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A new paradigm for the prediction of antidepressant treatment response
Current treatment of Major Depressive Disorder utilizes a trial-and-error sequential treatment strategy that results in delays in achieving response and remission for a majority of patients. Protracted ineffective treatment prolongs patient suffering and increases health care costs. In addition, lon...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Les Laboratoires Servier
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3181929/ https://www.ncbi.nlm.nih.gov/pubmed/20135901 |
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author | Leuchter, Andrew F. Cook, Ian A. Hunter, Aimee M. Korb, Alexander S. |
author_facet | Leuchter, Andrew F. Cook, Ian A. Hunter, Aimee M. Korb, Alexander S. |
author_sort | Leuchter, Andrew F. |
collection | PubMed |
description | Current treatment of Major Depressive Disorder utilizes a trial-and-error sequential treatment strategy that results in delays in achieving response and remission for a majority of patients. Protracted ineffective treatment prolongs patient suffering and increases health care costs. In addition, long and unsuccessful antidepressant trials may diminish patient expectations, reinforce negative cognitions, and condition patients not to respond during subsequent antidepressant trials, thus contributing to further treatment resistance. For these reasons, it is critical to identify reliable predictors of antidepressant treatment response that can be used to shorten or eliminate lengthy and ineffective trials. Research on possible endophenotypic as well as genomic predictors has not yet yielded reliable predictors. The most reliable predictors identified thus far are symptomatic and physiologic characteristics of patients that emerge early in the course of treatment. We propose here the term “response endophenotypes” (REs) to describe this class of predictors, defined as latent measurable symptomatic or neurobiologie responses of individual patients that emerge early in the course of treatment, and which carry strong predictive power for individual patient outcomes. Use of REs constitutes a new paradigm in which medication treatment trials that are likely to be ineffective could be stopped within 1 to 2 weeks and other medication more likely to be effective could be started. Data presented here suggest that early changes in symptoms, quantitative electroencephalography, and gene expression could be used to construct effective REs. We posit that this new paradigm could lead to earlier recovery from depressive illness and ultimately produce profound health and economic benefits. |
format | Online Article Text |
id | pubmed-3181929 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Les Laboratoires Servier |
record_format | MEDLINE/PubMed |
spelling | pubmed-31819292011-10-27 A new paradigm for the prediction of antidepressant treatment response Leuchter, Andrew F. Cook, Ian A. Hunter, Aimee M. Korb, Alexander S. Dialogues Clin Neurosci Clinical Research Current treatment of Major Depressive Disorder utilizes a trial-and-error sequential treatment strategy that results in delays in achieving response and remission for a majority of patients. Protracted ineffective treatment prolongs patient suffering and increases health care costs. In addition, long and unsuccessful antidepressant trials may diminish patient expectations, reinforce negative cognitions, and condition patients not to respond during subsequent antidepressant trials, thus contributing to further treatment resistance. For these reasons, it is critical to identify reliable predictors of antidepressant treatment response that can be used to shorten or eliminate lengthy and ineffective trials. Research on possible endophenotypic as well as genomic predictors has not yet yielded reliable predictors. The most reliable predictors identified thus far are symptomatic and physiologic characteristics of patients that emerge early in the course of treatment. We propose here the term “response endophenotypes” (REs) to describe this class of predictors, defined as latent measurable symptomatic or neurobiologie responses of individual patients that emerge early in the course of treatment, and which carry strong predictive power for individual patient outcomes. Use of REs constitutes a new paradigm in which medication treatment trials that are likely to be ineffective could be stopped within 1 to 2 weeks and other medication more likely to be effective could be started. Data presented here suggest that early changes in symptoms, quantitative electroencephalography, and gene expression could be used to construct effective REs. We posit that this new paradigm could lead to earlier recovery from depressive illness and ultimately produce profound health and economic benefits. Les Laboratoires Servier 2009-12 /pmc/articles/PMC3181929/ /pubmed/20135901 Text en Copyright: © 2009 LLS http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc-nd/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Clinical Research Leuchter, Andrew F. Cook, Ian A. Hunter, Aimee M. Korb, Alexander S. A new paradigm for the prediction of antidepressant treatment response |
title | A new paradigm for the prediction of antidepressant treatment response |
title_full | A new paradigm for the prediction of antidepressant treatment response |
title_fullStr | A new paradigm for the prediction of antidepressant treatment response |
title_full_unstemmed | A new paradigm for the prediction of antidepressant treatment response |
title_short | A new paradigm for the prediction of antidepressant treatment response |
title_sort | new paradigm for the prediction of antidepressant treatment response |
topic | Clinical Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3181929/ https://www.ncbi.nlm.nih.gov/pubmed/20135901 |
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