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A practical approach to the early identification of antidepressant medication non-responders

Published online by Cambridge University Press:  25 July 2011

J. Li ,
A. Y. C. Kuk  and
A. J. Rush
J. Li*
Affiliation:
Department of Statistics and Applied Probability, National University of Singapore, Singapore Duke-National University of Singapore, Graduate Medical School, Singapore
A. Y. C. Kuk
Affiliation:
Department of Statistics and Applied Probability, National University of Singapore, Singapore
A. J. Rush
Affiliation:
Duke-National University of Singapore, Graduate Medical School, Singapore
*
*Address for correspondence: J. Li, Ph.D., Department of Statistics and Applied Probability, National University of Singapore, 6 Science Drive 2, Singapore 117546. (Email: [email protected])
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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.

Method

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.

Keywords

Antidepressant medicationcross-validationdiagnostic medicinenegative predictive valuerecursive subsetting
Type
Original Articles
Information
Psychological Medicine , Volume 42 , Issue 2 , February 2012 , pp. 309 - 316
Copyright
Copyright © Cambridge University Press 2011

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