作者
Nikolaos Koutsouleris, Dominic B Dwyer, Franziska Degenhardt, Carlo Maj, Maria Fernanda Urquijo-Castro, Rachele Sanfelici, David Popovic, Oemer Oeztuerk, Shalaila S Haas, Johanna Weiske, Anne Ruef, Lana Kambeitz-Ilankovic, Linda A Antonucci, Susanne Neufang, Christian Schmidt-Kraepelin, Stephan Ruhrmann, Nora Penzel, Joseph Kambeitz, Theresa K Haidl, Marlene Rosen, Katharine Chisholm, Anita Riecher-Rössler, Laura Egloff, André Schmidt, Christina Andreou, Jarmo Hietala, Timo Schirmer, Georg Romer, Petra Walger, Maurizia Franscini, Nina Traber-Walker, Benno G Schimmelmann, Rahel Flückiger, Chantal Michel, Wulf Rössler, Oleg Borisov, Peter M Krawitz, Karsten Heekeren, Roman Buechler, Christos Pantelis, Peter Falkai, Raimo KR Salokangas, Rebekka Lencer, Alessandro Bertolino, Stefan Borgwardt, Markus Noethen, Paolo Brambilla, Stephen J Wood, Rachel Upthegrove, Frauke Schultze-Lutter, Anastasia Theodoridou, Eva Meisenzahl, PRONIA Consortium
发表日期
2021/2/1
期刊
JAMA psychiatry
卷号
78
期号
2
页码范围
195-209
出版商
American Medical Association
简介
Importance
Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear.
Objectives
To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models’ geographic generalizability; to test and integrate clinicians’ predictions; and to maximize clinical utility by building a sequential prognostic system.
Design, Setting, and Participants
This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 …
引用总数