title: Identification of early changes in specific symptoms that predict longer-term response to atypical antipsychotics in the treatment of patients with schizophrenia.
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 non-responders" 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.
  
  
  BACKGROUND
  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.
authors.affiliation: Eli Lilly and Company, Indianapolis, IN, USA. RUBERG_STEPHEN_J@LILLY.COM
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Ruberg
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.forename: Virginia
authors.initials: V
authors.lastname: Stauffer
authors.forename: Haya
authors.initials: H
authors.lastname: Ascher-Svanum
authors.forename: Sara
authors.initials: S
authors.lastname: Kollack-Walker
authors.forename: Robert R
authors.initials: RR
authors.lastname: Conley
authors.forename: John
authors.initials: J
authors.lastname: Kane
authors.forename: Bruce J
authors.initials: BJ
authors.lastname: Kinon
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D014150:Antipsychotic Agents / Q000627:therapeutic use*; D003663:Decision Trees; D039721:Diagnostic and Statistical Manual of Mental Disorders; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011336:Probability; D011569:Psychiatric Status Rating Scales / Q000706:statistics & numerical data; D011594:Psychometrics; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012559:Schizophrenia / Q000175:diagnosis* / Q000188:drug therapy*; D012565:Schizophrenic Psychology; D016896:Treatment Outcome
chemical_list: D014150:Antipsychotic Agents