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  Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models

Paul, R., Andlauer, T. F. M., Czamara, D., Hoehn, D., Lucae, S., Puetz, B., Lewis, C. M., Uher, R., Mueller-Myhsok, B., Ising, M., & Saemann, P. G. (2019). Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models. TRANSLATIONAL PSYCHIATRY, 9:. doi:10.1038/s41398-019-0524-4.

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アイテムのパーマリンク: https://hdl.handle.net/21.11116/0000-0009-D278-3 版のパーマリンク: https://hdl.handle.net/21.11116/0000-0009-D279-2
資料種別: 学術論文

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 作成者:
Paul, Riya1, 著者           
Andlauer, Till F. M.2, 著者           
Czamara, Darina2, 著者           
Hoehn, David3, 著者           
Lucae, Susanne3, 著者           
Puetz, Benno1, 著者           
Lewis, Cathryn M., 著者
Uher, Rudolf, 著者
Mueller-Myhsok, Bertram1, 著者           
Ising, Marcus3, 著者           
Saemann, Philipp G.3, 著者           
所属:
1RG Statistical Genetics, Max Planck Institute of Psychiatry, Max Planck Society, ou_2040288              
2Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society, ou_2035295              
3Max Planck Institute of Psychiatry, Max Planck Society, ou_1607137              

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 要旨: 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.

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 日付: 2019
 出版の状態: オンラインで出版済み
 ページ: -
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 識別子(DOI, ISBNなど): ISI: 000478795300005
DOI: 10.1038/s41398-019-0524-4
 学位: -

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出版物 1

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出版物名: TRANSLATIONAL PSYCHIATRY
種別: 学術雑誌
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出版社, 出版地: -
ページ: - 巻号: 9 通巻号: 187 開始・終了ページ: - 識別子(ISBN, ISSN, DOIなど): ISSN: 2158-3188