Detailed clinical phenotyping and generalisability in prognostic models of functioning in at-risk populations

Marlene Rosen; Linda T. Betz; Natalie Kaiser; Nora Penzel; Dominic Dwyer; Theresa K. Lichtenstein; Frauke Schultze-Lutter; Lana Kambeitz-Ilankovic; Alessandro Bertolino; Stefan Borgwardt; Paolo Brambilla; Rebekka Lencer; Eva Meisenzahl; Christos Pantelis; Raimo K.R. Salokangas; Rachel Upthegrove; Stephen Wood; Stephan Ruhrmann; Nikolaos Koutsouleris; Joseph Kambeitz

doi:10.1192/bjp.2021.141

Detailed clinical phenotyping and generalisability in prognostic models of functioning in at-risk populations

Marlene Rosen, Linda T. Betz, Natalie Kaiser, Nora Penzel, Dominic Dwyer, Theresa K. Lichtenstein, Frauke Schultze-Lutter, Lana Kambeitz-Ilankovic, Alessandro Bertolino, Stefan Borgwardt, Paolo Brambilla, Rebekka Lencer, Eva Meisenzahl, Christos Pantelis, Raimo K.R. Salokangas, Rachel Upthegrove, Stephen Wood, Stephan Ruhrmann, Nikolaos Koutsouleris, Joseph Kambeitz^*

^*Corresponding author for this work

Clinic of Psychiatry and Psychotherapy

Abstract

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.

Original language	English
Journal	British Journal of Psychiatry
Volume	220
Issue number	6
Pages (from-to)	318-321
Number of pages	4
ISSN	0007-1250
DOIs	https://doi.org/10.1192/bjp.2021.141
Publication status	Published - 16.06.2022

Funding

PRONIA is a Collaboration Project funded by the European Union under the 7th Framework Programme (grant agreement no. 602152). M.R. is supported by a grant from the Koeln Fortune Program/Faculty of Medicine, University of Cologne. R.U. reports grants from the Medical Research Council, grants from the National Institute for Health Research, and personal fees from Sunovion, outside the submitted work. J.K. and N.Ko. received honoraria for talks presented at education meetings organised by Otsuka/Lundbeck. C.P. was supported by a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellowship (1105825) and an NHMRC L3 Investigator Grant (1196508), has received honoraria for talks at educational meetings and has served on an advisory board for Lundbeck, Australia Pty Ltd.

Research Areas and Centers

Academic Focus: Center for Brain, Behavior and Metabolism (CBBM)

DFG Research Classification Scheme

2.23-10 Clinical Psychiatry, Psychotherapy, Child and Adolescent Psychiatry

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1192/bjp.2021.141

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Rosen, M., Betz, L. T., Kaiser, N., Penzel, N., Dwyer, D., Lichtenstein, T. K., Schultze-Lutter, F., Kambeitz-Ilankovic, L., Bertolino, A., Borgwardt, S., Brambilla, P., Lencer, R., Meisenzahl, E., Pantelis, C., Salokangas, R. K. R., Upthegrove, R., Wood, S., Ruhrmann, S., Koutsouleris, N., & Kambeitz, J. (2022). Detailed clinical phenotyping and generalisability in prognostic models of functioning in at-risk populations. British Journal of Psychiatry, 220(6), 318-321. https://doi.org/10.1192/bjp.2021.141

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title = "Detailed clinical phenotyping and generalisability in prognostic models of functioning in at-risk populations",

abstract = "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.",

author = "Marlene Rosen and Betz, \{Linda T.\} and Natalie Kaiser and Nora Penzel and Dominic Dwyer and Lichtenstein, \{Theresa K.\} and Frauke Schultze-Lutter and Lana Kambeitz-Ilankovic and Alessandro Bertolino and Stefan Borgwardt and Paolo Brambilla and Rebekka Lencer and Eva Meisenzahl and Christos Pantelis and Salokangas, \{Raimo K.R.\} and Rachel Upthegrove and Stephen Wood and Stephan Ruhrmann and Nikolaos Koutsouleris and Joseph Kambeitz",

note = "Publisher Copyright: Copyright {\textcopyright} The Author(s), 2021. Published by Cambridge University Press on behalf of the Royal College of Psychiatrists.",

year = "2022",

month = jun,

day = "16",

doi = "10.1192/bjp.2021.141",

language = "English",

volume = "220",

pages = "318--321",

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Rosen, M, Betz, LT, Kaiser, N, Penzel, N, Dwyer, D, Lichtenstein, TK, Schultze-Lutter, F, Kambeitz-Ilankovic, L, Bertolino, A, Borgwardt, S, Brambilla, P, Lencer, R, Meisenzahl, E, Pantelis, C, Salokangas, RKR, Upthegrove, R, Wood, S, Ruhrmann, S, Koutsouleris, N & Kambeitz, J 2022, 'Detailed clinical phenotyping and generalisability in prognostic models of functioning in at-risk populations', British Journal of Psychiatry, vol. 220, no. 6, pp. 318-321. https://doi.org/10.1192/bjp.2021.141

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T1 - Detailed clinical phenotyping and generalisability in prognostic models of functioning in at-risk populations

AU - Rosen, Marlene

AU - Betz, Linda T.

AU - Kaiser, Natalie

AU - Penzel, Nora

AU - Dwyer, Dominic

AU - Lichtenstein, Theresa K.

AU - Schultze-Lutter, Frauke

AU - Kambeitz-Ilankovic, Lana

AU - Bertolino, Alessandro

AU - Borgwardt, Stefan

AU - Brambilla, Paolo

AU - Lencer, Rebekka

AU - Meisenzahl, Eva

AU - Pantelis, Christos

AU - Salokangas, Raimo K.R.

AU - Upthegrove, Rachel

AU - Wood, Stephen

AU - Ruhrmann, Stephan

AU - Koutsouleris, Nikolaos

AU - Kambeitz, Joseph

PY - 2022/6/16

Y1 - 2022/6/16

N2 - 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.

AB - 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.

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JO - British Journal of Psychiatry

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ER -

Detailed clinical phenotyping and generalisability in prognostic models of functioning in at-risk populations

Abstract

Funding

Research Areas and Centers

DFG Research Classification Scheme

UN SDGs

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