Universität zu Köln

Koutsouleris, Nikolaos, Kambeitz-Ilankovic, Lana, Ruhrmann, Stephan ORCID: 0000-0002-6022-2364, Rosen, Marlene, Ruef, Anne, Dwyer, Dominic B., Paolini, Marco, Chisholm, Katharine ORCID: 0000-0002-0575-0789, Kambeitz, Joseph, Haidl, Theresa, Schmidt, Andre ORCID: 0000-0001-6055-8397, Gillam, John, Schultze-Lutter, Frauke, Falkai, Peter ORCID: 0000-0003-2873-8667, Reiser, Maximilian, Riecher-Rossler, Anita, Upthegrove, Rachel ORCID: 0000-0001-8204-5103, Hietala, Jarmo ORCID: 0000-0002-3179-6780, Salokangas, Raimo K. R., Pantelis, Christos ORCID: 0000-0002-9565-0238, Meisenzahl, Eva, Wood, Stephen J., Beque, Dirk, Brambilla, Paolo and Borgwardt, Stefan ORCID: 0000-0002-5792-3987 (2018). Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression A Multimodal, Multisite Machine Learning Analysis. JAMA Psychiatry, 75 (11). S. 1156 - 1173. CHICAGO: AMER MEDICAL ASSOC. ISSN 2168-6238

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Abstract

IMPORTANCE Social and occupational impairments contribute to the burden of psychosis and depression. There is a need for risk stratification tools to inform personalized functional-disability preventive strategies for individuals in at-risk and early phases of these illnesses. OBJECTIVE To determine whether predictors associated with social and role functioning can be identified in patients in clinical high-risk (CHR) states for psychosis or with recent-onset depression (ROD) using clinical, imaging-based, and combined machine learning; assess the geographic, transdiagnostic, and prognostic generalizability of machine learning and compare it with human prognostication; and explore sequential prognosis encompassing clinical and combined machine learning. DESIGN, SETTING, AND PARTICIPANTS This multisite naturalistic study followed up patients in CHR states, with ROD, and with recent-onset psychosis, and healthy control participants for 18 months in 7 academic early-recognition services in 5 European countries. Participants were recruited between February 2014 and May 2016, and data were analyzed from April 2017 to January 2018. AIN OUTCOMES AND MEASURES Performance and generalizability of prognostic models. RESULTS A total of 116 individuals in CHR states (mean [SD] age, 24.0 [5.1] years; 58 [50.0%] female) and 120 patients with ROD (mean [SD] age, 26.1 [6.1] years; 65 [54.2%] female) were followed up for a mean (SD) of 329 (142) days. Machine learning predicted the 1-year social-functioning outcomes with a balanced accuracy of 76.9% of patients in CHR states and 66.2% of patients with ROD using clinical baseline data. Balanced accuracy in models using structural neuroimaging was 76.2% in patients in CHR states and 65.0% in patients with ROD, and in combined models, it was 82.7% for CHR states and 70.3% for ROD. Lower functioning before study entry was a transdiagnostic predictor. Medial prefrontal and temporo-parieto-occipital gray matter volume (GMV) reductions and cerebellar and dorsolateral prefrontal GMV increments had predictive value in the CHR group; reduced mediotemporal and increased prefrontal-perisylvian GMV had predictive value in patients with ROD. Poor prognoses were associated with increased risk of psychotic, depressive, and anxiety disorders at follow-up in patients in the CHR state but not ones with ROD. Machine learning outperformed expert prognostication. Adding neuroimaging machine learning to clinical machine learning provided a 1.9-fold increase of prognostic certainty in uncertain cases of patients in CHR states, and a 10.5-fold increase of prognostic certainty for patients with ROD. CONCLUSIONS AND RELEVANCE Precision medicine tools could augment effective therapeutic strategies aiming at the prevention of social functioning impairments in patients with CHR states or with ROD.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Koutsouleris, Nikolaos UNSPECIFIED UNSPECIFIED UNSPECIFIED Kambeitz-Ilankovic, Lana UNSPECIFIED UNSPECIFIED UNSPECIFIED Ruhrmann, Stephan UNSPECIFIED orcid.org/0000-0002-6022-2364 UNSPECIFIED Rosen, Marlene UNSPECIFIED UNSPECIFIED UNSPECIFIED Ruef, Anne UNSPECIFIED UNSPECIFIED UNSPECIFIED Dwyer, Dominic B. UNSPECIFIED UNSPECIFIED UNSPECIFIED Paolini, Marco UNSPECIFIED UNSPECIFIED UNSPECIFIED Chisholm, Katharine UNSPECIFIED orcid.org/0000-0002-0575-0789 UNSPECIFIED Kambeitz, Joseph UNSPECIFIED UNSPECIFIED UNSPECIFIED Haidl, Theresa UNSPECIFIED UNSPECIFIED UNSPECIFIED Schmidt, Andre UNSPECIFIED orcid.org/0000-0001-6055-8397 UNSPECIFIED Gillam, John UNSPECIFIED UNSPECIFIED UNSPECIFIED Schultze-Lutter, Frauke UNSPECIFIED UNSPECIFIED UNSPECIFIED Falkai, Peter UNSPECIFIED orcid.org/0000-0003-2873-8667 UNSPECIFIED Reiser, Maximilian UNSPECIFIED UNSPECIFIED UNSPECIFIED Riecher-Rossler, Anita UNSPECIFIED UNSPECIFIED UNSPECIFIED Upthegrove, Rachel UNSPECIFIED orcid.org/0000-0001-8204-5103 UNSPECIFIED Hietala, Jarmo UNSPECIFIED orcid.org/0000-0002-3179-6780 UNSPECIFIED Salokangas, Raimo K. R. UNSPECIFIED UNSPECIFIED UNSPECIFIED Pantelis, Christos UNSPECIFIED orcid.org/0000-0002-9565-0238 UNSPECIFIED Meisenzahl, Eva UNSPECIFIED UNSPECIFIED UNSPECIFIED Wood, Stephen J. UNSPECIFIED UNSPECIFIED UNSPECIFIED Beque, Dirk UNSPECIFIED UNSPECIFIED UNSPECIFIED Brambilla, Paolo UNSPECIFIED UNSPECIFIED UNSPECIFIED Borgwardt, Stefan UNSPECIFIED orcid.org/0000-0002-5792-3987 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-166142
DOI:	10.1001/jamapsychiatry.2018.2165
Journal or Publication Title:	JAMA Psychiatry
Volume:	75
Number:	11
Page Range:	S. 1156 - 1173
Date:	2018
Publisher:	AMER MEDICAL ASSOC
Place of Publication:	CHICAGO
ISSN:	2168-6238
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language COGNITIVE ENHANCEMENT THERAPY; ULTRA-HIGH-RISK; GRAY-MATTER LOSS; 1ST-EPISODE PSYCHOSIS; EARLY INTERVENTION; DEFAULT MODE; SCHIZOPHRENIA; CONNECTIVITY; BIOMARKERS; DISORDERS Multiple languages Psychiatry Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/16614