A1 Refereed original research article in a scientific journal

The Strauss and Carpenter Prognostic Scale in subjects clinically at high risk of psychosis




AuthorsD H Nieman, E Velthorst, H E Becker, L de Haan, P M Dingemans, D H Linszen, M Birchwood, P Patterson, R K R Salokangas, M Heinimaa, A Heinz, G Juckel, H G von Reventlow, A Morrison, F Schultze-Lutter, J Klosterkötter, S Ruhrmann

PublisherWiley-Blackwell Publishing, Inc.

Publication year2013

JournalActa Psychiatrica Scandinavica

Journal acronymActa Psychiatr Scand

Number in series1

Volume127

Issue1

First page 53

Last page61

Number of pages9

ISSN0001-690X

DOIhttps://doi.org/10.1111/j.1600-0447.2012.01899.x


Abstract
Objective: To investigate the predictive value of the Strauss and Carpenter Prognostic Scale (SCPS) for transition to a first psychotic episode in subjects clinically at high risk (CHR) of psychosis. Method: Two hundred and forty-four CHR subjects participating in the European Prediction of Psychosis Study were assessed with the SCPS, an instrument that has been shown to predict outcome in patients with schizophrenia reliably. Results: At 18-month follow-up, 37 participants had made the transition to psychosis. The SCPS total score was predictive of a first psychotic episode (P < 0.0001). SCPS items that remained as independent predictors in the Cox proportional hazard model were as follows: most usual quality of useful work in the past year (P = 0.006), quality of social relations (P = 0.006), presence of thought disorder, delusions or hallucinations in the past year (P = 0.001) and reported severity of subjective distress in past month (P = 0.003). Conclusion: The SCPS could make a valuable contribution to a more accurate prediction of psychosis in CHR subjects as a second-step tool. SCPS items assessing quality of useful work and social relations, positive symptoms and subjective distress have predictive value for transition. Further research should focus on investigating whether targeted early interventions directed at the predictive domains may improve outcomes.



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