Depression includes a complex interaction of psychological patterns, biological vulnerabilities and social stressors, making its causes and symptoms very variable. Equally complicated is the treatment of depression, which requires an extremely personalized approach that may include a combination of drugs, psychotherapy and lifestyle changes.
In a decade multilevel study, the U of a psychologist has worked with Radboud University in the Netherlands to develop a precision approach to depression that gives patients personalized recommendations based on multiple characteristics, such as age and gender. Their findings are published in the magazine PLOS ONE.
The first -line treatment for depression should not be an approach to a size, said Zachary Cohen, a senior writer on paper and assistant professor in U of the Department of Psychology. Unfortunately, he said, today’s care standard involves a great deal of testing and error approach, which tests various medicines or treatments until an intervention or combination is found that effectively relieves symptoms.
About 50% of people do not respond to front -line treatments for depression. There is a lot of heterogeneity of the treatment response, which means that there are some people who respond very well and some people who do not. ”
Zachary Cohen, a senior writer in the newspaper and assistant professor at U of a Department of Psychology
The study specifically focused on adult depression. The research team has gathered patient data from randomized clinical trials performed worldwide, which have evaluated the effectiveness of five widely used depression treatments.
Prior to treatment, patients were evaluated in a variety of dimensions, including related psychiatric conditions, such as stress and personality disorders, said Ellen Driessen, lead researcher of the study and assistant professor of clinical psychology at Radboud University.
“We have examined whether people with certain characteristics, such as the presence of a coexistent situation, could benefit from one method of treatment over the other,” Dryessen said.
Researchers hope that their results will lead to a creation tool of clinical decision support, an algorithm that consider at the same time many variables, such as age, gender and co -existing conditions and relationships between variables to create a single composition. Once the patient’s variables are powered to the tool, it will create a personalized composition as opposed to a guideline that provides a list of generalized recommendations.
The data created by the group examined the results of patients from clinical trials of antidepressants, cognitive therapy, behavioral therapy, interpersonal therapy and short -term psychodynamic therapy, form of thorough treatment.
“Much of the previous work on the choice of treatment was based on data from individual tests whose sample sizes limit their ability to develop strong, reliable clinical prediction models,” Cohen said.
The research team spent about 10 years collecting and processing data from more than 60 tests involving almost 10,000 patients. Researchers from various parts of the world participated in the initiative by exchange of data from their studies. The research team also gathered an international team of scientists from different disciplines to develop the data analysis strategy.
“It took about five years only to clean up and combine existing data so that we can create a model that is updated by all available information,” Cohen said.
“This document is a protocol that details our plans in detail, but the real building of the tool is something we will work next year or two,” Driessen said.
In the future, the team plans to carry out a clinical trial that evaluates the benefits of using a clinical decision support tool to help match patients with their optimal treatment. If the results are favorable, the tool could escalate and be applied to real world clinical contexts. Researchers envision the tool to be a simple computer program or web application in which patient information can be inserted.
The team hopes to provide clinical doctors, people with depression and society a means of more effective use of existing treatment resources and help reduce the enormous personal and social costs associated with depression.
“If the results are generalized, this tool has the ability to be worldwide,” Cohen said. “What is exciting for the variables entering it is that they are relatively simple to obtain self -reporting questionnaires or clinical demographic characteristics. The cost of applying it will also be relatively low.”
Source:
Magazine report:
DRIESSEN, E., et al. (2025) Developing a multi -variable prediction model to support a personalized selection between five major empirical supported therapies for adult depression. Study Protocol for a systematic review and post-analysis of a data network data network. PLOS ONE. doi.org/10.1371/journal.pone.0322124.
