Tae Young Lee1,2, Wu Jeong Hwang3,4, Nahrie S Kim2,3,4, Inkyung Park3,4, Silvia Kyungjin Lho1, Sun-Young Moon1, Sanghoon Oh1, Junhee Lee1, Minah Kim1, Choong-Wan Woo3,4,5, Jun Soo Kwon1,3,4. 1. Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea. 2. Department of Psychiatry, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea. 3. Department of Brain and Cognitive Neuroscience, Seoul National University College of Natural Sciences, Seoul, Republic of Korea. 4. Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea. 5. Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
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.
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.
Authors: Leigha A MacNeill; Norrina B Allen; Roshaye B Poleon; Teresa Vargas; K Juston Osborne; Katherine S F Damme; Deanna M Barch; Sheila Krogh-Jespersen; Ashley N Nielsen; Elizabeth S Norton; Christopher D Smyser; Cynthia E Rogers; Joan L Luby; Vijay A Mittal; Lauren S Wakschlag Journal: Dev Psychopathol Date: 2021-12-07
Authors: Ana Catalan; Gonzalo Salazar de Pablo; Claudia Aymerich; Stefano Damiani; Veronica Sordi; Joaquim Radua; Dominic Oliver; Philip McGuire; Anthony J Giuliano; William S Stone; Paolo Fusar-Poli Journal: JAMA Psychiatry Date: 2021-06-16 Impact factor: 25.911