TITLE

Identification of early changes in specific symptoms that predict longer-term response to atypical antipsychotics in the treatment of patients with schizophrenia

AUTHOR(S)
Ruberg, Stephen J.; Lei Chen; Stauffer, Virginia; Ascher-Svanum, Haya; Kollack-Walker, Sara; Conley, Robert R.; Kane, John; Kinon, Bruce J.
PUB. DATE
January 2011
SOURCE
BMC Psychiatry;2011, Vol. 11 Issue 1, p23
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Background: To identify a simple decision tree using early symptom change to predict response to atypical antipsychotic therapy in patients with (Diagnostic and Statistical Manual, Fourth Edition, Text Revised) chronic schizophrenia. Methods: Data were pooled from moderately to severely ill patients (n = 1494) from 6 randomized, double-blind trials (N = 2543). Response was defined as a ≥30% reduction in Positive and Negative Syndrome Scale (PANSS) Total score by Week 8 of treatment. Analyzed predictors were change in individual PANSS items at Weeks 1 and 2. A decision tree was constructed using classification and regression tree (CART) analysis to identify predictors that most effectively differentiated responders from non-responders. Results: A 2-branch, 6-item decision tree was created, producing 3 distinct groups. First branch criterion was a 2- point score decrease in at least 2 of 5 PANSS positive items (Week 2). Second branch criterion was a 2-point score decrease in the PANSS excitement item (Week 2). "Likely responders" met the first branch criteria; "likely nonresponders" did not meet first or second criterion; "not predictable" patients did not meet the first but did meet the second criterion. Using this approach, response to treatment could be predicted in most patients (92%) with high positive predictive value (79%) and high negative predictive value (75%). Predictive findings were confirmed through analysis of data from 2 independent trials. Conclusions: Using a data-driven approach, we identified decision rules using early change in the scores of selected PANSS items to accurately predict longer-term treatment response or non-response to atypical antipsychotic therapy. This could lead to development of a simple quantitative evaluation tool to help guide early treatment decisions. Trial Registration: This is a retrospective, non-intervention study in which pooled results from 6 previously published reports were analyzed; thus, clinical trial registration is not required.
ACCESSION #
59162086

 

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