Combining MRI and clinical data to detect high relapse risk after the first episode of psychosis

Solanes, Aleix; Mezquida, Gisela; Janssen, Joost; Amoretti, Silvia; Lobo, Antonio; González-Pinto, Ana; Arango, Celso; Vieta, Eduard; Castro-Fornieles, Josefina; Bergé, Daniel; Albacete, Auria; Giné, Eloi; Parellada, Mara; Bernardo, Miquel; PEPs Group; Pomarol-Clotet, Edith; Radua, Joaquim

Combining MRI and clinical data to detect high relapse risk after the first episode of psychosis

Mostra el registre complet Registre parcial de l'ítem

dc.contributor.author Solanes, Aleix
dc.contributor.author Mezquida, Gisela
dc.contributor.author Janssen, Joost
dc.contributor.author Amoretti, Silvia
dc.contributor.author Lobo, Antonio
dc.contributor.author González-Pinto, Ana
dc.contributor.author Arango, Celso
dc.contributor.author Vieta, Eduard
dc.contributor.author Castro-Fornieles, Josefina
dc.contributor.author Bergé, Daniel
dc.contributor.author Albacete, Auria
dc.contributor.author Giné, Eloi
dc.contributor.author Parellada, Mara
dc.contributor.author Bernardo, Miquel
dc.contributor.author PEPs Group
dc.contributor.author Pomarol-Clotet, Edith
dc.contributor.author Radua, Joaquim
dc.date.accessioned 2023-05-10T06:17:40Z
dc.date.available 2023-05-10T06:17:40Z
dc.date.issued 2022
dc.description.abstract Detecting patients at high relapse risk after the first episode of psychosis (HRR-FEP) could help the clinician adjust the preventive treatment. To develop a tool to detect patients at HRR using their baseline clinical and structural MRI, we followed 227 patients with FEP for 18-24 months and applied MRIPredict. We previously optimized the MRI-based machine-learning parameters (combining unmodulated and modulated gray and white matter and using voxel-based ensemble) in two independent datasets. Patients estimated to be at HRR-FEP showed a substantially increased risk of relapse (hazard ratio = 4.58, P < 0.05). Accuracy was poorer when we only used clinical or MRI data. We thus show the potential of combining clinical and MRI data to detect which individuals are more likely to relapse, who may benefit from increased frequency of visits, and which are unlikely, who may be currently receiving unnecessary prophylactic treatments. We also provide an updated version of the MRIPredict software.
dc.description.sponsorship We are grateful to all participants. We would also like to thank the Instituto de Salud Carlos III, the Spanish Ministry of Science, Innovation, and Universities, the European Regional Development Fund (ERDF/FEDER), European Social Fund, “Investing in your future”, “A way of making Europe” (projects: PI08/0208, PI11/00325, PI14/00292, PI14/00612, PI14/01148, PI14/01151, PI17/00481, PI17/01997, PI18/01055, PI19/00394, PI19/00766, PI20/00721, PI20/01342, and PI21/00713; contracts: CP14/00041, JR19/00024, CPII19/00009, and CD20/00177); CERCA Program; Catalan Government, the Secretariat of Universities and Research of the Department of Enterprise and Knowledge (2017SGR01271, 2017SGR1355, and 2017SGR1365); Institut de Neurociències, Universitat de Barcelona; Madrid Regional Government (B2017/BMD-3740 AGES-CM-2); the University of the Basque Country (2019 321218ELCY GIC18/107); the Basque Government (2017111104); European Union Structural Funds, European Union Seventh Framework Program, European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement No 115916, Project PRISM, and grant agreement No 777394, Project AIMS-2-TRIALS), Fundación Familia Alonso, Fundación Alicia Koplowitz and Fundación Mutua Madrileña. The funding organizations played no role in the study design, data collection, analysis, or manuscript approval.
dc.format.mimetype application/pdf
dc.identifier.citation Solanes A, Mezquida G, Janssen J, Amoretti S, Lobo A, González-Pinto A, Arango C, Vieta E, Castro-Fornieles J, Bergé D, Albacete A, Giné E, Parellada M, Bernardo M; PEPs group (collaborators); Pomarol-Clotet E, Radua J. Combining MRI and clinical data to detect high relapse risk after the first episode of psychosis. Schizophrenia (Heidelb). 2022 Nov 17;8(1):100. DOI: 10.1038/s41537-022-00309-w
dc.identifier.doi http://dx.doi.org/10.1038/s41537-022-00309-w
dc.identifier.issn 2754-6993
dc.identifier.uri http://hdl.handle.net/10230/56743
dc.language.iso eng
dc.publisher Nature Research
dc.relation.ispartof Schizophrenia (Heidelb). 2022 Nov 17;8(1):100
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/777394
dc.rights © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject.keyword Biomarkers
dc.subject.keyword Psychosis
dc.subject.keyword Schizophrenia
dc.title Combining MRI and clinical data to detect high relapse risk after the first episode of psychosis
dc.type info:eu-repo/semantics/article
dc.type.version info:eu-repo/semantics/publishedVersion

Col·leccions

Articles (Hospital del Mar Research Institute)
Documents OpenAIRE (Open Access Infrastructure for Research in Europe)