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Prediction of psychosis: model development and internal validation of a personalized risk calculator

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Issue Date
2022-10
Publisher
Cambridge University Press
Citation
Psychological Medicine, Vol.52 No.13, pp.2632-2640
Abstract
Background Over the past two decades, early detection and early intervention in psychosis have become essential goals of psychiatry. However, clinical impressions are insufficient for predicting psychosis outcomes in clinical high-risk (CHR) individuals; a more rigorous and objective model is needed. This study aims to develop and internally validate a model for predicting the transition to psychosis within 10 years. Methods Two hundred and eight help-seeking individuals who fulfilled the CHR criteria were enrolled from the prospective, naturalistic cohort program for CHR at the Seoul Youth Clinic (SYC). The least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was used to develop a predictive model for a psychotic transition. We performed k-means clustering and survival analysis to stratify the risk of psychosis. Results The predictive model, which includes clinical and cognitive variables, identified the following six baseline variables as important predictors: 1-year percentage decrease in the Global Assessment of Functioning score, IQ, California Verbal Learning Test score, Strange Stories test score, and scores in two domains of the Social Functioning Scale. The predictive model showed a cross-validated Harrell's C-index of 0.78 and identified three subclusters with significantly different risk levels. Conclusions Overall, our predictive model showed a predictive ability and could facilitate a personalized therapeutic approach to different risks in high-risk individuals.
ISSN
0033-2917
URI
https://hdl.handle.net/10371/188998
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College of Natural Sciences (자연과학대학)Brain and Cognitive Sciences (뇌인지과학과)Journal Papers (저널논문_뇌인지과학과)
College of Natural Sciences (자연과학대학)Brain and Cognitive Sciences (뇌인지과학과)Journal Papers (저널논문_뇌인지과학과)
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