Using clinical information to make individualized prognostic predictions in people at ultra high risk for psychosis

TY - JOUR

T1 - Using clinical information to make individualized prognostic predictions in people at ultra high risk for psychosis

AU - Mechelli, Andrea

AU - Lin, Ashleigh

AU - Wood, Stephen

AU - McGorry, Patrick

AU - Amminger, Paul

AU - Tognin, Stefania

AU - McGuire, Philip

AU - Young, Jonathan

AU - Nelson, Barnaby

AU - Yung, Alison

PY - 2017/6/1

Y1 - 2017/6/1

N2 - Recent studies have reported an association between psychopathology and subsequent clinical and functional outcomes in people at ultra-high risk (UHR) for psychosis. This has led to the suggestion that psychopathological information could be used to make prognostic predictions in this population. However, because the current literature is based on inferences at group level, the translational value of the findings for everyday clinical practice is unclear. Here we examined whether psychopathological information could be used to make individualized predictions about clinical and functional outcomes in people at UHR. Participants included 416 people at UHR followed prospectively at the Personal Assessment and Crisis Evaluation (PACE) Clinic in Melbourne, Australia. The data were analysed using Support Vector Machine (SVM), a supervised machine learning technique that allows inferences at the individual level. SVM predicted transition to psychosis with a specificity of 60.6%, a sensitivity of 68.6% and an accuracy of 64.6% (p .

AB - Recent studies have reported an association between psychopathology and subsequent clinical and functional outcomes in people at ultra-high risk (UHR) for psychosis. This has led to the suggestion that psychopathological information could be used to make prognostic predictions in this population. However, because the current literature is based on inferences at group level, the translational value of the findings for everyday clinical practice is unclear. Here we examined whether psychopathological information could be used to make individualized predictions about clinical and functional outcomes in people at UHR. Participants included 416 people at UHR followed prospectively at the Personal Assessment and Crisis Evaluation (PACE) Clinic in Melbourne, Australia. The data were analysed using Support Vector Machine (SVM), a supervised machine learning technique that allows inferences at the individual level. SVM predicted transition to psychosis with a specificity of 60.6%, a sensitivity of 68.6% and an accuracy of 64.6% (p .

KW - Clinical outcome

KW - Functional outcome

KW - Psychosis

KW - Support vector machine

KW - Ultra-high risk

UR - http://www.scopus.com/inward/record.url?scp=85007420633&partnerID=8YFLogxK

U2 - 10.1016/j.schres.2016.11.047

DO - 10.1016/j.schres.2016.11.047

M3 - Article

AN - SCOPUS:85007420633

SN - 0920-9964

VL - 184

SP - 32

EP - 38

JO - Schizophrenia Research

JF - Schizophrenia Research

IS - 0

ER -