Researchers call for reliable methods to identify risk of relapse into depression
At least half of patients recovering from depression will experience a relapse, but there are currently no evidence-based tools to help GPs or other healthcare professionals to identify those at high risk.
A team of researchers are therefore calling for more reliable methods for predicting which patients are at high risk of relapsing into depression following a period of recovery.
The team, including researchers from Keele University’s School of Medicine, the University of York, and the Hull York Medical School carried out a Cochrane review of all available evidence which aimed to develop prediction tools. The work was funded by a National Institute of Health Research (NIHR) Doctoral Research Fellowship, held by lead author Dr Andrew Moriarty from the University of York.
Relapse contributes to the overall burden of depression, now the most common cause of disability worldwide, and there is evidence that suggests that once a patient has experienced a relapse, they are at increased risk of subsequent relapses.
During the study, published in the Cochrane Database for Systematic Reviews, the team looked at existing prediction tools across 11 different academic studies and found that there were ten predictive models, but unfortunately none that could be introduced into clinical practice to improve outcomes for patients at present. Although there were promising data for some of the tools, weaknesses in how the studies were carried out meant it was not possible to draw firm conclusions yet.
Researchers say the use of reliable prediction tools may play an important role in determining and targeting the types of interventions needed for different patients.
The research team are now looking at ways of better identifying and helping those at increased risk of relapse.
Professor Richard Riley, Professor of Biostatistics from Keele’s School of Medicine, said: “Risk prediction tools have huge potential to inform the clinical management and care of people with depression, but they must be reliable. Sadly, methodology standards fall short in existing risk prediction studies, which gives concern that they are not fit-for-purpose. New studies must address this, for example by using large high-quality datasets alongside appropriate statistical methodology, robust validation, and transparent reporting.”
Lead researcher Dr Andrew Moriarty, from the University of York’s Mental Health and Addictions Research Group, said: “Around one in two people who recover from depression will become unwell again within the next few years. This is a very unsatisfactory situation for patients and for GPs like me. We need to urgently address this, work to reduce the risk of relapse and improve overall outcomes for patients.
“For this to happen we need models that have a strong evidence base of success and have been tested in clinical practice. We found that these unfortunately do not yet exist in this area.”
Professor Carolyn Chew-Graham, one of Andrew’s PhD supervisors, said: “This work is so important for people with depression who are mostly managed by general practitioners. Andrew is moving onto the next stage of his study which will involve interviews with people with lived experience of depression and GPs, exploring their perspectives on the use of relapse-prevention interventions.”