[Core factors of schizophrenia structure based on PANSS and SAPS/SANS results. Discerning and head-to-head comparisson of PANSS and SASPS/SANS validity].

AIM

A lot of inconsistencies across dimensional studies of schizophrenia(s) are being unveiled. These problems are strongly related to the methodological aspects of collecting data and specific statistical analyses. Psychiatrists have developed lots of psychopathological models derived from analytic studies based on SAPS/SANS (the Scale for the Assessment of Positive Symptoms/the Scale for the Assessment of Negative Symptoms) and PANSS (The Positive and Negative Syndrome Scale). The unique validation of parallel two independent factor models was performed--ascribed to the same illness and based on different diagnostic scales--to investigate indirect methodological causes of clinical discrepancies.

METHOD

100 newly admitted patients (mean age--33.5, 18-45, males--64, females--36, hospitalised on average 5.15 times) with paranoid schizophrenia (according to ICD-10) were scored and analysed using PANSS and SAPS/SANS during psychotic exacerbation. All patients were treated with neuroleptics of various kinds with 410mg equivalents of chlorpromazine (atypicals:typicals --> 41:59). Factor analyses were applied to basic results (with principal component analysis, normalised varimax rotation). Investing the cross-model validity, canonical analysis was applied.

RESULTS

Models of schizophrenia varied from 3 to 5 factors. PANSS model included: positive, negative, disorganisation, cognitive and depressive components and SAPS/SANS model was dominated by positive, negative and disorganisation factors. The SAPS/SANS accounted for merely 48% of the PANSS common variances. The SAPS/SANS combined measurement preferentially (67% of canonical variance) targeted positive-negative dichotomy. Respectively, PANSS shared positive-negative phenomenology in 35% of its own variance. The general concept of five-dimensionality in paranoid schizophrenia looks clinically more heuristic and statistically more stabilised.