A cognitive-emotional biomarker for predicting remission with antidepressant medications: a report from the iSPOT-D trial.

Journal Article (Journal Article;Multicenter Study)

Depression involves impairments in a range of cognitive and emotional capacities. It is unknown whether these functions can inform medication choice when considered as a composite predictive biomarker. We tested whether behavioral tests, grounded in the neurobiology of cognitive and emotional functions, predict outcome with common antidepressants. Medication-free outpatients with nonpsychotic major depressive disorder (N=1008; 665 completers) were assessed before treatment using 13 computerized tests of psychomotor, executive, memory-attention, processing speed, inhibitory, and emotional functions. Matched healthy controls (N=336) provided a normative reference sample for test performance. Depressed participants were then randomized to escitalopram, sertraline, or venlafaxine-extended release, and were assessed using the 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR16) and the 17-item Hamilton Rating Scale for Depression. Given the heterogeneity of depression, analyses were furthermore stratified by pretreatment performance. We then used pattern classification with cross-validation to determine individual patient-level composite predictive biomarkers of antidepressant outcome based on test performance. A subgroup of depressed participants (approximately one-quarter of patients) were found to be impaired across most cognitive tests relative to the healthy norm, from which they could be discriminated with 91% accuracy. These patients with generally impaired cognitive task performance had poorer treatment outcomes. For this impaired subgroup, task performance furthermore predicted remission on the QIDS-SR16 at 72% accuracy specifically following treatment with escitalopram but not the other medications. Therefore, tests of cognitive and emotional functions can form a clinically meaningful composite biomarker that may help drive general treatment outcome prediction for optimal treatment selection in depression, particularly for escitalopram.

Full Text

Duke Authors

Cited Authors

  • Etkin, A; Patenaude, B; Song, YJC; Usherwood, T; Rekshan, W; Schatzberg, AF; Rush, AJ; Williams, LM

Published Date

  • May 2015

Published In

Volume / Issue

  • 40 / 6

Start / End Page

  • 1332 - 1342

PubMed ID

  • 25547711

Pubmed Central ID

  • PMC4397406

Electronic International Standard Serial Number (EISSN)

  • 1740-634X

Digital Object Identifier (DOI)

  • 10.1038/npp.2014.333

Language

  • eng

Conference Location

  • England