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

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 Unive...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Gordon E, Rush AJ, Palmer DM, Braund TA, Rekshan W
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2015
Materias:	Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/576dacb077e248199dc4b9a5ec2875d2
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:576dacb077e248199dc4b9a5ec2875d2
record_format	dspace
spelling	oai:doaj.org-article:576dacb077e248199dc4b9a5ec2875d22021-12-02T02:09:39ZToward an online cognitive and emotional battery to predict treatment remission in depression1178-2021https://doaj.org/article/576dacb077e248199dc4b9a5ec2875d22015-02-01T00:00:00Zhttp://www.dovepress.com/toward-an-online-cognitive-and-emotional-battery-to-predict-treatment--peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021 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 medicationGordon ERush AJPalmer DMBraund TARekshan WDove Medical PressarticleNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol 2015, Iss default, Pp 517-531 (2015)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 Gordon E Rush AJ Palmer DM Braund TA Rekshan W Toward an online cognitive and emotional battery to predict treatment remission in depression
description	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
format	article
author	Gordon E Rush AJ Palmer DM Braund TA Rekshan W
author_facet	Gordon E Rush AJ Palmer DM Braund TA Rekshan W
author_sort	Gordon E
title	Toward an online cognitive and emotional battery to predict treatment remission in depression
title_short	Toward an online cognitive and emotional battery to predict treatment remission in depression
title_full	Toward an online cognitive and emotional battery to predict treatment remission in depression
title_fullStr	Toward an online cognitive and emotional battery to predict treatment remission in depression
title_full_unstemmed	Toward an online cognitive and emotional battery to predict treatment remission in depression
title_sort	toward an online cognitive and emotional battery to predict treatment remission in depression
publisher	Dove Medical Press
publishDate	2015
url	https://doaj.org/article/576dacb077e248199dc4b9a5ec2875d2
work_keys_str_mv	AT gordone towardanonlinecognitiveandemotionalbatterytopredicttreatmentremissionindepression AT rushaj towardanonlinecognitiveandemotionalbatterytopredicttreatmentremissionindepression AT palmerdm towardanonlinecognitiveandemotionalbatterytopredicttreatmentremissionindepression AT braundta towardanonlinecognitiveandemotionalbatterytopredicttreatmentremissionindepression AT rekshanw towardanonlinecognitiveandemotionalbatterytopredicttreatmentremissionindepression
_version_	1718402648696160256

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

Ejemplares similares