4.6 Article

Individualized Prediction of Prodromal Symptom Remission for Youth at Clinical High Risk for Psychosis

期刊

SCHIZOPHRENIA BULLETIN
卷 48, 期 2, 页码 395-404

出版社

OXFORD UNIV PRESS
DOI: 10.1093/schbul/sbab115

关键词

remission; clinical high risk; schizophrenia; psychosis; risk prediction; machine learning

资金

  1. National Institute of Mental Health [U01MH081984, U01MH081928, U01MH081944, U01MH081902, U01MH082004, U01MH081988, U01MH082022, U01MH076989, U01MH081857]

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The study focuses on individuals in the clinical high-risk period before first episode of psychosis (CHR-P) who do not transition to psychosis, and aims to develop a predictive model for remission outcomes. Using a data-driven machine-learning approach, the researchers identified clinical and demographic predictors of symptomatic remission in CHR-P individuals. The study found that individuals who eventually experienced remission had lower baseline prodromal symptoms. This study highlights the importance of understanding factors contributing to resilience and recovery in CHR-P individuals.
The clinical high-risk period before a first episode of psychosis (CHR-P) has been widely studied with the goal of understanding the development of psychosis; however, less attention has been paid to the 75%-80% of CHR-P individuals who do not transition to psychosis. It is an open question whether multivariable models could be developed to predict remission outcomes at the same level of performance and generalizability as those that predict conversion to psychosis. Participants were drawn from the North American Prodrome Longitudinal Study (NAPLS3). An empirically derived set of clinical and demographic predictor variables were selected with elastic net regularization and were included in a gradient boosting machine algorithm to predict prodromal symptom remission. The predictive model was tested in a comparably sized independent sample (NAPLS2). The classification algorithm developed in NAPLS3 achieved an area under the curve of 0.66 (0.60-0.72) with a sensitivity of 0.68 and specificity of 0.53 when tested in an independent external sample (NAPLS2). Overall, future remitters had lower baseline prodromal symptoms than nonremitters. This study is the first to use a data-driven machine-learning approach to assess clinical and demographic predictors of symptomatic remission in individuals who do not convert to psychosis. The predictive power of the models in this study suggest that remission represents a unique clinical phenomenon. Further study is warranted to best understand factors contributing to resilience and recovery from the CHR-P state.

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