Early intervention in psychiatry | 2021
Exploring specific predictors of psychosis onset over a 2-year period: A decision-tree model.
Abstract
AIM\nThe fluctuating symptoms of clinical high risk for psychosis hamper conversion prediction models. Exploring specific symptoms using machine-learning has proven fruitful in accommodating this challenge. The aim of this study is to explore specific predictors and generate atheoretical hypotheses of onset using a close-monitoring, machine-learning approach.\n\n\nMETHODS\nStudy participants, N\xa0=\xa096, mean age 16.55\u2009years, male to female ratio 46:54%, were recruited from the Prevention of Psychosis Study in Rogaland, Norway. Participants were assessed using the Structured Interview for Psychosis Risk Syndromes (SIPS) at 13 separate assessment time points across 2\u2009years, yielding 247 specific scores. A machine-learning decision-tree analysis (i) examined potential SIPS predictors of psychosis conversion and (ii) hierarchically ranked predictors of psychosis conversion.\n\n\nRESULTS\nFour out of 247 specific SIPS symptom scores were significant: (i) reduced expression of emotion at baseline, (ii) experience of emotions and self at 5\u2009months, (iii) perceptual abnormalities/hallucinations at 3\u2009months and (iv) ideational richness at 6\u2009months. No SIPS symptom scores obtained after 6\u2009months of follow-up predicted psychosis.\n\n\nCONCLUSIONS\nStudy findings suggest that early negative symptoms, particularly those observable by peers and arguably a risk factor for social exclusion, were predictive of psychosis. Self-expression and social behaviour might prove relevant entry points for early intervention in psychosis and psychosis risk. Testing study results in larger samples and at other sites is warranted.