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How Effective Is Algorithm-Guided Treatment for Depressed Inpatients? Results from the Randomized Controlled Multicenter German Algorithm Project 3 Trial
BACKGROUND: Treatment algorithms are considered as key to improve outcomes by enhancing the quality of care. This is the first randomized controlled study to evaluate the clinical effect of algorithm-guided treatment in inpatients with major depressive disorder. METHODS: Inpatients, aged 18 to 70 ye...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5581493/ https://www.ncbi.nlm.nih.gov/pubmed/28645191 http://dx.doi.org/10.1093/ijnp/pyx043 |
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author | Adli, Mazda Wiethoff, Katja Baghai, Thomas C Fisher, Robert Seemüller, Florian Laakmann, Gregor Brieger, Peter Cordes, Joachim Malevani, Jaroslav Laux, Gerd Hauth, Iris Möller, Hans-Jürgen Kronmüller, Klaus-Thomas Smolka, Michael N Schlattmann, Peter Berger, Maximilian Ricken, Roland Stamm, Thomas J Heinz, Andreas Bauer, Michael |
author_facet | Adli, Mazda Wiethoff, Katja Baghai, Thomas C Fisher, Robert Seemüller, Florian Laakmann, Gregor Brieger, Peter Cordes, Joachim Malevani, Jaroslav Laux, Gerd Hauth, Iris Möller, Hans-Jürgen Kronmüller, Klaus-Thomas Smolka, Michael N Schlattmann, Peter Berger, Maximilian Ricken, Roland Stamm, Thomas J Heinz, Andreas Bauer, Michael |
author_sort | Adli, Mazda |
collection | PubMed |
description | BACKGROUND: Treatment algorithms are considered as key to improve outcomes by enhancing the quality of care. This is the first randomized controlled study to evaluate the clinical effect of algorithm-guided treatment in inpatients with major depressive disorder. METHODS: Inpatients, aged 18 to 70 years with major depressive disorder from 10 German psychiatric departments were randomized to 5 different treatment arms (from 2000 to 2005), 3 of which were standardized stepwise drug treatment algorithms (ALGO). The fourth arm proposed medications and provided less specific recommendations based on a computerized documentation and expert system (CDES), the fifth arm received treatment as usual (TAU). ALGO included 3 different second-step strategies: lithium augmentation (ALGO LA), antidepressant dose-escalation (ALGO DE), and switch to a different antidepressant (ALGO SW). Time to remission (21-item Hamilton Depression Rating Scale ≤9) was the primary outcome. RESULTS: Time to remission was significantly shorter for ALGO DE (n=91) compared with both TAU (n=84) (HR=1.67; P=.014) and CDES (n=79) (HR=1.59; P=.031) and ALGO SW (n=89) compared with both TAU (HR=1.64; P=.018) and CDES (HR=1.56; P=.038). For both ALGO LA (n=86) and ALGO DE, fewer antidepressant medications were needed to achieve remission than for CDES or TAU (P<.001). Remission rates at discharge differed across groups; ALGO DE had the highest (89.2%) and TAU the lowest rates (66.2%). CONCLUSIONS: A highly structured algorithm-guided treatment is associated with shorter times and fewer medication changes to achieve remission with depressed inpatients than treatment as usual or computerized medication choice guidance. |
format | Online Article Text |
id | pubmed-5581493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-55814932017-09-06 How Effective Is Algorithm-Guided Treatment for Depressed Inpatients? Results from the Randomized Controlled Multicenter German Algorithm Project 3 Trial Adli, Mazda Wiethoff, Katja Baghai, Thomas C Fisher, Robert Seemüller, Florian Laakmann, Gregor Brieger, Peter Cordes, Joachim Malevani, Jaroslav Laux, Gerd Hauth, Iris Möller, Hans-Jürgen Kronmüller, Klaus-Thomas Smolka, Michael N Schlattmann, Peter Berger, Maximilian Ricken, Roland Stamm, Thomas J Heinz, Andreas