Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

Koutsouleris, Nikolaos and Dwyer, Dominic B. and Degenhardt, Franziska and Maj, Carlo and Urquijo-Castro, Maria Fernanda and Sanfelici, Rachele and Popovic, David and Oeztuerk, Oemer and Haas, Shalaila S. and Weiske, Johanna and Ruef, Anne and Kambeitz-Ilankovic, Lana and Antonucci, Linda A. and Neufang, Susanne and Schmidt-Kraepelin, Christian and Ruhrmann, Stephan and Penzel, Nora and Kambeitz, Joseph and Haidl, Theresa K. and Rosen, Marlene and Chisholm, Katharine and Riecher-Rössler, Anita and Egloff, Laura and Schmidt, André and Andreou, Christina and Hietala, Jarmo and Schirmer, Timo and Romer, Georg and Walger, Petra and Franscini, Maurizia and Traber-Walker, Nina and Schimmelmann, Benno G. and Flückiger, Rahel and Michel, Chantal and Rössler, Wulf and Borisov, Oleg and Krawitz, Peter M. and Heekeren, Karsten and Buechler, Roman and Pantelis, Christos and Falkai, Peter and Salokangas, Raimo K. R. and Lencer, Rebekka and Bertolino, Alessandro and Borgwardt, Stefan and Noethen, Markus and Brambilla, Paolo and Wood, Stephen J. and Upthegrove, Rachel and Schultze-Lutter, Frauke and Theodoridou, Anastasia and Meisenzahl, Eva. (2021) Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression. JAMA Psychiatry, 78 (2). pp. 195-209.

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Official URL: https://edoc.unibas.ch/79800/

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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.; 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.; This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020.; Accuracy and generalizability of prognostic systems.; A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results.; These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.
Faculties and Departments:03 Faculty of Medicine > Bereich Psychiatrie (Klinik) > Erwachsenenpsychiatrie UPK
03 Faculty of Medicine > Departement Klinische Forschung > Bereich Psychiatrie (Klinik) > Erwachsenenpsychiatrie UPK
UniBasel Contributors:Schmidt, André and Andreou, Christina
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:American Medical Association
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:22 Mar 2021 10:43
Deposited On:22 Mar 2021 10:43

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