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Toward an online cognitive and emotional battery to predict treatment remission in depression

Authors Gordon E, Rush AJ, Palmer D, Braund T, Rekshan W

Received 17 October 2014

Accepted for publication 28 November 2014

Published 26 February 2015 Volume 2015:11 Pages 517—531

DOI https://doi.org/10.2147/NDT.S75975

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 6

Editor who approved publication: Dr Roger Pinder


Evian Gordon,1 A John Rush,2 Donna M Palmer,3,4 Taylor A Braund,3 William Rekshan1

1Brain Resource, San Francisco, CA, USA; 2Duke-NUS, Singapore; 3Brain Resource, Sydney, NSW, Australia; 4Brain Dynamics Center, Sydney Medical School – Westmead and Westmead Millennium Institute, The University of Sydney, Sydney, NSW, Australia

Purpose: To evaluate the performance of a cognitive and emotional test battery in a representative sample of depressed outpatients to inform likelihood of remission over 8 weeks of treatment with each of three common antidepressant medications.
Patients and methods: Outpatients 18–65 years old with nonpsychotic major depressive disorder (17 sites) were randomized to escitalopram, sertraline or venlafaxine-XR (extended release). Participants scored ≥12 on the baseline 16-item Quick Inventory of Depressive Symptomatology – Self-Report and completed 8 weeks of treatment. The baseline test battery measured cognitive and emotional status. Exploratory multivariate logistic regression models predicting remission (16-item Quick Inventory of Depressive Symptomatology – Self-Report score ≤5 at 8 weeks) were developed independently for each medication in subgroups stratified by age, sex, or cognitive and emotional test performance. The model with the highest cross-validated accuracy determined the participant proportion in each arm for whom remission could be predicted with an accuracy ≥10% above chance. The proportion for whom a prediction could be made with very high certainty (positive predictive value and negative predictive value exceeding 80%) was calculated by incrementally increasing test battery thresholds to predict remission/non-remission.
Results: The test battery, individually developed for each medication, improved identification of remitting and non-remitting participants by ≥10% beyond chance for 243 of 467 participants. The overall remission rates were escitalopram: 40.8%, sertraline: 30.3%, and venlafaxine-XR: 31.1%. Within this subset for whom prediction exceeded chance, test battery thresholds established a negative predictive value of ≥80%, which identified 40.9% of participants not remitting on escitalopram, 77.1% of participants not remitting on sertraline, and 38.7% of participants not remitting on venlafaxine-XR (all including 20% false negatives).
Conclusion: The test battery identified about 50% of each medication group as being ≥10% more or less likely to remit than by chance, and identified about 38% of individuals who did not remit with ≥80% certainty. Clinicians might choose to avoid this specific medication in these particular patients.

Keywords: depression, treatment selection, cognitive tests, biomarkers, treatment prediction, antidepressant medication


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