PMID- 23830855
OWN - NLM
STAT- MEDLINE
DCOM- 20140311
LR  - 20220409
IS  - 1573-2509 (Electronic)
IS  - 0920-9964 (Linking)
VI  - 149
IP  - 1-3
DP  - 2013 Sep
TI  - Neurocognition in clinical high risk young adults who did or did not convert to a 
      first schizophrenic psychosis: a meta-analysis.
PG  - 48-55
LID - S0920-9964(13)00317-4 [pii]
LID - 10.1016/j.schres.2013.06.017 [doi]
AB  - BACKGROUND: Individuals at clinical high risk (CHR) for psychosis have become a 
      major focus for research designed to explore early predictors of transition to 
      full psychosis. Characterizing differences in neurocognitive (NC) functioning 
      between psychosis converters (CHR-C) and non-converters (CHR-NC) might contribute 
      to the identification of specific NC predictors of psychosis onset. Therefore, 
      the aim of the present meta-analysis was to compare the baseline NC performance 
      between CHR-C and CHR-NC. METHOD: PubMed (MEDLINE), Web of Science, Embase and 
      reference lists were searched for studies reporting baseline cognitive data of 
      CHR-C and CHR-NC. Included NC tests were classified within the MATRICS - 
      Measurement and Treatment Research to Improve Cognition in Schizophrenia - 
      cognitive domains. RESULTS: Of 95 studies assessed for eligibility, 9 studies 
      comprising 583 CHR subjects (N CHR-C=195, N CHR-NC=388) met all the inclusion 
      criteria. CHR-C performed significantly worse compared to CHR-NC on 2 MATRICS 
      domains namely working memory (ES=-0.29, 95% CI=-0.53 to -0.05) and visual 
      learning (ES=-0.40, 95% CI=-0.68 to -0.13). For the remaining 4 domains 
      (processing speed, attention/vigilance, verbal learning, reasoning/problem 
      solving) no significant differences between CHR-C and CHR-NC were observed. 
      CONCLUSION: Based on the current meta-analytic data we might conclude that it is 
      possible to differentiate between CHR-C and CHR-NC with respect to working memory 
      and visual learning. The addition of visual learning and working memory tasks to 
      psychosis regression models might contribute to the predictive power of these 
      models.
CI  - Copyright (c) 2013 Elsevier B.V. All rights reserved.
FAU - De Herdt, Amber
AU  - De Herdt A
AD  - Department of Rehabilitation Science, KU Leuven, Belgium. 
      amber.deherdt@faber.kuleuven.be
FAU - Wampers, Martien
AU  - Wampers M
FAU - Vancampfort, Davy
AU  - Vancampfort D
FAU - De Hert, Marc
AU  - De Hert M
FAU - Vanhees, Luc
AU  - Vanhees L
FAU - Demunter, Hella
AU  - Demunter H
FAU - Van Bouwel, Ludwina
AU  - Van Bouwel L
FAU - Brunner, Emanuel
AU  - Brunner E
FAU - Probst, Michel
AU  - Probst M
LA  - eng
PT  - Journal Article
PT  - Meta-Analysis
PT  - Research Support, Non-U.S. Gov't
PT  - Review
DEP - 20130705
PL  - Netherlands
TA  - Schizophr Res
JT  - Schizophrenia research
JID - 8804207
SB  - IM
MH  - Cognition Disorders/etiology
MH  - Databases, Factual/statistics & numerical data
MH  - Disease Progression
MH  - Female
MH  - Humans
MH  - Male
MH  - Psychotic Disorders/complications/*diagnosis/psychology
MH  - Schizophrenia/complications/*diagnosis
MH  - *Schizophrenic Psychology
OTO - NOTNLM
OT  - At-risk-mental state
OT  - Cognition
OT  - First-episode
OT  - Schizophrenia
EDAT- 2013/07/09 06:00
MHDA- 2014/03/13 06:00
CRDT- 2013/07/09 06:00
PHST- 2012/12/31 00:00 [received]
PHST- 2013/05/16 00:00 [revised]
PHST- 2013/06/04 00:00 [accepted]
PHST- 2013/07/09 06:00 [entrez]
PHST- 2013/07/09 06:00 [pubmed]
PHST- 2014/03/13 06:00 [medline]
AID - S0920-9964(13)00317-4 [pii]
AID - 10.1016/j.schres.2013.06.017 [doi]
PST - ppublish
SO  - Schizophr Res. 2013 Sep;149(1-3):48-55. doi: 10.1016/j.schres.2013.06.017. Epub 
      2013 Jul 5.