models showing good discrimination performance. None of the tested models significantly outperformed clinical raters (AUC = 0.75). Combining predictions of clinical raters and the best model descriptively improved discrimination performance (AUC = 0.84). Results show that personalized prediction of transition in CHR is potentially feasible on a global scale. For implementation in clinical practice, further rounds of external validation, impact studies, and development of an ethical framework is necessary.

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 Dates: 2021
 Publication Status: Issued
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Title: NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS
Source Genre: Journal
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Pages: - Volume / Issue: 125 Sequence Number: - Start / End Page: 478 - 492 Identifier: ISSN: 0149-7634