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Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models
The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical ba...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683145/ https://www.ncbi.nlm.nih.gov/pubmed/31383853 http://dx.doi.org/10.1038/s41398-019-0524-4 |
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author | Paul, Riya Andlauer, Till. F. M. Czamara, Darina Hoehn, David Lucae, Susanne Pütz, Benno Lewis, Cathryn M. Uher, Rudolf Müller-Myhsok, Bertram Ising, Marcus Sämann, Philipp G. |
author_facet | Paul, Riya Andlauer, Till. F. M. Czamara, Darina Hoehn, David Lucae, Susanne Pütz, Benno Lewis, Cathryn M. Uher, Rudolf Müller-Myhsok, Bertram Ising, Marcus Sämann, Philipp G. |
author_sort | Paul, Riya |
collection | PubMed |
description | The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response. |
format | Online Article Text |
id | pubmed-6683145 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-66831452019-08-08 Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models Paul, Riya Andlauer, Till. F. M. Czamara, Darina Hoehn, David Lucae, Susanne Pütz, Benno Lewis, Cathryn M. Uher, Rudolf Müller-Myhsok, Bertram Ising, Marcus Sämann, Philipp G. Transl Psychiatry Article The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response. Nature Publishing Group UK 2019-08-05 /pmc/articles/PMC6683145/ /pubmed/31383853 http://dx.doi.org/10.1038/s41398-019-0524-4 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Paul, Riya Andlauer, Till. F. M. Czamara, Darina Hoehn, David Lucae, Susanne Pütz, Benno Lewis, Cathryn M. Uher, Rudolf Müller-Myhsok, Bertram Ising, Marcus Sämann, Philipp G. Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models |
title | Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models |
title_full | Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models |
title_fullStr | Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models |
title_full_unstemmed | Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models |
title_short | Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models |
title_sort | treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683145/ https://www.ncbi.nlm.nih.gov/pubmed/31383853 http://dx.doi.org/10.1038/s41398-019-0524-4 |
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