An empirical method to identify patterns in the course of psychotic episodes of people with schizophrenia

• Published in: International journal of methods in psychiatric research Vol. 18; no. 4; pp. 265 - 278
• Main Authors: Burti, Lorenzo, Mazzi, Mariangela, Koeter, Maarten, Schene, Aart, Helm, Hedda, Puschner, Bernd, Bindman, Jonathan, Leese, Morven, Thornicroft, Graham, Tansella, Michele
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.12.2009
• Subjects: Adolescent; Adult; Aged; clinical course; Cluster Analysis; Databases, Factual - statistics & numerical data; Diagnostic and Statistical Manual of Mental Disorders; Disability Evaluation; Disease Progression; factor analysis; Factor Analysis, Statistical; Female; Hospitalization - statistics & numerical data; Humans; Longitudinal Studies; Male; Middle Aged; Psychiatric Status Rating Scales; Psychotic Disorders - diagnosis; Psychotic Disorders - etiology; Quality of Life; Randomized Controlled Trials as Topic; Retrospective Studies; schizophrenia; Schizophrenia - complications; Schizophrenic Psychology; Young Adult
• ISSN: 1049-8931; 1557-0657; 1557-0657
• DOI: 10.1002/mpr.287

Objective: This paper illustrates the process of constructing, selecting and applying simple measures in order to empirically derive patterns of course of psychotic episodes in schizophrenia. Method: Data were collected with a composite instrument constructed for a multi‐centre, follow‐up randomized controlled trial of adherence therapy for people with schizophrenia. The instrument included a retrospective weekly assessment of psychotic/non‐psychotic status, which was used to derive the measures, and the DSM‐IV course specifiers. Results: The measures discriminated well between different course patterns and identified homogeneous clusters of subjects which correlated with the groups derived from the DSM‐IV course specifiers. Conclusions: The new measures provide an empirical basis to identify specific patterns of course and to differentiate patients according to pre‐defined criteria. They can be used in follow‐up studies as measures of outcome, to investigate correlations between variables and to identify potential predictors of outcome. Copyright © 2009 John Wiley & Sons, Ltd.