B1 Non-refereed article in a scientific journal
Detailed clinical phenotyping and generalisability in prognostic models of functioning in at-risk populations
Authors: Rosen Marlene, Betz Linda T, Kaiser Natalie, Penzel Nora, Dwyer Dominic, Lichtenstein Theresa K, Schultze-Lutter Frauke, Kambeitz-Ilankovic Lana, Bertolino Alessandro, Borgwardt Stefan, Brambilla Paolo, Lencer Rebekka, Meisenzahl Eva, Pantelis Christos, Salokangas Raimo KR, Upthegrove Rachel, Wood Stephen, Ruhrmann Stephan, Koutsouleris Nikolaos, Kambeitz Joseph; and the PRONIA consortium
Publisher: CAMBRIDGE UNIV PRESS
Publication year: 2022
Journal: British Journal of Psychiatry
Journal name in source: BRITISH JOURNAL OF PSYCHIATRY
Journal acronym: BRIT J PSYCHIAT
Article number: PII S0007125021001410
Volume: 220
Issue: 6
First page : 318
Last page: 321
Number of pages: 4
ISSN: 0007-1250
eISSN: 1472-1465
DOI: https://doi.org/10.1192/bjp.2021.141
Web address : https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/detailed-clinical-phenotyping-and-generalisability-in-prognostic-models-of-functioning-in-atrisk-populations/EBE4336659A5057DA04182AECBF48C1F
Personalised prediction of functional outcomes is a promising approach for targeted early intervention in psychiatry. However, generalisability and resource efficiency of such prognostic models represent challenges. In the PRONIA study (German Clinical Trials Register: DRKS00005042), we demonstrate excellent generalisability of prognostic models in individuals at clinical high-risk for psychosis or with recent-onset depression, and substantial contributions of detailed clinical phenotyping, particularly to the prediction of role functioning. These results indicate that it is possible that functioning prediction models based only on clinical data could be effectively applied in diverse healthcare settings, so that neuroimaging data may not be needed at early assessment stages.
