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

Tae Young Lee, Wu Jeong Hwang, Nahrie S. Kim, Inkyung Park, Silvia Kyungjin Lho, Sun Young Moon, Sanghoon Oh, Junhee Lee, Minah Kim, Choong Wan Woo, Jun Soo Kwon

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish
Pages (from-to)2632-2640
Number of pages9
JournalPsychological medicine
Issue number13
StatePublished - 14 Oct 2022


  • Clinical high-risk (CHR)
  • personalized medicine
  • prediction
  • psychosis
  • transition


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