Can we PREDICT Relapse of depression in primary care? (Protocol for the PREDICTR Study)

Conference:

SAPC ASM 2021 - virtual

Talk Code:

2D.2

Presenter:

Andrew Moriarty

Twitter:

@A_S_Moriarty

Co-authors:

Lewis Paton, Nick Meader, Kym IE Snell, Richard D Riley, Carolyn A Chew-Graham, Simon Gilbody, Dean McMillan

Author institutions:

Department of Health Sciences and Hull York Medical School, University of York; Centre for Reviews and Dissemination, University of York; Centre for Prognosis Research, Keele University; School of Medicine, Keele University

Problem

The majority of people with depression in the UK are managed in primary care by General Practitioners (GPs). Relapse is common in people who have been treated for depression, and leads to considerable morbidity and decreased quality of life. Research suggests that at least 50% of patients will relapse after a first episode of depression, and that the majority of these will do so within the first 6 months. There is limited guidance and no validated tools available to help GPs identify and offer appropriate support to individuals who are at higher risk of relapse. The goal of this programme of work is to develop a primary care-based prognostic model to enable GPs to identify people with remitted depression who are at increased risk of relapse.

Approach

During the first part of this study, we carried out a systematic review and critical appraisal of existing prognostic models developed to predict relapse or recurrence of depression. We identified nine existing models; these were either developed in studies judged to be at high risk of bias or had poor predictive performance. Subsequently, we have created a dataset, drawn from seven primary care-based Randomised Controlled Trials of primary care-based interventions for depression and one longitudinal cohort study. We will use penalised logistic regression to develop a statistical model to predict risk of relapse within 6-8 months after reaching remission. We will include the following established relapse predictors as variables in the model: residual depressive symptoms; previous depressive episodes; co-morbid anxiety; and severity of the index episode. If sample size and availability of predictor information allows, we will also include the following less well-established predictors in an exploratory analysis: age: relationship status; multi-morbidity; employment status; gender; and ethnicity. The predictive performance and clinical utility of the model will be assessed.

Findings

This study is on-going and we plan to begin data analysis shortly.

Consequences

The longer-term goal of this study is to develop a clinical tool to support clinicians to identify patients who are at increased risk of relapse, so that the appropriate interventions and support can be targeted at this group. The aim is to improve clinical outcomes and quality of life for patients, and to allow more efficient use of NHS resources. Further work on validation and implementation will take place beyond this study and will be guided by qualitative work with patients and primary care clinicians. The study has been supported from inception by on-going public and patient involvement (PPI). The PPI group, made up of people with lived experience of depression, will continue to guide the development of this tool and further research.

Submitted by:

Andrew Moriarty

Funding acknowledgement:

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 authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care.