Jone Bjornestad1,2,3, Tore Tjora1, Johannes H Langeveld2,4, Helen J Stain2,5, Inge Joa2,4, Jan Olav Johannessen2,4, Michelle Friedman-Yakoobian6,7, Wenche Ten Velden Hegelstad1,2. 1. Department of Social Studies, Faculty of Social Sciences, University of Stavanger, Stavanger, Norway. 2. TIPS - Network for Clinical Research in Psychosis, Stavanger University Hospital, Stavanger, Norway. 3. Department of Psychiatry, District General Hospital of Førde, Førde, Norway. 4. Faculty of Health, Network for Medical Sciences, University of Stavanger, Stavanger, Norway. 5. School of Arts and Humanities, Edith Cowan University, Joondalup, Western Australia, Australia. 6. Harvard Medical School, Department of Psychiatry, Boston, Massachusetts, USA. 7. Massachusetts Mental Health Center Division of Public Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Abstract
AIM: The 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. METHODS: Study participants, N = 96, mean age 16.55 years, 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 years, 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. RESULTS: Four out of 247 specific SIPS symptom scores were significant: (i) reduced expression of emotion at baseline, (ii) experience of emotions and self at 5 months, (iii) perceptual abnormalities/hallucinations at 3 months and (iv) ideational richness at 6 months. No SIPS symptom scores obtained after 6 months of follow-up predicted psychosis. CONCLUSIONS: Study 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.
AIM: The 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. METHODS: Study participants, N = 96, mean age 16.55 years, 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 years, 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. RESULTS: Four out of 247 specific SIPS symptom scores were significant: (i) reduced expression of emotion at baseline, (ii) experience of emotions and self at 5 months, (iii) perceptual abnormalities/hallucinations at 3 months and (iv) ideational richness at 6 months. No SIPS symptom scores obtained after 6 months of follow-up predicted psychosis. CONCLUSIONS: Study 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.
Authors: Ingvild Aase; Johannes H Langeveld; Inge Joa; Jan Olav Johannessen; Ingvild Dalen; Wenche Ten Velden Hegelstad Journal: Schizophr Res Cogn Date: 2022-06-02