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
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
This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.Download Article [PDF] View Full Text [HTML][Machine readable]