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

Translational Psychiatry
Riya PaulPhilipp G Sämann

Abstract

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...Continue Reading

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Citations

Aug 12, 2020·Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology·Amanda M Buch, Conor Liston
Mar 29, 2021·Current Opinion in Psychology·Marieke A HelmichArnout C Smit
May 6, 2021·Heliyon·Wcleubianne Matias Nascimento MaiaLuciano Da Silva Lopes
May 19, 2021·World Psychiatry : Official Journal of the World Psychiatric Association (WPA)·Adam M ChekroudKarmel Choi

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Methods Mentioned

BETA
genotyping

Software Mentioned

R package Ranger
FlexMix
R
Ranger
MARS
GENDEP

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