B3550 - Long-term outcomes of antidepressant use a machine learning approach - 01/06/2020

B number: 
Principal applicant name: 
Rebecca Pearson | Digital Health
Holly Fraser , Bittany Davidson , Ryan Mcconville, Dr Alex Kwong
Title of project: 
Long-term outcomes of antidepressant use, a machine learning approach
Proposal summary: 

This project aims to understand the long-term effects of antidepressant use in the adult population.
There is currently a gap in the literature regarding the longitudinal effects of pharmaceutical
interventions for depression, so there is not great clinical understanding of the impact of
antidepressants on health outcomes long-term. For example, some longitudinal studies suggest that
the symptoms of depression improve overall after long term antidepressant use, but this is reported
in tandem with adverse psychological and physical health outcomes like weight gain, sexual
dysfunction, and emotional numbness (Dehar et al. 2016). In the psychiatric literature, there is an
impoverished understanding of depression causality, with multiple competing hypotheses
suggesting genetic, psychological, neurochemical, and neurostructural correlates of the illness. This
suggests that depression as an overarching umbrella term could include multiple phenotypes, that if
captured, could explain different illness trajectories and predict differential health outcomes after
pharmaceutical intervention.

The long-term effects of antidepressant use on a wide range of health outcomes should be explored
to ascertain whether they are an optimal intervention for people who present with mild to
moderate depression and anxiety.

Impact of research: 
To inform use of antidepressants in certain populations
Date proposal received: 
Monday, 1 June, 2020
Date proposal approved: 
Monday, 1 June, 2020
Mental health - Psychology, Psychiatry, Cognition, Pregnancy - e.g. reproductive health, postnatal depression, birth outcomes, etc., Statistical methods, Psychology - personality