A Bayes1,2, G Parker1,2. 1. School of Psychiatry, University of New South Wales, Sydney, NSW, Australia. 2. Black Dog Institute, Randwick, NSW, Australia.
Abstract
OBJECTIVE: We consider how to choose an antidepressant (AD) medication for the treatment of clinical depression. METHOD: A narrative review was undertaken addressing antidepressant 'choice' considering a range of parameters either weighted by patients and clinicians or suggested in the scientific literature. Findings were synthesised and incorporated with clinical experience into a model to assist AD choice. RESULTS: Efficacy studies comparing ADs offer indicative guidance, while precision psychiatry prediction based on genetics, developmental trauma, neuroimaging, behavioural and cognitive biomarkers, currently has limited clinical utility. Our model offers guidance for AD choice by assessing first for the presence of a depressive subtype or symptom cluster and matching choice of AD class accordingly. Failing this, an AD can be chosen based on depression severity. Within-class choice can be determined by reference to personality style, patient preference, medical or psychiatric comorbidities and side-effect profile. CONCLUSION: Clarification of AD choice would occur if medications are trialled in specific depressive subtypes rather than using the generic diagnosis of major depressive disorder (MDD). Such 'top-down' methods could be enhanced by 'bottom-up' studies to classify individuals according to symptom clusters and biomarkers with AD efficacy tested in these categories. Both methods could be utilised for personalised AD choice.
OBJECTIVE: We consider how to choose an antidepressant (AD) medication for the treatment of clinical depression. METHOD: A narrative review was undertaken addressing antidepressant 'choice' considering a range of parameters either weighted by patients and clinicians or suggested in the scientific literature. Findings were synthesised and incorporated with clinical experience into a model to assist AD choice. RESULTS: Efficacy studies comparing ADs offer indicative guidance, while precision psychiatry prediction based on genetics, developmental trauma, neuroimaging, behavioural and cognitive biomarkers, currently has limited clinical utility. Our model offers guidance for AD choice by assessing first for the presence of a depressive subtype or symptom cluster and matching choice of AD class accordingly. Failing this, an AD can be chosen based on depression severity. Within-class choice can be determined by reference to personality style, patient preference, medical or psychiatric comorbidities and side-effect profile. CONCLUSION: Clarification of AD choice would occur if medications are trialled in specific depressive subtypes rather than using the generic diagnosis of major depressive disorder (MDD). Such 'top-down' methods could be enhanced by 'bottom-up' studies to classify individuals according to symptom clusters and biomarkers with AD efficacy tested in these categories. Both methods could be utilised for personalised AD choice.
Authors: Cornelis F Vos; Tom K Birkenhäger; Willem A Nolen; Walter W van den Broek; Marieke J H Coenen; Sophie E Ter Hark; Robbert-Jan Verkes; Joost G E Janzing Journal: Brain Behav Immun Health Date: 2021-08-05