Research to identify patients at increased risk of relapse into depression essential

6 May 2021
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Researchers at Hull York Medical School and the University of York are calling for more reliable methods for predicting which patients are at high risk of relapsing into depression following a period of recovery.  

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 study shows. 

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. 

Researchers say the use of prediction tools to identify those people at increased risk of relapse and subsequently target treatments to prevent relapse in those individuals could improve the situation. Researchers looked at existing prediction tools across 11 different academic studies to understand how effective they could be in clinical practice.  

They found that there were 10 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 mean we cannot draw firm conclusions yet. 

The team, from Hull York Medical School, the University of York, the and Keele University, led a Cochrane Review of all of the 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, who also works as a GP in York. 

Dr Moriarty, who holds a joint appointment with Hull York Medical School and the University of York 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.”

Dr Moriarty said that a reliable prediction model might help GPs to ensure that interventions can be more effectively made available during a recovery period that will reduce patients’ likelihood of relapse in the future.  

“For this to happen we need models that have a strong evidence base of success and have been tested in clinical practice,” he added. “We found that these unfortunately do not yet exist in this area.” 

Although we have effective treatments for depression, many people become unwell again. Identifying and helping those who are most at risk of relapse is important, but as yet we do not know how to do this. The researchers acknowledge that psychological interventions can be time and resource intensive, and pharmaceutical methods need input from trained healthcare professionals to ensure that they are taken correctly and to reduce adverse effects.  

Making patients aware of the potential for relapse and the ways in which to spot the warning signs through self-monitoring is another potential intervention, but the researchers say more work is needed to understand if this could be effective in the long-term. They argue that predictive methods may play an important role in determining the types of interventions needed for different patients. 

Professor Simon Gilbody, Director of the Mental Health and Addictions Research Group at the University of York, said: “This important review has revealed a big gap in our management of patients with depression and needs some urgent attention.  

“We require improved research in this clinical area to give GPs the evidence they need to employ methods that they can use with confidence to support their patients through this condition, as well as throughout their recovery.” 

The research is published in the Cochrane Database for Systematic Reviews. The team behind this review are now looking at ways of better identifying and helping those at increased risk of relapse. 

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