Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression

作者：Nikolaos Koutsouleris, Lana Kambeitz‐Ilankovic, Stephan Ruhrmann, Marlene Rosen, Anne Ruef, Dominic Dwyer, Marco Paolini, Katharine Chisholm, Joseph Kambeitz, Theresa Haidl, André Schmidt, J. F. E. Gillam, Frauke Schultze‐Lutter, Peter Falkai, Maximilian F. Reiser, Anita Riecher‐Rössler, Rachel Upthegrove, Jarmo Hietala, Raimo K. R. Salokangas, Christos Pantelis, Eva Meisenzahl, Stephen J. Wood, D. Bequé, Paolo Brambilla, Stefan Borgwardt · 发表于：JAMA Psychiatry · 年份：2018 · DOI：10.1001/jamapsychiatry.2018.2165 · 被引用次数：343 · 研究领域：Schizophrenia research and treatment、Digital Mental Health Interventions、Mental Health via Writing

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...