title: A practical approach to the early identification of antidepressant medication non-responders.
abstract: BACKGROUND
  The aim of the present study was to determine whether a combination of baseline features and early post-baseline depressive symptom changes have clinical value in predicting out-patient non-response in depressed out-patients after 8 weeks of medication treatment.
  
  
  METHODS
  We analysed data from the Combining Medications to Enhance Depression Outcomes study for 447 participants with complete 16-item Quick Inventory of Depressive Symptomatology - Self-Report (QIDS-SR16) ratings at baseline and at treatment weeks 2, 4 and 8. We used a multi-time point, recursive subsetting approach that included baseline features and changes in QIDS-SR16 scores from baseline to weeks 2 and 4, to identify non-responders (<50% reduction in QIDS-SR16) at week 8 with a pre-specified accuracy level.
  
  
  RESULTS
  Pretreatment clinical features alone were not clinically useful predictors of non-response after 8 weeks of treatment. Baseline to week 2 symptom change identified 48 non-responders (of which 36 were true non-responders). This approach gave a clinically meaningful negative predictive value of 0.75. Symptom change from baseline to week 4 identified 79 non-responders (of which 60 were true non-responders), achieving the same accuracy. Symptom change at both weeks 2 and 4 identified 87 participants (almost 20% of the sample) as non-responders with the same accuracy. More participants with chronic than non-chronic index episodes could be accurately identified by week 4.
  
  
  CONCLUSIONS
  Specific baseline clinical features combined with symptom changes by weeks 2-4 can provide clinically actionable results, enhancing the efficiency of care by personalizing the treatment of depression.
authors.affiliation: Department of Statistics and Applied Probability, National University of Singapore, Singapore.
authors.forename: J
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Statistics and Applied Probability, National University of Singapore, Singapore.
authors.forename: A Y C
authors.initials: AY
authors.lastname: Kuk
authors.affiliation: Duke-National University of Singapore, Graduate Medical School, Singapore.
authors.forename: A J
authors.initials: AJ
authors.lastname: Rush
mesh_terms: D000328:Adult; D000465:Algorithms; D000928:Antidepressive Agents / Q000494:pharmacology*; D003865:Major Depressive Disorder / Q000188:drug therapy*; D006801:Humans; D016032:Randomized Controlled Trials as Topic; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D000928:Antidepressive Agents