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Prediction of clinical outcomes beyond psychosis in the ultra‐high risk for psychosis population
Author(s) -
Polari Andrea,
Yuen Hok Pan,
Amminger Paul,
Berger Gregor,
Chen Eric,
deHaan Lieuwe,
Hartmann Jessica,
Markulev Connie,
McGorry Patrick,
Nieman Dorien,
Nordentoft Merete,
RiecherRössler Anita,
Smesny Stefan,
Stratford John,
Verma Swapna,
Yung Alison,
Lavoie Suzie,
Nelson Barnaby
Publication year - 2021
Publication title -
early intervention in psychiatry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.087
H-Index - 45
eISSN - 1751-7893
pISSN - 1751-7885
DOI - 10.1111/eip.13002
Subject(s) - psychopathology , psychosis , logistic regression , psychological intervention , population , clinical psychology , psychology , psychiatry , prodrome , schizophrenia (object oriented programming) , at risk mental state , receiver operating characteristic , medicine , environmental health
Aim Several prediction models have been introduced to identify young people at greatest risk of transitioning to psychosis. To date, none has examined the possibility of developing a clinical prediction model of outcomes other than transition. The aims of this study were to examine the association between baseline clinical predictors and outcomes including, but not limited to, transition to psychosis in young people at risk for psychosis, and to develop a prediction model for these outcomes. Methods Several evidence‐based variables previously associated with transition to psychosis and some important clinical comorbidities experienced by ultra‐high risk (UHR) individuals were identified in 202 UHR individuals. Secondary analysis of the Neurapro clinical trial were conducted to investigate the associations between these variables and favourable (remission and recovery) or unfavourable (transition to psychosis, no remission, any recurrence and relapse) clinical outcomes. Logistic regression, best subset selection, Akaike Information Criterion and receiver operating characteristic curves were used to seek the best prediction model for clinical outcomes from all combinations of possible predictors. Results When considered individually, only higher general psychopathology levels ( P = .023) was associated with the unfavourable outcomes. Prediction models suggest that general psychopathology and functioning are predictive of unfavourable outcomes. Conclusion The predictive performance of the resulting models was modest and further research is needed. Nonetheless, when designing early intervention centres aiming to support individuals in the early phases of a mental disorder, the proper assessment of general psychopathology and functioning should be considered in order to inform interventions and length of care provided.