Development of a prognostic model to PREDICT Relapse of depression in primary care (the PREDICTR study)

Conference:

2020 SAPC North

Talk Code:

A1.2

Presenter:

Andrew S Moriarty

Twitter:

@@A_S_Moriarty

Co-authors:

Lewis W Paton1 (lewis.paton@york.ac.uk); Nick Meader2 (nick.meader@york.ac.uk); Kym IE Snell3 (k.snell@keele.ac.uk); Richard D Riley3 (r.riley@keele.ac.uk); Simon Gilbody4 (simon.gilbody@york.ac.uk); Dean McMillan4 (dean.mcmillan@york.ac.uk); Carolyn A Chew-Graham3 (c.a.chew-graham@york.ac.uk)

Author institutions:

Moriarty - Department of Health Sciences and Hull York Medical School, University of York 1. Department of Health Sciences, University of York 2. Centre for Reviews and Dissemination, University of York 3. School of Primary, Community and Social Care, Keele University 4. Department of Health Sciences and Hull York Medical School, University of York

The problem

Most people with depression are managed in primary care. After a first episode, approximately half of patients will experience a relapse, and this risk increases for each subsequent episode. There is uncertainty about what factors are associated with an increased risk of relapse (some established factors include adverse childhood events, previous episodes of depression and residual symptoms). Combining several prognostic factors within a multivariable prognostic model can result in improved individualised risk predictions. The goal of this programme of work is to develop a primary care-based prognostic model to identify patients at increased risk of relapse.

The Approach

We have carried out a systematic review and critical appraisal of existing prognostic models. Using a dataset drawn from seven primary care-based RCTs and one longitudinal cohort study, we will now use logistic regression to develop a statistical model to predict risk of relapse within 6-8 months after reaching remission. Focus groups and interviews with patients and primary care professionals, along with a PPI group, will provide additional information about the acceptability and feasibility of the model.

The Learning

We identified nine existing prognostic models designed to predict relapse of depression. All were either developed in studies assessed to be at high risk of bias or had poor predictive performance. There is a need for improved risk predictions for depressive relapse in primary care.

Why it matters

The longer-term goal of this study is to develop a clinical tool, to be implemented in general practice, to support clinicians to identify patients who are at increased risk of relapse. This could allow for more targeted allocation of relapse prevention interventions to the individuals who need them most. Ultimately, we aim to improve clinical outcomes and quality of life for patients, as well as facilitating more targeted use of NHS resources in primary care.

This report is independent research supported by the National Institute for Health Research (NIHR Doctoral Research Fellowship, Dr Andrew Moriarty, DRF-2018-11-ST2-044). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care.

Presenting author: Andrew S Moriarty; Department of Health Sciences and Hull York Medical School, University of York; Andrew.moriarty@york.ac.uk; @A_S_Moriarty

Submitted by:

Andrew S Moriarty