One treatment fits all: Effectiveness of a multicomponent cognitive behavioral therapy program in data-driven subtypes of perinatal depression

A Waqas,A Rahman

doi:10.1192/j.eurpsy.2021.398

Abstract

IntroductionIt has been well established that depressive disorders including perinatal depression are very heterogeneous, which partly explain the ineffectiveness of available treatments for many patients. Recent innovations in data science can help elucidate the nature of perinatal depression especially the heterogeneity in its presentation.ObjectivesThe present study aime to elucidate heterogeneous subtypes of PND and assess the effectiveness of a multicomponent cognitive behavioral therapy (CBT) across heterogenous subtypes of PND.MethodsThis study was conducted in 2005 in two rural areas of Rawalpindi, Pakistan. Out of a total of 3,898 women, 903 pregnant women were identifed with PND (using DSM-IV) and randomly assigned to intervention and control group. Baseline assessments included interviewer admininstered Hamilton Depression Scale (HDS) and social risk factors. Follow-up assessments were conducted at 6 months and 12 months post-intervention. Principle component analysis was run to reduce dimensionality of the HDS. Two step cluster analysis was then run to elucidate subtypes of PND using the dimensional scores. Thereafter, effectiveness of CBT was compared across these subtypes of PND using multilevel modelling.ResultsPrinciple component analysis revealed a four component solution for the Hamilton depression rating scale. Using these dimensional scores, cluster analysis (average silhouette= 0.5) revealed a parsimonius four cluster soultion of participants with mild PND symptoms (n=326); predominant sleep problems (n=311) c) predominant atypical symptoms (n=80) and d) comorbid depressive and anxiety symptoms (n=186). CBT yielded moderate effect sizes across all these subtypes of PND (cohen’s d > 0.8).ConclusionsMulticomponent CBT is effective across hetergeneous presentations of PND.DisclosureNo significant relationships.

Highlights

Autism spectrum disorder (ASD) is among the most common and pervasive neurodevelopmental disorders
We leverage multiple functional brain imaging cohorts (ABIDE, Stanford; N = 1004) and exciting recent advances in explainable artificial intelligence, to develop a novel multivariate time series deep neural network model that extracts informative brain dynamics features that accurately distinguish between ASD and TD children, and predict clinical symptom severity
Despite the differences in symptom profiles, age, and data acquisition protocols, our model accurately classified data from an independent Stanford cohort without additional training. xAI analyses revealed that brain features associated with the default mode network, and the human voice/face processing and communication systems, most clearly distinguished ASD from TD children in both cohorts

Summary

European Psychiatry

S145 applications (part of the U-PGx consortium, a Horizon2020 funded project on clinical relevant PGx in the EU). Results: Imputed data contains over 11 million SNPs of 77,639 individuals. Conclusions: We expect results in the end of 2020. Disclosure: We thank the iPSYCH consortium, in specific the iPSYCH PI’s Psychiatry And Behavioral Sciences, Stanford University, Stanford, United States of America *Corresponding author.

Introduction

Prevention of mental disorders