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

Andrea Mechelli*, Ashleigh Lin, Stephen Wood, Patrick McGorry, Paul Amminger, Stefania Tognin, Philip McGuire, Jonathan Young, Barnaby Nelson, Alison Yung

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

55 Citations (Scopus)
339 Downloads (Pure)

Abstract

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 .

Original languageEnglish
Pages (from-to)32-38
JournalSchizophrenia Research
Volume184
Issue number0
Early online date4 Dec 2016
DOIs
Publication statusPublished - 1 Jun 2017

Keywords

  • Clinical outcome
  • Functional outcome
  • Psychosis
  • Support vector machine
  • Ultra-high risk

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