Converting positive and negative symptom scores between PANSS and SAPS/SANS

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• 作者：Theo G.M. van Erp ; Adrian Preda ; Dana Nguyen ; Lawrence Faziola ; Jessica Turner ; Juan Bustillo ; Aysenil Belger ; Kelvin O. Lim ; Sarah McEwen ; James Voyvodic ; Daniel H. Mathalon ; Judith Ford ; Steven G. Potkin ; FBIRN
• 关键词：Schizophrenia ; Symptoms ; Marder ; Conversion ; Meta ; Multi-center
• 刊名：Schizophrenia Research
• 出版年：January, 2014
• 年：2014
• 卷：152
• 期：1
• 页码：289-294
• 全文大小：333 K

文摘

The Scale for the Assessment of Positive Symptoms (SAPS), the Scale for the Assessment of Negative Symptoms (SANS), and the Positive and Negative Syndrome Scale for Schizophrenia (PANSS) are the most widely used schizophrenia symptom rating scales, but despite their co-existence for 25 years no easily usable between-scale conversion mechanism exists. The aim of this study was to provide equations for between-scale symptom rating conversions. Two-hundred-and-five schizophrenia patients [mean age 卤 SD = 39.5 卤 11.6, 156 males] were assessed with the SANS, SAPS, and PANSS. Pearson's correlations between symptom scores from each of the scales were computed. Linear regression analyses, on data from 176 randomly selected patients, were performed to derive equations for converting ratings between the scales. Intraclass correlations, on data from the remaining 29 patients, not part of the regression analyses, were performed to determine rating conversion accuracy. Between-scale positive and negative symptom ratings were highly correlated. Intraclass correlations between the original positive and negative symptom ratings and those obtained via conversion of alternative ratings using the conversion equations were moderate to high (ICCs = 0.65 to 0.91). Regression-based equations may be useful for conversion between schizophrenia symptom severity as measured by the SANS/SAPS and PANSS, though additional validation is warranted. This study's conversion equations, implemented at , may aid in the comparison of medication efficacy studies, in meta- and mega-analyses examining symptoms as moderator variables, and in retrospective combination of symptom data in multi-center data sharing projects that need to pool symptom rating data when such data are obtained using different scales.