Sensitivity of the Positive and Negative Syndrome Scale (PANSS) in Detecting Treatment Effects via Network Analysis

| December 1, 2017 | 0 Comments

by Farnaz Zamani Esfahlani, MS; Hiroki Sayama, DSc; Katherine Frost Visser, MS; and Gregory P. Strauss, PhD

Ms. Zamani Esfahlani is a PhD candidate in the Systems Science program at the State University of New York at Binghamton. Dr. Sayama is the director of the Center for Collective Dynamics of Complex Systems (CoCo), professor in the Department of Systems Science and Industrial Engineering, and director of Graduate Programs in Systems Science at the State University of New York at Binghamton. Ms. Visser is a graduate student at the State University of New York at Binghamton. Dr. Strauss is an assistant professor at the State University of New York at Binghamton. 

Funding: No funding was provided for this study.

Disclosures: The authors have no conflicts of interest relevant to the content of this article.

Abstract: Objective. The Positive and Negative Syndrome Scale is a primary outcome measure in clinical trials examining the efficacy of antipsychotic medications. Although the Positive and Negative Syndrome Scale has demonstrated sensitivity as a measure of treatment change in studies using traditional univariate statistical approaches, its sensitivity to detecting network-level changes in dynamic relationships among symptoms has yet to be demonstrated using more sophisticated multivariate analyses. In the current study, we examined the sensitivity of the Positive and Negative Syndrome Scale to detecting antipsychotic treatment effects as revealed through network analysis.

Design. Participants included 1,049 individuals diagnosed with psychotic disorders from the Phase I portion of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Of these participants, 733 were clinically determined to be treatment-responsive and 316 were found to be treatment-resistant. Item level data from the Positive and Negative Syndrome Scale were submitted to network analysis, and macroscopic, mesoscopic, and microscopic network properties were evaluated for the treatment-responsive and treatment-resistant groups at baseline and post-phase I antipsychotic treatment.

Results. Network analysis indicated that treatment-responsive patients had more densely connected symptom networks after antipsychotic treatment than did treatment-responsive patients at baseline, and that symptom centralities increased following treatment. In contrast, symptom networks of treatment-resistant patients behaved more randomly before and after treatment.

Conclusions. These results suggest that the Positive and Negative Syndrome Scale is sensitive to detecting treatment effects as revealed through network analysis. Its findings also provide compelling new evidence that strongly interconnected symptom networks confer an overall greater probability of treatment responsiveness in patients with psychosis, suggesting that antipsychotics achieve their effect by enhancing a number of central symptoms, which then facilitate reduction of other highly coupled symptoms in a network-like fashion.

Keywords: Schizophrenia, psychosis, antipsychotic, treatment resistance, network analysis, PANSS, Positive and Negative Syndrome Scale

Innov Clin Neurosci. 2017;14(11–12):59–65


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Category: Assessment Tools, Drug Development, Original Research, Past Articles, Patient Assessment, Psychiatry, Psychopharmacology, Scales, Schizophrenia, Trial Methodology

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