Bauer, Michael Int J Neuropsychopharmacol Regular Research Articles BACKGROUND: Treatment algorithms are considered as key to improve outcomes by enhancing the quality of care. This is the first randomized controlled study to evaluate the clinical effect of algorithm-guided treatment in inpatients with major depressive disorder. METHODS: Inpatients, aged 18 to 70 years with major depressive disorder from 10 German psychiatric departments were randomized to 5 different treatment arms (from 2000 to 2005), 3 of which were standardized stepwise drug treatment algorithms (ALGO). The fourth arm proposed medications and provided less specific recommendations based on a computerized documentation and expert system (CDES), the fifth arm received treatment as usual (TAU). ALGO included 3 different second-step strategies: lithium augmentation (ALGO LA), antidepressant dose-escalation (ALGO DE), and switch to a different antidepressant (ALGO SW). Time to remission (21-item Hamilton Depression Rating Scale ≤9) was the primary outcome. RESULTS: Time to remission was significantly shorter for ALGO DE (n=91) compared with both TAU (n=84) (HR=1.67; P=.014) and CDES (n=79) (HR=1.59; P=.031) and ALGO SW (n=89) compared with both TAU (HR=1.64; P=.018) and CDES (HR=1.56; P=.038). For both ALGO LA (n=86) and ALGO DE, fewer antidepressant medications were needed to achieve remission than for CDES or TAU (P<.001). Remission rates at discharge differed across groups; ALGO DE had the highest (89.2%) and TAU the lowest rates (66.2%). CONCLUSIONS: A highly structured algorithm-guided treatment is associated with shorter times and fewer medication changes to achieve remission with depressed inpatients than treatment as usual or computerized medication choice guidance. Oxford University Press 2017-06-22 /pmc/articles/PMC5581493/ /pubmed/28645191 http://dx.doi.org/10.1093/ijnp/pyx043 Text en © The Author 2017. Published by Oxford University Press on behalf of CINP. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Regular Research Articles Adli, Mazda Wiethoff, Katja Baghai, Thomas C Fisher, Robert Seemüller, Florian Laakmann, Gregor Brieger, Peter Cordes, Joachim Malevani, Jaroslav Laux, Gerd Hauth, Iris Möller, Hans-Jürgen Kronmüller, Klaus-Thomas Smolka, Michael N Schlattmann, Peter Berger, Maximilian Ricken, Roland Stamm, Thomas J Heinz, Andreas Bauer, Michael How Effective Is Algorithm-Guided Treatment for Depressed Inpatients? Results from the Randomized Controlled Multicenter German Algorithm Project 3 Trial |
title | How Effective Is Algorithm-Guided Treatment for Depressed Inpatients? Results from the Randomized Controlled Multicenter German Algorithm Project 3 Trial |
title_full | How Effective Is Algorithm-Guided Treatment for Depressed Inpatients? Results from the Randomized Controlled Multicenter German Algorithm Project 3 Trial |
title_fullStr | How Effective Is Algorithm-Guided Treatment for Depressed Inpatients? Results from the Randomized Controlled Multicenter German Algorithm Project 3 Trial |
title_full_unstemmed | How Effective Is Algorithm-Guided Treatment for Depressed Inpatients? Results from the Randomized Controlled Multicenter German Algorithm Project 3 Trial |
title_short | How Effective Is Algorithm-Guided Treatment for Depressed Inpatients? Results from the Randomized Controlled Multicenter German Algorithm Project 3 Trial |
title_sort | how effective is algorithm-guided treatment for depressed inpatients? results from the randomized controlled multicenter german algorithm project 3 trial |
topic | Regular Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5581493/ https://www.ncbi.nlm.nih.gov/pubmed/28645191 http://dx.doi.org/10.1093/ijnp/pyx043 |
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