PMID- 36669290
OWN - NLM
STAT- MEDLINE
DCOM- 20230228
LR  - 20230228
IS  - 1876-2026 (Electronic)
IS  - 1876-2018 (Linking)
VI  - 81
DP  - 2023 Mar
TI  - Multivariate joint models for the dynamic prediction of psychosis in individuals 
      with clinical high risk.
PG  - 103468
LID - S1876-2018(23)00022-9 [pii]
LID - 10.1016/j.ajp.2023.103468 [doi]
AB  - This study attempted to construct and validate dynamic prediction via 
      multivariate joint models and compare the prognostic performance of these models 
      to both static and univariate joint models. Individuals with clinical high 
      risk(CHR)(n = 289) were recruited and re-assessed for positive symptoms, general 
      functions, and conversion to psychosis at 2-months, 1-year, and 2-years to 
      develop the dynamic models. A multivariate joint model of positive psychotic 
      symptoms was assessed using the Structured Interview for Prodromal 
      Symptoms(SIPSp) and general function assessed by global assessment of functioning 
      scores(GAFs) with time-to-conversion to psychosis. The area under the receiver 
      operating characteristic(ROC) curve(AUC) was used to test the accuracy of the 
      models. Among 298 CHR individuals, 68 converted to psychosis within 2 years after 
      the initial assessments. Multivariate joint models showed that declining GAFs and 
      increasing SIPSp corresponded to significant and trending to significantly 
      increased risk of psychosis onset and had much higher prognostic accuracy 
      (cross-validated AUC=0.9) compared to the static model(AUC=0.6) and univariate 
      joint models(cross-validated AUC=0.6-0.8). Our results showed that multivariate 
      joint models could be highly efficient in forecasting psychosis onset for CHR 
      individuals. Longitudinal assessments for psychopathology and general functions 
      can be useful for dynamically predicting the prognosis of the pre-morbid phase of 
      psychosis.
CI  - Copyright (c) 2023 Elsevier B.V. All rights reserved.
FAU - Zhang, TianHong
AU  - Zhang T
AD  - Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 
      Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China.
FAU - Tang, XiaoChen
AU  - Tang X
AD  - Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 
      Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China.
FAU - Zhang, Yue
AU  - Zhang Y
AD  - Department of Bioinformatics and Biostatistics, School of Life Sciences and 
      Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, PR China.
FAU - Xu, LiHua
AU  - Xu L
AD  - Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 
      Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China.
FAU - Wei, YanYan
AU  - Wei Y
AD  - Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 
      Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China.
FAU - Hu, YeGang
AU  - Hu Y
AD  - Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 
      Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China.
FAU - Cui, HuiRu
AU  - Cui H
AD  - Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 
      Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China.
FAU - Tang, YingYing
AU  - Tang Y
AD  - Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 
      Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China.
FAU - Liu, HaiChun
AU  - Liu H
AD  - Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR 
      China.
FAU - Chen, Tao
AU  - Chen T
AD  - Big Data Research Lab, University of Waterloo, Ontario, Canada; Senior Research 
      Fellow, Labor and Worklife Program, Harvard University, MA, USA.
FAU - Li, ChunBo
AU  - Li C
AD  - Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 
      Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China.
FAU - Wang, JiJun
AU  - Wang J
AD  - Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 
      Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China; CAS 
      Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), 
      Chinese Academy of Science, Shanghai, PR China; Institute of Psychology and 
      Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China. Electronic 
      address: jijunwang27@163.com.
LA  - eng
PT  - Journal Article
DEP - 20230118
PL  - Netherlands
TA  - Asian J Psychiatr
JT  - Asian journal of psychiatry
JID - 101517820
SB  - IM
MH  - Humans
MH  - *Psychotic Disorders/diagnosis
MH  - Prognosis
MH  - Prodromal Symptoms
MH  - Psychopathology
OTO - NOTNLM
OT  - Dynamic prediction
OT  - Joint model
OT  - Prodromal psychosis
OT  - Transition
OT  - Ultra high risk
COIS- Conflicting of interests There are no conflicts of interest to report.
EDAT- 2023/01/21 06:00
MHDA- 2023/03/03 06:00
CRDT- 2023/01/20 18:08
PHST- 2022/08/06 00:00 [received]
PHST- 2023/01/03 00:00 [revised]
PHST- 2023/01/16 00:00 [accepted]
PHST- 2023/01/21 06:00 [pubmed]
PHST- 2023/03/03 06:00 [medline]
PHST- 2023/01/20 18:08 [entrez]
AID - S1876-2018(23)00022-9 [pii]
AID - 10.1016/j.ajp.2023.103468 [doi]
PST - ppublish
SO  - Asian J Psychiatr. 2023 Mar;81:103468. doi: 10.1016/j.ajp.2023.103468. Epub 2023 
      Jan 18.